Miracle-dz/UniFAR-FTUs huggingface dataset:
Drone to Obstacle Distance Estimation Using YOLO V3 Network and Mathematical Principles

Now a days, drones are very commonly used in various real time applications. Moving towards autonomy, these drones rely on obstacle detection sensors and various collision avoidance algorithms programmed into it. Development of fully autonomous drones provide the fundamental benefits of being able to operate in hazardous environments without a human pilot. Among the various sensors, monocular cameras provide a rich source of information and are one of the main sensing mechanisms in low flying drones. These drones can be used for rescue and search operations, traffic monitoring, infrastructure, and pipeline inspection, and in construction sites. In this paper, we propose an onboard obstacle detection model using deep learning techniques, combined with a mathematical approach to calculate the distance between the detected obstacle and the drone. This when implemented does not need any additional sensor or Global Positioning Systems (GPS) other than the vision sensor.
Detection of fraudulent credit card transactions: A comparative analysis of data sampling and classification techniques

Every year there is an increasing loss of a huge amount of money due to fraudulent credit card transactions. Recently there is a focus on using machine learning algorithms to identify fraud transactions. The number of fraud cases to non-fraud transactions is very low. This creates a skewed or unbalanced data, which poses a challenge to training the machine learning models. The availability of a public dataset for this research problem is scarce. The dataset used for this work is obtained from Kaggle. In this paper, we explore different sampling techniques such as under-sampling, Synthetic Minority Oversampling Technique (SMOTE) and SMOTE-Tomek, to work on the unbalanced data. Classification models, such as k-Nearest Neighbour (KNN), logistic regression, random forest and Support Vector Machine (SVM), are trained on the sampled data to detect fraudulent credit card transactions. The performance of the various machine learning approaches are evaluated for its precision, recall and F1-score. The classification results obtained is promising and can be used for credit card fraud detection.
Network intrusion detection: a comparative study of four classifiers using the NSL-KDD and KDD’99 datasets

As most of the population acquires access to the internet, protecting online identity from threats of confidentiality, integrity, and accessibility becomes an increasingly important problem to tackle. By definition, a network intrusion detection system (IDS) helps pinpoint and identify anomalous network traffic to bring forward and classify suspicious activity. It is a fundamental part of network security and provides the first line of defense against a potential attack by alerting an administrator or appropriate personnel of possible malicious network activity. Several academic publications propose various artificial intelligence (AI) methods for an accurate network intrusion detection system (IDS). This paper outlines and compares four AI methods to train two benchmark datasets- the KDD’99 and the NSL-KDD. Apart from model selection, data preprocessing plays a vital role in contributing to accurate solutions, and thus, we propose a simple yet effective data preprocessing method. We also evaluate and compare the accuracy and performance of four popular models- decision tree (DT), multi-layer perceptron (MLP), random forest (RF), and a stacked autoencoder (SAE) model. Of the four methods, the random forest classifier showed the most consistent and accurate results.
A web-based application for face detection in real-time images and videos

Face detection is widely used in the consumer industry such as advertising, user interfaces, video streaming apps and in many security applications. Every application has its own demands and constraints, and hence cannot be fulfilled by a single face detection algorithm. In this work, we developed an interactive web-based application for face detection in real-time images and videos. Pretrained face detection algorithms, namely Haar cascade classifier, HOG-based frontal face detector, Multi-task Cascaded Convolutional Neural Network (MTCNN) and Deep Neural Network (DNN), were used in the web-based application. A performance analysis of these face detection algorithms is done for various parameters such as different lighting conditions, face occlusion and frame rate. The web app interface can be used for an easy comparison of different face detection algorithms. This will help the user to decide on the algorithm that suits their purpose and requirements for various applications.
Study of distance metrics on k - nearest neighbor algorithm for star categorization

Classification of stars is essential to investigate the characteristics and behavior of stars. Performing classifications manually is error-prone and time-consuming. Machine learning provides a computerized solution to handle huge volumes of data with minimal human input. k-Nearest Neighbor (kNN) is one of the simplest supervised learning approaches in machine learning. This paper aims at studying and analyzing the performance of the kNN algorithm on the star dataset. In this paper, we have analyzed the accuracy of the kNN algorithm by considering various distance metrics and the range of k values. Minkowski, Euclidean, Manhattan, Chebyshev, Cosine, Jaccard, and Hamming distance were applied on kNN classifiers for different k values. It is observed that Cosine distance works better than the other distance metrics on star categorization.
Emerging Military Applications of Free Space Optical Communication Technology: A Detailed Review

