Miracle-dz/UniFAR-FTUs huggingface dataset:
Abnormal Network Traffic Detection based on Leaf Node Density Ratio

As the network evolves, cyber-attacks become more and more diverse. In the process of detecting network traffic, the most complicated but also the most important task is to find unknown abnormal network traffic data in time. In the existing abnormal network traffic detection method based on Extended Isolation Forest, there are limitations such as unbalanced detection accuracy and insufficient generalization ability. An improved abnormal network traffic detection method EIF-LNDR is proposed for the above problems. Based on the leaf node density ratio, the anomaly score of the instance can be calculated differently for each iTree. The experiments show that EIF-LNDR has significant improvement in precision, false negative rate, and detector efficiency compared with Extended Isolation Forest and LOF methods.
Analysis of TCP congestion management algorithms for Wireless Mesh Network

The Wireless Mesh Networks WMNs are considered among the most innovative technologies in the world of computer networks. They provide Internet access to a large number of users, wide bandwidth and high mobility. However, congestion remains one of the major problems of these networks due to the fast growth in internet, large number of users, and the increasing use of broadband links. TCP (Transmission Control Protocol)is the most famous transport layer protocol for the Internet, it makes it possible to transfer and control end-to-end information between two hosts, whether for wired or wireless networks. It uses virtual ports to establish the connection (connection-oriented) and monitors the transmission of information by ensuring reliable data transport. In fact, thanks to it the applications can communicate in a safe way thanks to the system of acknowledgment. Another particularity of TCP is to be able to regulate its data rate thanks to its capacity to emit segments of variable sizes. Therefore, against congestion TCP must be able to ensure a fair and efficient bandwidth allocation for all competing flows, which has made congestion the concern and the center of interest of the network community. For this, different mechanisms of congestion management have emerged. In this paper, we try to evoke the main mechanisms developed to solve this problem (Additive Increase Multiplicative Decrease, Slow Start, .), as well as the main algorithms integrated in TCP's implementations for better congestion management (Tahoe, Reno, New Reno, Vegas .). We make also a simulation including these different algorithms to decide, via real network measurements, their effectiveness. At the end, we conclude that each algorithm has its own characteristics, and the choice of one depends on the circumstances of the network (congested or not) and the criteria that each application requires (latency, packets lost, flow.).
The identification of Impervious Area from Sentinel-2 Imagery Using A Novel Spectral Spatial Residual Convolution Neural Network

With the rapid increasing of urban areas, impervious surfaces play an important role as an indicator of urban development and the change of the city's environment. Due to the wide variety of materials of impervious surfaces, it is an arduous task to draw impervious surfaces. Fortunately, the Sentinel-2 satellite provides accessible multi-spectral imagery with a high spatial resolution to solve this problem. However, huge volumes of Sentinel-2 imagery produced every 5 days need a fast and accurate classifier for impervious mapping. In this paper, a novel spectral spatial residual convolution neural network (SSRCNN) has been designed to deal with the massive imagery for impervious classification with high speed and accuracy. Compared to typical algorithms, deep learning methods are more suitable in this task. The CNN demonstrates great success in image classification. In this study, a comparison between CNN and SSRCNN has been done, and the result shows that the SSRCNN model outperforms the CNN model by about 0.74 percent in terms of overall classification accuracy (OA). The use of the NVIDIA 1080Ti graphics processing unit (GPU) can improve the computational efficiency of the SSRCNN model.
Multi-source Landmark Fusion based on Machine Learning

Network entity landmark is the key foundation of IP geolocaiton which plays an important role in network security. Integrating multi-source landmarks to generate a landmark database with high IP coverage and high location accuracy is an important solution for improving the IP geolocation effect. However, the city-level landmarks provide the city name while street-level landmarks provide the latitude and longitude where they located. Owning to their inconsistent format, the state-of-art fusion algorithm cannot effectively integrate the two types of data. Hence, this paper proposes Lusion, a multi-source landmark fusion algorithm. We first extend the IP addresses in the landmark data sources, then model the location data of the two types of landmarks using the landmark location mixture model, and finally use the expectation -maximization algorithm to estimate the location of the landmarks. The simulation experiments on 25 landmark data sources show that the algorithm can effectively integrate the city-level and street-level landmarks from different data sources, and have a significantly better performance than the original data sources in the location accuracy. Furthermore, we evaluate Lusion on real-world datasets, which consists of 7 city-level and 3 street-level landmark data sources, by locating 100 IP addresses in Hong Kong and Zhengzhou respectively. The geolocation results show that Lusion increased the city-level accuracy by at least 8 percentage points compared with the original data sources, and reduces the geolocation error from 3.42 km to 2.92 km based on the best original landmark data set.
Trial of educational coding material on the theme of image recognition

