Miracle-dz/UniFAR-FTUs huggingface dataset:
SYN‐Guard: An effective counter for SYN flooding attack in software‐defined networking

In software‐defined networking (SDN), TCP SYN flooding attack is considered as one of the most effective attacks to perform control plane and target server saturation. In this attack, an attacker generates a large number of malicious SYN requests, and because of the absence of the forwarding rules, the data plane switches have to forward these SYN messages to the controller. This excessive forwarding causes congestion over the communication channel between a data plane and control plane, and it also exhausts computational resources at both the planes. In this paper, we propose a novel countermeasure called SYN‐Guard to detect and prevent SYN flooding in SDN networks. We fully implement SYN‐Guard on the SDN controller to validate the incoming TCP connection requests. The controller installs forwarding rules for the SYN requests that successfully clear the validation test of SYN‐Guard. The host of the fake SYN request is detected, and SYN‐Guard prevents it from sending any further SYN requests to the data plane switch. The performance evaluation done using the simulation results shows that SYN‐Guard exhibits low side effect for genuine TCP requests, and when compared with standard SDN and state‐of‐art proposals, it reduces the average response time up to 21% during an ongoing SYN flooding attack.
Object Detection in The Image Using the Method of Selecting Significant Structures

The approach to image segmentation is reviewed in the article. The method of highlighting significant contours in the image is reviewed. Some structures in the image attract attention more than others due to certain distinctive properties. The article reviews the approach of highlighting significant structures in the image representing the areas of candidates identifying the object in the video frame for mobile platforms. For example, such shapes can be smoother, longer and closed. Such structures are called significant. It would be expedient to use only these significant structures to increase the speed of image recognition by computer vision methods focused on the contour selection. This approach allocates the computing resources only to significant structures, thus reducing the total computation time. Since the image consists of many pixels and links between them, which are called edges, significant structures can be measured. The article presents an approach to measuring the structure significance that largely coincides with human perception. Some image structures attract our attention without the need for a systematic scan of the entire image. In most cases, this significance represents the structure properties as a whole, i.e. parts of the structure cannot be isolated. This article presents a measure of significance based on the measurement of length and curvature. The measure highlights structures characteristic of human perception, and they often correspond to objects of interest in the image. A method is presented for calculating significance using an iterative scheme combined into a single local network for processing elements. The optimization approach to represent a processed image highlighting significant locations is used in the network.
A Semidefinite Programming Approach for Harmonic Balance Method

The harmonic balance method is broadly employed for analyzing and predicting the periodic steady-state solution. Most of the traditional methods in the literature do not guarantee global optimality. Due to its nonconvexity, it is a complex task to find a global solution to the harmonic balance problem in the frequency domain. A convex relaxation in the form of semidefinite programming has attracted attention since its introduction because it yields a global solution in most cases. This paper introduces a novel optimization-based approach to predict periodic solution by determining the Fourier series coefficients with high accuracy. Unlike the other commonly used methods, the proposed approach is completely independent of initial conditions. In our proposed method, the nonlinear constraints composed of time-dependent trigonometric functions are converted into nonlinear algebraic polynomial equations. Then, nonlinear unknowns are convexified through moment-sum of squares approach. However, computing a global solution costs a higher runtime. Our approach is validated through small examples which contain only polynomial nonlinearities. In all cases, the Mosek solver shows a better performance in comparison with SDPT3 and SeDuMi solvers. The proposed method shows high computational cost as a result of an increase in the positive semidefinite matrix size, which can depend on the number of harmonics and the degree of nonlinearity.
Transfer Learning in Knowledge-Intensive Tasks: A Test in Healthcare Text Analytics

Transfer learning has been widely utilized in many machine learning applications. However, our literature review suggests that transfer learning, especially relational knowledge transfer learning, has been underutilized in healthcare analytics tasks. In this study, we identify and incorporate three different types of knowledge transfer approaches into a deep learning-based healthcare text analytics model. Our evaluation results suggest that the improved performance can be achieved by incorporating the three relational knowledge transfer learning approaches individually and in combined. This study provides an excellent starting point for studying different types of transfer learning approaches in detail.
Content Pushing Over Multiuser MISO Downlinks With Multicast Beamforming and Recommendation: A Cross-Layer Approach