Free Space Optical Communication (FSOC) technology, designed to operate through free space as medium is rapidly emerging as reliable, fast & secure alternative for broadband communication. It is being researched & investigated for applications and technical utilities in both civil & military domain systems owing to its immense benefits including high security, better data rates & fast installations, no requirement of licensed spectrum, best costs & simplicity of design as compared to contemporary Radio Frequency (RF) systems. FSOC networks once established are difficult to detect & intercept in view of small divergence of the laser beams. This paper reviews the FSOC technology and presents features based merits as well as unmatched advantages & associated major applications in various fields collating them into a single reference point for future research. Efforts have also been invested to present a review of FSOC’s limitations & innovative emerging mitigation techniques which can prove to be a one stop feeder & a launch pad for future research in FSOC domain. A literature survey has been undertaken of available FSOC related military applications to review & gather relevant inputs to throw light on emerging trends in military applications including recent experiments & researched areas pertaining to laser systems & weapons, Unmanned Aerial Vehicles (UAVs), under sea usages, terrestrial applications, aerial, naval ships/shore based applications & RF/hybrid systems. It has been endeavored to shed light on findings & developments in these classified military domains to generate inputs for future work in this domain. Finally, a future technical roadmap and a way ahead & suggestions have been coined up pointing towards required impetus & focus areas in FSOC research domain.
Power Inadequacy, the Thorn in Economic Growth of Nigeria

Adequate access to reliable, quality and affordable power is crucial to national economic growth. The Nigerian power industry performance track records and statistics are dismal and appalling. The power supply is characterized by low accessibility, epileptic and poor transmission. The socioeconomic activities in Nigeria have been grossly impacted negatively by insufficient power supply. Yet the huge energy potential available in the country are insignificantly tapped. Therefore, this study x-rays the past and present power situation in Nigeria; the determinants of the amount of power demanded; and the several interventions in the Nigerian power supply. The study reported that population, industry, transportation, agriculture, and commercial services are the main determinants of energy demand in Nigeria. The study reveals that population interconnects all other factors. Industrial, transportation and residential sectors consume the most energy in Nigeria. Industrialization, urbanization and the general economic wellbeing of Nigeria depend largely on effective power supply. This study concludes that effective power supply relies on careful and logical handling of these critical power technical activities ‒ power generation; efficient power transmission to users; efficient power distribution to the users; reasonable level of domestic power equipment manufacturing infrastructure; management of population growth; and national power infrastructural development planning and enabling policy for power sector investments.
NovelD: A Simple yet Effective Exploration Criterion

Efﬁcient exploration under sparse rewards remains a key challenge in deep re-inforcement learning. Previous exploration methods (e.g., RND) have achieved strong results in multiple hard tasks. However, if there are multiple novel areas to explore, these methods often focus quickly on one without sufﬁciently trying others (like a depth-wise ﬁrst search manner). In some scenarios (e.g., four corridor environment in Sec. 4.2), we observe they explore in one corridor for long and fail to cover all the states. On the other hand, in theoretical RL, with optimistic initialization and the inverse square root of visitation count as a bonus, it won’t suffer from this and explores different novel regions alternatively (like a breadth-ﬁrst search manner). In this paper, inspired by this, we propose a simple but effective criterion called NovelD by weighting every novel area approximately equally. Our algorithm is very simple but yet shows comparable performance or even outperforms multiple SOTA exploration methods in many hard exploration tasks. Speciﬁcally, NovelD solves all the static procedurally-generated tasks in Mini-Grid with just 120 M environment steps, without any curriculum learning. In comparison, the previous SOTA only solves 50 % of them. NovelD also achieves SOTA on multiple tasks in NetHack, a rogue-like game that contains more challenging procedurally-generated environments. In multiple Atari games (e.g., MonteZuma’s Revenge, Venture, Gravitar), NovelD outperforms RND. We analyze NovelD thoroughly in Mini-Grid and found that empirically it helps the agent explore the environment more uniformly with a focus on exploring beyond the boundary. 1
Comparing Black-Box Optimization Methods for Online DBMS Tuning