The recent progress in deep learning has led to the third wave in artificial intelligence, and the supply and development of human resources in this field will be required to power further technological advances. To further human resource development, it is necessary to support those who have acquired logical thinking skills and the fundamentals of programming. Therefore, in this study, we examined educational material relating to image recognition programming targeting university students with limited programming skills and evaluated its effectiveness. We held experimental classes using this educational material for third grade students at Meisei University, and it confirmed that many students would benefit from acquiring a deeper understanding of programming.
"The superhero of the university": experience-driven design and field study of the university guidance robot

Robots have recently gained popularity in customer service. Especially social robots are nowadays utilized in healthcare, elderly homes and schools. Although it is crucial to design social robots according to well-defined user experience goals, research related to experience-driven design of social robots is still scarce. Experience-Driven Design (EDD) is a framework to design interaction for technology based on certain goals, known as experience goals. In this paper, we present the design and evaluation of the university guidance robot based on the user experience goals defined in previous research. The experience goals are nurture, fellowship and recreation. We designed applications, interaction, and robot's behavior to support the fulfillment of the experience goals. The social robot Pepper served as a platform for the university guidance robot. The evaluation was conducted as a field study in a university campus with 32 university students during the orientation week. According to our findings, the university guide robot successfully evoked nurture, fellowship and recreation among participants.
Scale Estimation of Monocular SLAM Using Direct Acceleration Pair Measurements

Monocular SLAM is increasingly being used to provide navigation solutions for autonomous systems. In this, a visual inertial solution is commonly used to get the scale factor estimate for the monocular SLAM problem where the acceleration data from IMU is integrated to get the absolute velocity and position estimates. Since the accelerometer data may have bias and inaccuracies, it may lead to accumulation of bias and noise errors resulting in drift from the true value positions. This may require an additional sensor to correct the drift. In this paper, the scale factor is estimated using the average acceleration pair data of SLAM and IMU and without any third sensor. For accounting the noise on average accelerations, the Maximum Likelihood estimator is proposed. The scale factor is therefore recovered without any integration of IMU acceleration avoiding any drift errors, improving the estimation of scale factor values. This scale estimation method is included in the ORB-SLAM algorithm in a separate thread for real-time implementation. The output of maximum likelihood estimator is compared with simple estimators namely arithmetic mean, geometric mean and median. The real time formulation developed is validated in experiments using an off the self commercial OptiTrack motion capture system. The present approach gives a robust and less complex estimate of scale factor purely from camera and IMU in the presence of noise on acceleration pairs.
Blue piece: a web-based tool to provide screening and intervention services for parents of children with autism spectrum disorder

Early intervention has been associated with the best outcomes for children with autism spectrum disorder (ASD). However, in some cities in Mexico is not easy to get access to early screening and intervention services due to socioeconomic disparities and lack of information. This paper describes Blue Piece, a web-based tool to provide screening and intervention services for parents of children with ASD. Blue Piece allows parents to get access to web-based screening tests, and depending on the test results, it provides information about the intervention services that are available near them. Blue Piece was designed following a user-centered approach. We close with plans for future work.
Autonomous Visual Navigation for Mobile Robots

Autonomous mobile robots are required to move throughout map the environment, locate themselves, and plan paths between positions. Vision stands out among the other senses for its richness and practicality. Even though there are well-established autonomous navigation solutions, as far as we can tell, no complete autonomous navigation system that is solely based on vision and that is suitable for dynamic indoor environments has fully succeeded. This article presents a systematic literature review of methods and techniques used to solve the complete autonomous navigation problem or its parts: localization, mapping, path planning, and locomotion. The focus of this review lays on vision-based methods for indoor environments and ground robots. A total of 121 studies were considered, comprising methods, conceptual models, and other literature reviews published between 2000 and 2017. To the best of our knowledge, this is the first systematic review about vision-based autonomous navigation suitable for dynamic indoor environments. It addresses navigation methods, autonomous navigation requirements, vision benefits, methods testing, and implementations validation. The results of this review show a deficiency in testing and validation of presented methods, poor requirements specification, and a lack of complete navigation systems in the literature. These results should encourage new works on computer vision techniques, requirements specification, development, integration, and systematic testing and validation of general navigation systems. In addition to these findings, we also present the complete methodology used for the systematic review, which provides a documentation of the process (allowing quality assessment and repeatability), the criteria for selecting and evaluating the studies, and a framework that can be used for future reviews in this research area.
Efficiency of Japanese-Vietnamese Translation Job Thanks to the Use of Technology in the Fourth Industrial Revolution