Proactive caching is recognized as a promising approach to handle the rapid growth of data traffic, thereby attracting much attention recently. As a key performance metric of caching, the hit ratio is determined by demand probabilities of users for content items and caching decisions. Because the recommendation system is capable of shaping user demands, the joint caching and recommendation holds the potential of improving the hit ratio substantially. In this paper, joint pushing and recommendation (JPR) schemes are presented for multiuser multiple-input single-output (MISO) systems, in which content items are pushed over MISO downlinks with multicast beamforming. Aiming at maximizing the effective throughput, we formulate a multi-stage stochastic programming problem under the constraints of transmit power and quality of experience (QoE). Since the formulated problem is intractable, suboptimal online JPR policies are presented based on the convex–concave procedure and branch-and-bound methods. Simulations show that presented JPR policies are capable of attaining significant effective throughput gains.
AUTOMATIC SEA BIRD DETECTION FROM HIGH RESOLUTION AERIAL IMAGERY

Abstract. Great efforts are presently taken in the scientific community to develop computerized and (fully) automated image processing methods allowing for an efficient and automatic monitoring of sea birds and marine mammals in ever-growing amounts of aerial imagery. Currently the major part of the processing, however, is still conducted by especially trained professionals, visually examining the images and detecting and classifying the requested subjects. This is a very tedious task, particularly when the rate of void images regularly exceeds the mark of 90%. In the content of this contribution we will present our work aiming to support the processing of aerial images by modern methods from the field of image processing. We will especially focus on the combination of local, region-based feature detection and piecewise global image segmentation for automatic detection of different sea bird species. Large image dimensions resulting from the use of medium and large-format digital cameras in aerial surveys inhibit the applicability of image processing methods based on global operations. In order to efficiently handle those image sizes and to nevertheless take advantage of globally operating segmentation algorithms, we will describe the combined usage of a simple performant feature detector based on local operations on the original image with a complex global segmentation algorithm operating on extracted sub-images. The resulting exact segmentation of possible candidates then serves as a basis for the determination of feature vectors for subsequent elimination of false candidates and for classification tasks.
A CMOS smart temperature sensor with a 3σ inaccuracy of ±0.1°C from -55°C to 125°C

A low-cost temperature sensor with on-chip sigma-delta ADC and digital bus interface was realized in a 0.5 μm CMOS process. Substrate pnp transistors are used for temperature sensing and for generating the ADC's reference voltage. To obtain a high initial accuracy in the readout circuitry, chopper amplifiers and dynamic element matching are used. High linearity is obtained by using second-order curvature correction. With these measures, the sensor's temperature error is dominated by spread on the base-emitter voltage of the pnp transistors. This is trimmed after packaging by comparing the sensor's output with the die temperature measured using an extra on-chip calibration transistor. Compared to traditional calibration techniques, this procedure is much faster and therefore reduces production costs. The sensor is accurate to within ±0.5 °C (3σ) from -50 °C to 120 °C.
Visible Light Positioning via Floor Reflections

The last two decades have witnessed significant advances in the field of indoor positioning systems, which have led to technologies with high location precision. However, there still lacks robust meter-level indoor positioning approaches which only rely on in-building communication infrastructures and smartphones. We present STARLIT, a system that enables a single LED beacon to localize smartphones to within sub-meter. As the smartphone camera contains millions of pixels, we create a virtual sensor array with the camera to measure the received signal strength (RSS) of the LED beacon. Different from the existing camera-based approaches, which need to capture images of the LED within a short light-to-camera distance, we utilize the reflection light from the floor. By exploiting the rolling shutter mechanism in the smartphone cameras, we propose a solution to separate the signal layer from the image background and noise. Given the measured RSSs, we establish an equation set with the Lambertian model and the camera projection model to solve the location of the smartphone. We have implemented STARLIT and evaluated its performance in an office room. Our experiments demonstrate that the STARLIT can achieve a median error of 23cm and an 80-percentile error of 55cm.
Bayesian analysis of continuous time Markov chains with applications to phylogenetics

Bayesian analysis of continuous time, discrete state space time series is an important and challenging problem, where incomplete observation and large parameter sets call for user-defined priors based on known properties of the process. Generalized Linear Models (GLM) have a largely unexplored potential to construct such prior distributions. We show that an important challenge with Bayesian generalized linear modelling of Continuous Time Markov Chains (CTMCs) is that classical Markov Chain Monte Carlo (MCMC) techniques are too ineffective to be practical in that setup. We propose two computational methods to address this issue. The first algorithm uses an auxiliary variable construction combined with an Adaptive Hamiltonian Monte Carlo (AHMC) algorithm. The second algorithm combines Hamiltonian Monte Carlo (HMC) and Local Bouncy Particle Sampler (LBPS) to take advantage of the sparsity in certain high-dimensional rate matrices. We propose a characterization for a class of sparse factor graphs, where LBPS can be efficient. We also provide a framework to assess the computational complexity for the algorithm. An important aspect of practical implementation of Bouncy Particle Sampler (BPS) is the simulation of event times. Default implementations use conservative thinning bounds. Such bounds can slow down the algorithm and limit the computational performance. In the second algorithm, we develop exact analytical solutions to
An asymptotic problem in coding theory

