An assessment of cybersecurity awareness level among Northeastern University students in Nigeria

<p>The world economy today has adopted the internet as a medium of transactions, this has made many organizations use the internet for their daily activities. With this, there is an urgent need to have knowledge in cybersecurity and also how to defend critical assets. The objective of this paper is to identify the level of cybersecurity awareness of students in Northeastern Nigeria. A quantitative approach was used for data collection and cyberbully, personal information, internet banking, internet addiction, and Self-protection were the items ask for cybersecurity awareness level identification. Descriptive analysis was performed for initial result findings using SPSS and OriginPro for graphical design. the preliminary result shows of the students have some basic knowledge of cybersecurity in an item like internet banking, while other items like cyberbully, self-protection and, internet addiction result show moderate awareness, the students' participation based on gender, males constitute 77.1% i.e. (N=340) and females constitute 22.9% i.e. (N=101). Future research would concentrate on designing awareness programs that would increase the level of their awareness especially the students in the Northeastern part of Nigeria.</p>

Influential Nodes in the OBOR Fossil Energy Trade Network Based on D-S Theory: Detection and Evolution Analysis

The structure formed by fossil energy trade among countries can be divided into multiple subcommodity networks. However, the difference of coupling mode and transmission mechanism between layers of the multirelationship network will affect the measurement of node importance. In this paper, a framework of multisource information fusion by considering data uncertainty and the classical network centrality measures is build. Then, the evidential centrality (EVC) indicator is proposed, by integrating Dempster–Shafer evidence theory and network theory, to empirically identify influential nodes of fossil energy trade along the Belt and Road Initiative. The initial result of the heterogeneity characteristics of the constructed network drives us to explore the core node issue further. The main detected evidential nodes include Russia, Kazakhstan, Czechia, Slovakia, Egypt, Romania, China, Saudi Arabia, and Singapore, which also have higher impact on network efficiency. In addition, cluster analysis discovered that resource endowment is an essential factor influencing country’s position, followed by geographical distance, economic level, and economic growth potential. Therefore, the above aspects should be considered when ensuring national trade security. At last, the rationality and comprehensiveness of EVC are verified by comparing with some benchmark centralities.

Estimation of Suspended Sediment Concentration in Downstream of the Ba River Basin using Remote Sensing Images

Assessing the tendency of suspended sediment concentration (SSC) in the river watershedsenables a better understanding of the hydromorphological properties of its basins and the associatedprocesses. In addition, analyzing this trend is essential to address several important issues such as erosion,water pollution, human health risks, etc. Therefore, it is critical to determine a proper method to quantifyspatio-temporal variability in SSC. In recent years, remote sensing and GIS technologies are being widelyapplied to support scientists, researchers, and environmental resource investigators to quickly andsynchronously capture information on a large scale. The combination of remote sensing and GIS data willbecome the reliable and timely updated data source for the managers, researchers on many fields. Thereare several tools, software, algorithms being used in extracting information from satellites and support forthe analysis, image interpretation, data collection. The information from satellite images related to waterresources includes vegetational cover, flooding events on a large scale, rain forecast, populationdistribution, forest fire, landslide movements, sedimentation, etc., and especially information on waterquality, sediment concentration. This paper presents the initial result from LANDSAT satellite imageinterpretation to investigate the amount of sediment carried downstream of the Ba river basin.

Universal periodic review as a migration desecuritization instrument

For the last decades, it was observed that the migration subject was addressed as a security issue due to a social construction proposed by the state that sees immigrants as a threat to security, in which they are subconsciously considered as “the other”. Thus, migration issues started to be analyzed under the security bias, which resulted in the topic being securitized instead of politicized and discussed by all sectors of society and under the human rights scope. In 2006 the United Nations Human Rights Council created the Universal Periodic Review (UPR) mechanism, which allows all UN member states to have their human rights situations reviewed every four years and a half. In this respect, the paper aims at presenting how the UPR mechanism may be a tool to desecuritize the migration subject by using Spain as a study case, which is the country that receives more recommendations about migrants among all UN member states. Therefore, the research focuses on a comprehensive evaluation of documents on Spain outcomes in the first two UPR cycles, in order to identify the main recommendations about the migration subject and to understand the interventions related to Spain's position on accepting or not such recommendations. The purpose here is to check the effectiveness of the UPR as a tool that may contribute to the desecuritization of the migration subject under the human rights perspective. The research focuses on a review of documents and bibliographic references, with a qualitative approach and exploratory nature. The initial result points out that the interactive discussion promoted by the UPR mechanism can help support to desecuritize the migrant issue.