Vietnamese translation background is still unfamiliar with the translation support technologies that international friends have used for a long time. So, what to do to get a perfect translation is considered as an extremely important factor for anyone who has been working in the translation field. In addition to accuracy, the translation must be clear and understandable. Traditional translation methods still have their values but may not meet the ever-changing requirements of constantly moving and developing science. We need to use different technologies to suit our projects. This will help customers satisfy, trust and choose us. The article is done with the support of filling out online survey forms of FPT University students and office staffs currently working at Gifu Kogyo Software Company, Quang Trung park, District 12, Ho Chi Minh City.
Determination of Computationally Efficient Multi-pool Model Fitting Approach for Pulsed Chemical Exchange Saturation Transfer MRI

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is an emerging imaging technique that detects lowly-concentrated labile protons which can have many potential clinical applications. Multi-pool model fitting is a CEST quantification technique that is able to quantify the pure CEST effect of the individual labile proton pools for a more accurate analysis. However, multi-pool model fitting suffers from long computational times which make it impractical for clinical use. There are two approaches with which multi-pool model fitting may be performed. This study aimed to determine the more efficient approach for the multi-pool model fitting for pulsed CEST MRI. Simulated CEST data using published model parameters were applied to assess the computational efficiency of the different approaches. Both approaches were able to produce equal accuracy but one of them was twice more computationally efficient than the other. However, further work is required to further improve the speed of multi-pool model fitting of pulsed CEST for potential future clinical applications.
An Automated Animal Training Apparatus for Operant Conditioning Studies in Rodents

Animal behavioral experiments like operant conditionings are common techniques in the research of biomedical sciences. However, Manual operation of behavioral experiments is usually inaccurate, prone to mistakes and quite time-consuming. It cannot accomplish some complex experiments such as training animals to discriminate different sounds of different frequencies, triggering data acquisition equipment to record electrophysiological signals immediately after an acoustic/optical stimulus. Here, we designed and implemented an automatic apparatus for animal behavior studies, which is competent to perform different kinds of complex stimulus-reward experiments by software settings. The apparatus is consisted of a microcontroller and several peripheral components. Programmable procedures are controlled by the microcontroller for training or testing of rodents, in no need of human intervention. Parameters such as the color, duration and timing of light stimuli, or the frequency, duration and patterns of sound are adjustable by software. Using this apparatus, we conducted experiments and verified that rats had the ability to discriminate sound stimuli with different duration or different cycles. This apparatus worked automatically, stably and reliably, with less manual operation and higher efficiency. It would also be a useful tool for other studies such as learning and memory experiments.
A Burmese (Myanmar) Treebank

A 20,000-sentence Burmese (Myanmar) treebank on news articles has been released under a CC BY-NC-SA license. Complete phrase structure annotation was developed for each sentence from the morphologically annotated data prepared in previous work of Ding et al. [1]. As the final result of the Burmese component in the Asian Language Treebank Project, this is the first large-scale, open-access treebank for the Burmese language. The annotation details and features of this treebank are presented.
Towards a Framework of Implementing Software Robots: Transforming Human-executed Routines into Machines

Software robots tend to increasingly take over organizational processes. However, little is known about principles of building and implementing as opposed to using robotic systems, such as bots for process automation (RPA) and chatbots. Therefore, based on an empirically illustrated theoretical conceptualization of routine automation and affordance actualization, this paper develops a framework that guides how different types of software robots can be built and implemented through transforming a human-executed routine into a robot-automated routine by applying specific implementation guidelines.
Embodying the homunculus in virtual reality: creating an interactive laboratory procedure for brain mapping and visualization