~ k The asymptotic behaviour of x . '. E (j(kx»" for n ~ 00 and of .'.E IT I(ix) for x lOis detennined k~ k~i~ together with explicit bounds for the hidden constants. Here I is a positive nonincreasing function on [0,(0) with given asymptotic behaviour at zero.
Incremental and parallel algorithms for dense subgraph mining

xvii CHAPTER 1. OVERVIEW . 1 1.
Topological metal at the surface of an ultrathin Bi 1 − x Sb x alloy film

T. Hirahara,1,* Y. Sakamoto,1 Y. Saisyu,1 H. Miyazaki,2 S. Kimura,2 T. Okuda,3,† I. Matsuda,3 S. Murakami,4 and S. Hasegawa1 1Department of Physics, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan 2UVSOR Facility, Institute for Molecular Science, Okazaki 444-8585, Japan 3Synchrotron Radiation Laboratory, ISSP, University of Tokyo, Kashiwa 277-8581, Japan 4Department of Physics, Tokyo Institute of Technology, Tokyo 152-8551, Japan Received 28 January 2010; revised manuscript received 25 March 2010; published 15 April 2010
Packet-oriented QoS management model for a wireless Access Point

This document introduces a packet-oriented QoS management model applicable to a wireless Access Point, referred to as "IP aware Access Point". This model may be used in addition, or as a substitute to the connection-oriented traditional QoS model defined for cellular mobile networks, based on the notion of bearer.
Projecting emotions from artworks to maps using neural style transfer

Abstract. Recent advances in deep learning have facilitated the exchange of styles and textures between input images to create unique synthesised outputs. This paper assesses the applicability of neural style transfer to cartography and evaluates to what degree emotions attached to input images can be preserved in maps co-created by human and algorithm. As a source of emotions we utilized personal paintings created during a workshop with international artists at the School of Machines, Making & Make-Believe in August 2018. The neural style transfer was used as a tool to transfer the characteristics of the artworks onto the map. Differences in emotion perception between human-made textures and generated maps were assessed with an online survey completed by 1187 users. The results confirmed that emotional descriptions remain the same before and after the procedure of neural style transfer. The users perceived artificially generated maps as interesting and visually pleasing artefacts. Artworks with variety of line, point and surface depictions were the most suitable algorithm inputs and achieved better visual results in representing the map content. After analysing the neural style transfer technique and identifying its limitations for cartographic style and map content, we conclude with plausible directions for future research.
An airborne regional carbon balance for Central Amazonia

We obtained regional estimates of surface CO2 exchange rates using atmospheric boundary layer budgeting techniques above tropical forest near Manaus, Brazil. Comparisons were made with simultaneous measurements from two eddy covariance towers below. Although there was good agreement for daytime measurements, large differences emerged for integrating periods dominated by the night-time fluxes. These results suggest that a systematic underestimation of night time respiratory effluxes may be responsible for the high Amazonian carbon sink suggested by several previous eddy covariance studies. Large CO2 fluxes from riverine sources or high respiratory losses from recently disturbed forests do not need to be invoked in order to balance the carbon budget of the Amazon. Our results do not, however, discount some contribution of these processes to the overall Amazon carbon budget.
Application of Memory Reduction into Environmentally Invariant Regressor CNN

In recent years, the need for computer vision applications such as object/scene classification has grown rapidly. For example, tracking of vehicles and recognizing number plates can be used to automatically estimate traffic flow. These applications require high environmental invariance due to weather conditions, illumination changes, etc.) as well as compact design and fast rocessing speed in order to be deployed into edge devices. In this paper, we consider the regression problem for vehicle’s number plate recognition. It is shown in [1] that recognition accuracy increases if letters/digits are geometrically transformed beforehand to fix distortions or mis-alignments. However, using CNN to realize such regression task requires a large nunber of parameters. We propose to use low-rank approximation and fine-tuning to reduce the size of such CNNs. Experiments show that under various environments, our method is effective in greatly reducing memory consumption without significantly degrading performance.
Viscoelasticity Measurements of knee Muscles with Simulated Knee Osteoarthritis Treated by Novel Chinese Medicine: A Preliminary Study