Erroneous pixel prediction for semantic image segmentation

We consider semantic image segmentation. Our method is inspired by Bayesian deep learning which improves image segmentation accuracy by modeling the uncertainty of the network output. In contrast to uncertainty, our method directly learns to predict the erroneous pixels of a segmentation network, which is modeled as a binary classification problem. It can speed up training comparing to the Monte Carlo integration often used in Bayesian deep learning. It also allows us to train a branch to correct the labels of erroneous pixels. Our method consists of three stages: (i) predict pixel-wise error probability of the initial result, (ii) redetermine new labels for pixels with high error probability, and (iii) fuse the initial result and the redetermined result with respect to the error probability. We formulate the error-pixel prediction problem as a classification task and employ an error-prediction branch in the network to predict pixel-wise error probabilities. We also introduce a detail branch to focus the training process on the erroneous pixels. We have experimentally validated our method on the Cityscapes and ADE20K datasets. Our model can be easily added to various advanced segmentation networks to improve their performance. Taking DeepLabv3+ as an example, our network can achieve 82.88% of mIoU on Cityscapes testing dataset and 45.73% on ADE20K validation dataset, improving corresponding DeepLabv3+ results by 0.74% and 0.13% respectively.

Hybrid handheld gamma-ultrasound prototype for radioguided surgery: initial results

We have witnessed impressive advances in preoperative imaging of cancer and the development of dualmodality scanners. However, there is a need for a scanner with functional and anatomical imaging capability suitable for surgical settings and radioguided surgery. The current paper introduces a handheld gamma-ultrasound scanner prototype and illustrates the initial result of testing its very first version. The result of the testing was promising and encouraging in continuing the further development of the prototype.

Pemanfaatan Media Filter Kearifan Lokal dalam Meningkatkan Kualitas Air dengan Proses Filtrasi

The need for clean water continues to increase with changing times and the passage of time, however, clean water that is suitable for consumption is not easily available in some areas, considering that the physical conditions of regional geomorphology and hydrology have different forms. Water that is suitable for drinking must be clean and minimal from pollutant loads and substances that can interfere with the health of the body, this is different from the water obtained by people in Rantau Karya Village, Geragai District, Tanjung Jabung Timur Regency, because the hydrological conditions of the area are dominated by peatlands so that the water consumed is included in peat water, where the majority of the people use dug well water, therefore a simple technology is needed in dug well water treatment by utilizing local wisdom filter media. The results showed the efficiency of removal of organic substances (KMnO4) where the initial parameter was 22.5 mg / l to be 11.218 mg / l. The efficiency of turbidity reduction is 56%, where the initial result of the turbidity parameter is 31 NTU and the final result is 15 NTU, and the final pH of well water is 6.26, where the initial test shows the number 5.6. Each thickness of the filter media to get optimum results with a thickness of 15 cm.

Using Probabilistic Movement Primitives in Analyzing Human Motion Differences Under Transcranial Current Stimulation

In medical tasks such as human motion analysis, computer-aided auxiliary systems have become the preferred choice for human experts for their high efficiency. However, conventional approaches are typically based on user-defined features such as movement onset times, peak velocities, motion vectors, or frequency domain analyses. Such approaches entail careful data post-processing or specific domain knowledge to achieve a meaningful feature extraction. Besides, they are prone to noise and the manual-defined features could hardly be re-used for other analyses. In this paper, we proposed probabilistic movement primitives (ProMPs), a widely-used approach in robot skill learning, to model human motions. The benefit of ProMPs is that the features are directly learned from the data and ProMPs can capture important features describing the trajectory shape, which can easily be extended to other tasks. Distinct from previous research, where classification tasks are mostly investigated, we applied ProMPs together with a variant of Kullback-Leibler (KL) divergence to quantify the effect of different transcranial current stimulation methods on human motions. We presented an initial result with 10 participants. The results validate ProMPs as a robust and effective feature extractor for human motions.

A Tracking Platform Solution for Autonomous Vehicles Localization in Future Smart Cities Using Machine and Deep Learning

The localization of autonomous vehicles requires, accurate tracking of its position and orientation in all conditions. As modern cities evolve localization would require a more precise accuracy that up to the level of centimetre and decimetre. One of the most crucial struggles in global positioning system and inertial navigation fusion is that the accuracy of the algorithm is reduced during GPS interruptions. In recent days bigdata, machine and deep learning offer great opportunities, especially for future smart and industrial 4.0 autonomous applications. This research programme is aiming to investigate and deploy machine and deep learning approach to improve and reach the level of reliability, accuracy and robustness required at low-cost GPS/IMU unit. The programme will also present a tracking platform solution that would compensates the issues of lack of accuracy in existing localization methods. The initial result of this ongoing programme is presented and reported in this paper. The paper also covers the research programme future development plans and milestones.

Visual Localization of the Tianwen-1 Lander Using Orbital, Descent and Rover Images

Tianwen-1, China’s first Mars exploration mission, was successfully landed in the southern part of Utopia Planitia on 15 May 2021 (UTC+8). Timely and accurately determining the landing location is critical for the subsequent mission operations. For timely localization, the remote landmarks, selected from the panorama generated by the earliest received Navigation and Terrain Cameras (NaTeCam) images, were matched with the Digital Orthophoto Map (DOM) generated by high resolution imaging camera (HiRIC) images to obtain the initial result based on the triangulation method. Then, the initial localization result was refined by the descent images received later and the NaTeCam DOM. Finally, the lander location was determined to be (25.066°N, 109.925°E). Verified by the new orbital image with the lander and Zhurong rover visible, the localization accuracy was within a pixel of the HiRIC DOM.