Modern database management systems (DBMSs) expose hundreds of configuration knobs that collectively determine their runtime behavior. Those knobs control, for example, the amount of memory used for cache and the maximum number of concurrent reads. If the knobs are optimally configured, the database systems can achieve higher performance and efficiency. The traditional practice for manual DBMS tuning is where the database administrators (DBAs) use their expertise and manually experiment by changing individual knobs to understand their effectiveness on the entire workload. However, manual DBMS tuning is a daunting task even for expert DBAs because of the following reasons: (1) Complexity: Modern DBMSs have hundreds of interdependent configuration knobs, and most knobs take in continuous values. As a result, the configuration space for DBMS tuning is orders of magnitude larger than those of traditional systems (e.g., file systems) [8]. (2) Hardwaredependent: Different hardware might have different optimal configurations. e.g. a configuration that is good for SSD might not work for HDD [1]. (3) Workloaddependent: The optimal configuration for one application might be sub-optimal for another [19]. (4) DBMSdependent: Two DBMSs may use different names for the same knob [19]. (5) Inter-dependent: The knobs are also dependent of each other (configuring one knob could impact the other knobs). Moreover, different DBMSs use different internal structures, so optimal configurations are not generic across different DBMSs. As a result, recent research has focused on the automatic tuning of the configuration knobs.
Associations and Interactions between Heavy Metals with White Blood Cell and Eosinophil Count

The accumulation of heavy metals in the body has been associated with an elevated immune response. The aim of this study was to investigate the associations among heavy metals and white blood cell (WBC) and eosinophil count in the general population in southern Taiwan. We also explored the interactions and synergetic effects of heavy metals on WBC and eosinophil count. We conducted a health survey in the general population living in southern Taiwan between June 2016 and September 2018. Seven heavy metals were measured: blood lead (Pb), and urine cadmium (Cd), copper (Cu), nickel, arsenic (As), chromium and manganese (Mn). A total of 2,447 participants were enrolled. In multivariable analysis, high concentrations of Pb (log per 1 mg/L; coefficient β, 0.332; p = 0.005) and Cu (log per 1 μg/dL; coefficient β, 0.476; p < 0.001) were significantly associated with a high WBC count. In addition, high concentrations of Pb (log per 1 mg/L; coefficient β, 0.732; p < 0.001), As (log per 1 μg/L; coefficient β, 0.133; p = 0.015), Cu (log per 1 μg/dL; coefficient β, 0.181; p = 0.018), and Cd (log per 1 μg/L; coefficient β, 0.139; p = 0.002) were significantly associated with a high eosinophil count. Further, the effect of interactions between Pb and As (coefficient β, 0.721; p = 0.029) and Mn and Cu (coefficient β, 0.482; p = 0.018) on WBC count, and As and Cu (unstandardized coefficient β, 0.558; p = 0.002) on eosinophil count were statistically significant. In conclusion, the heavy metals Pb, As, Cu, and Cd were associated with WBC and eosinophil count. In addition, synergistic effects of heavy metal poisoning on the association with WBC and eosinophil count were also observed.
My Past Dictates my Present: Relevance, Exposure, and Inﬂuence of Longitudinal Data on Facebook

—Online social networks accumulate unprecedented amounts of data that continue to exist on user proﬁles long after the time of posting. Given that these platforms primarily provide a venue for people to connect and foster online friendships, the inﬂuence and the risks associated with longitudinal data may impact users and their reasons for using these platforms. To better understand these issues, we conducted two user studies of Facebook users analyzing the history of past postings w.r. t. to their perceived relevance, longitudinal exposure, and impact on the users’ befriending behavior. The studies give us a cross-cultural undergraduate student sample (n=89, campus study) and a Mechanical Turk sample of two cultural backgrounds from the US and India (n=209, MTurk study). Our ﬁndings reveal that a sizable group of participants consider their past postings irrelevant and, at times, embarrassing. However, participants’ awareness and usage of longitudinal privacy control features (e.g., Limit Past Posts) are limited, resulting in overexposure of their past postings and personal information. Importantly, we ﬁnd support that these overexposed, yet irrelevant, past postings (of both participants and friend requesters) have the potential to inﬂuence users’ fundamental behavior on the platform: friend network expansion. Participants greatly valued friend requester’s past postings, particularly in the absence of prior personal interactions, but are inﬂuenced by their backgrounds (American users rely signiﬁcantly more than their Indian counterparts on the requesters’ past postings for their befriending behavior). We close by discussing the implications of our ﬁndings on the future of longitudinal privacy controls.
Investigation of the ballooning and rupture of Zircaloy-4 with neighboring rod effect using NEST experimental setup

The ballooning and rupture phenomena on the fuel cladding occurs during early stages of loss of coolant accident (LOCA) could induce channel blockage and fuel relocation/dispersal. Most of previous studies focused on the behavior of single rod and applied to code development, but single rod tests could not represent the practical behaviors with the consideration of the mechanical restriction effect of neighboring rods and the changes of thermal gradient. In this regards, Neighboring rods Effect Simulation Tester (NEST) using real-time DIC (Digital Image Correlation), which are experimental setups for the large deformation of fuel cladding considering neighboring rods, has been developed to validate multi-rod analysis model. In this study, the ballooning and rupture behaviors of Zircaloy-4 (Zry-4) with neighboring rod effect were investigated using NEST-1. The thermal/mechanical interaction between specimen and neighboring rods were observed. Also, post-test examinations including SEM/EDS analysis and dimensional changes measurement were performed.
Bank Ownership and Product Market Competition