Virtual training has been proposed as a controlled, interactive and efficient way to learn new concepts and acquire new skills. Medical training has shown a fast adoption of virtual reality (VR) technologies in several applications. Although progress has been made to reduce the cost of novel and highly immersive hardware tools, the availability of specialized virtual contents to complement conventional training methods is still limited. In this paper, we present preliminary efforts to create an interactive online platform called the "Neuro VR Lab" which contains different activities aiming to recreate well-known neuroscientific concepts and practical experiments. Particularly, this paper covers the development of the Homunculus VR experience, a hands-on interactive laboratory procedure created to illustrate the concept of mechano-perception and brain mapping. Once in pairs, practitioners use a simple cardboard tool to measure the skin sensitivity of their partners in different parts of the body. By registering this data, practitioners are encouraged to use a VR application that allows visualizing their personalized Homunculus in a recreated virtual laboratory. Full-body tracking through the Kinect V2 sensor allows improving the interactivity with the Homunculus avatar by fostering a more embodied experience. Activities are freely available at the Neuro VR Lab website as well as documents with guidelines to carry out the hands-on activities and the executables to run the interactive applications in VR. Our ultimate goal is to facilitate the adoption of these immersive technologies in schools, universities and education institutions by providing free, virtual and highly interactive applications.
Naive Mesh-to-Mesh Coloured Model Generation using 3D GANs

3D model creation forms a large part of the development process in 3D graphical environments such as games or simulations. If an unsupervised approach can be used to generate high-quality textured models the turnaround in these areas could be greatly improved. Advances in generative deep learning have been shown to understand even complex 3D structures, allowing neural networks to output generations learned from abundant model data. But there are no methods that incorporate colour channels into these techniques, an important factor when attempting to use the generations in an immersive environment. Proposed in this paper is an advancement on the initial voxel-based 3D generative adversarial network (GAN) learning to include colour within the output generated samples through adapting the channels of voxel inputs. Followed by the application of marching cubes to translate the voxel-based models into a naive coloured mesh. The method uses unsupervised learning but requires a target 3D textured model data set. The techniques shown in this paper were tested on a sparse collection of model inputs from a set of open access textured models. The method was tested on a data set of 24 variant models of fish. The outputs from the trained generative model in this paper show promising results, learning the shape and a variety of unique texture patterns.
Mid-Air Gestural Interaction with a Large Fogscreen

Projected walk-through fogscreens have been created, but there is little research on the evaluation of the interaction performance with fogscreens. The present study investigated mid-air hand gestures for interaction with a large fogscreen. Participants (N = 20) selected objects from a fogscreen using tapping and dwell-based gestural techniques, with and without vibrotactile/haptic feedback. In terms of Fitts’ law, the throughput was about 1.4 bps to 2.6 bps, suggesting that gestural interaction with a large fogscreen is a suitable and effective input method. Our results also suggest that tapping without haptic feedback has good performance and potential for interaction with a fogscreen, and that tactile feedback is not necessary for effective mid-air interaction. These findings have implications for the design of gestural interfaces suitable for interaction with fogscreens.
Semantic-based Malware Behavior Description: Past and Future

Traditional malware detection is faced with problems of low efficiency and high cost. In recent years, in order to improve the efficiency and accuracy of malware detection, a method based on semantic malware behavior description has been widely studied. In this paper, we firstly classified the current methods of semantic-based malware behavior description by their evolution and characteristics. Secondly, we performed a statistical analysis for the typical description in different aspects. Finally, through statistical analysis, we revealed the challenges faced by semantic-based malware behavior description and its future development prospects.
FIMIL : A high-throughput deep learning model for abnormality detection with weak annotation in microscopy images

Automatic computer-aided detection plays an important role in biomedical image analysis. Many studies have focused on weak supervised learning as annotation tasks are time-consuming and tedious. Compared with pixel-wise annotation by particular software on the scanned digital high-resolution images, an alternative method of marking out of suspicious regions on microscopy slides is significantly more convenient for pathologists. Additionally, with a focus on dysplasias in the central area, there is a high likelihood of the similar tissues to be found around in clusters. In this paper, for weak annotation on microscopy images, we propose an efficient Foveated Imaging based Multiple Instance Learning (FIMIL) framework to classify weakly-labeled microscopy images. The model also provides multi-scale algorithm for arbitrary image size, in which the patches with highest possibility to contain dysplasia are considered as ”fixation points” in the image. The developed model combines deep convolutional neural networks (CNNs) with multiple instance learning (MIL) for dysplasias detection with only image-level labeling. The benchmark tests are carried out on the marked regions of 40x magnified whole-slide cytology images and the normal/abnormal label and their corresponding possibilities are predicted. Evaluated on the real-life clinical data, our proposed model shows high accuracy and efficiency by weakly-supervised learning. 1