This paper studies how bank ownership of industrial firms affects their market power. We find that bank ownership increases firms’ markups, while bank ownership of industry rivals reduces firms’ markups. Using bank mergers that generate exogenous shocks to bank ownership of industry rivals, we employ a difference-indifferences analysis to establish causality inference. The mechanism analyses show that the decreased markup effect is stronger for competitive industries and R&D intensive firms. Besides, firms are more likely to switch banks, especially when banks have more private information of them. We also find increased costs of loans for the affected firms.
Foreign direct investment – using network analysis to understand the position of Portugal in a global FDI network

Understanding the role of foreign direct investment (FDI) is of utmost importance in a world economy of increasingly interdependent economies. However, the lack of an unified data source of FDI covering a long time frame has posed serious challenges to its analysis. In this article we apply methods of network analysis to build a representation of the global FDI relationships. We show how the network representation of the global FDI can be used to identify patterns, identify preferential paths for investment, establish trends and describe the relations between countries over time. We present the results by using specific visualisation tools that graphically illustrate the interlinkages between the economies, and that can be a valuable instrument for the design and deployment of regulating instruments.
Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning

In this review, we summarize existing trends of flow control used to improve the aerodynamic efficiency of wings. We first discuss active methods to control turbulence, starting with flat-plate geometries and building towards the more complicated flow around wings. Then, we discuss active approaches to control separation, a crucial aspect towards achieving a high aerodynamic efficiency. Furthermore, we highlight methods relying on turbulence simulation, and discuss various levels of modeling. Finally, we thoroughly revise data-driven methods and their application to flow control, and focus on deep reinforcement learning (DRL). We conclude that this methodology has the potential to discover novel control strategies in complex turbulent flows of aerodynamic relevance.
First Arrival Position in Molecular Communication Via Generator of Diffusion Semigroup

—We consider the problem of characterizing the ﬁrst arrival position (FAP) density in molecular communication (MC) with a diffusion-advection channel that permits a constant drift velocity pointed to arbitrary direction. The advantage of FAP modulation lies in the fact that it could encode more information into higher dimensional spatial variables, compared to other modulation techniques using time or molecule numbers. However, effective methods to characterize the FAP density in a general framework do not exist. In this paper, we devise a methodology that fully resolves the FAP density with planar absorbing receivers in arbitrary dimensions. Our work recovers existing results of FAP in 2D and 3D as special cases. The key insight of our approach is to remove the time dependence of the MC system evolution based on the generator of diffusion semigroups.
A Survey on Applications of Digital Human Avatars toward Virtual Co-presence

This paper investigates different approaches to build and use digital human avatars toward interactive Virtual Co-presence (VCP) environments. We evaluate the evolution of technologies for creating VCP environments and how the advancement in Artificial Intelligence (AI) and Computer Graphics affect the quality of VCP environments. We categorize different methods in the literature based on their applications and methodology and compare various groups and strategies based on their applications, contributions, and limitations. We also have a brief discussion about the approaches that other forms of human representation, rather than digital human avatars, have been utilized in VCP environments. Our goal is to fill the gap in the research domain where there is a lack of literature review investigating different approaches for creating avatar-based VCP environments. We hope this study will be useful for future research involving human representation in VCP or Virtual Reality (VR) environments. To the best of our knowledge, it is the first survey research that investigates avatar-based VCP environments. Specifically, the categorization methodology suggested in this paper for avatar-based methods is new.
Cloud Computing in the Public Sector: Mapping the Knowledge Domain

Cloud computing is a key element in many nations’ pursuit of fast-tracked digital transformation and the quick implementation of digital tools but is still facing considerable barriers due to the distinct challenges that information technology adoption faces in public sector environments. Using scientometric data from the Web of Science database, this study explores the current state of research and the structure of the public sector cloud computing knowledge domain in a novel way, utilizing the CiteSpace visual analytic software to produce knowledge maps that visualize public sector cloud computing research in terms of publication activity, constituent authors, and publication venues, as well as exploring the intellectual base of the knowledge domain. For public sector cloud computing researchers and practitioners, the study provides visual insights and analyses that support future research, collaboration, and evidence-based cloud computing implementation and utilization.