Knee osteoarthritis (KOA) has been developed to be one of the most popular chronic joint disease in elder. Muscle shrink or force reduction of lower limb muscles such as gastrocnemius and quadriceps is a pathogenic factor about KOA. Therefore, imaging techniques such as MRI and CT for observing muscles morphological difference become popular in diagnosing KOA in clinic. However, the image information in different populations may vary according to age, weight, sex, and may be influenced by the subjective judgment according to doctor's experience. Viscoelasticity is a quantitative indicator for assessing the biomechanical properties of muscle, but there's lack of research to describe the relationship between viscoelasticity of lower limb muscles and KOA. The purpose of this study is to discuss the difference of viscoelasticity during lower limb muscles paining. We designed an experiment called muscle perfusion to simulate the muscle pain of KOA patients, and measured the lower limb muscles' viscoelasticity using shear-wave dispersion ultra-sound vibrometry (SDUV). Besides, we detected the viscoelasticity changes of a volunteer who had lower limb pain (which was similar to the symptom of KOA) during Fu's Subcutaneous Needling (FSN) treatment. Results show that during FSN treatment as well as perfusion, the elasticity and viscosity of lower limb muscles changed significantly, showing that viscoelasticity was a potential quantitative index to diagnose and qualify the disease of KOA.
A Longitudinal Analysis of Bug Handling Across Eclipse Releases

Large open source software projects, like Eclipse, follow a continuous software development process, with a regular release cycle. During each release, new bugs are reported, triaged and resolved. Previous studies have focused on various aspects of bug fixing, such as bug triaging, bug prediction, and bug process analysis. Most studies, however, do not distinguish between what happens before and after each scheduled release. We are also unaware of studies that compare bug fixing activities across different project releases. This paper presents an empirical analysis of the bug handling process of Eclipse over a 15-year period, considering 138K bug reports from Bugzilla, including 16 annual Eclipse releases and two quarterly releases in 2018. We compare the bug resolution rate, the fixing rate, the bug triaging time and the fixing time before and after each release date, and we study the possible impact of "release pressure". Among others, our results reveal that Eclipse bug handling activity is improving over time, with an important decrease in the number of reported bugs before releases, an increase in the bug fixing rate and an increasingly balanced bug handling workload before and after releases. The recent transition from an annual to a quarterly release cycle continued to improve the bug handling process.
DIVA: Exploration and Validation of Hypothesized Drug‐Drug Interactions

Adverse reactions caused by drug‐drug interactions are a major public health concern. Currently, adverse reaction signals are detected through a tedious manual process in which drug safety analysts review a large number of reports collected through post‐marketing drug surveillance. While computational techniques in support of this signal analysis are necessary, alone they are not sufficient. In particular, when machine learning techniques are applied to extract candidate signals from reports, the resulting set is (1) too large in size, i.e., exponential to the number of unique drugs and reactions in reports, (2) disconnected from the underlying reports that serve as evidence and context, and (3) ultimately requires human intervention to be validated in the domain context as a true signal warranting action. In this work, we address these challenges though a visual analytics system, DIVA, designed to align with the drug safety analysis workflow by supporting the detection, screening, and verification of candidate drug interaction signals. DTVA's abstractions and encodings are informed by formative interviews with drug safety analysts. DIVA's coordinated visualizations realize a proposed novel augmented interaction data model (AIM) which links signals generated by machine learning techniques with domain‐specific metadata critical for signal analysis. DIVA's alignment with the drug review process allows an analyst to interactively screen for important signals, triage signals for in‐depth investigation, and validate signals by reviewing the underlying reports that serve as evidence. The evaluation of DIVA encompasses case‐studies and interviews by drug analysts at the US Food and Drug Administration ‐ both of which confirm that DIVA indeed is effective in supporting analysts in the critical task of exploring and verifying dangerous drug‐drug interactions.
A low cost indoor localization system for mobile robot experimental setup

Indoor localization becomes one of the most important part in mobile robot system One fundamental requirement is to provide an easy-to-use and practical localization system for real-time experiments. In this paper we propose a combination of a recent open source virtual reality (VR) tools, a simple MATLAB code and a low cost USB webcam as an indoor mobile robot localization system Using the VR tools as a server and MATLAB as a client, the proposed solution can cover up to 1.6 [m] × 3.2 [m] with the measurement position accuracy up to 1.2 [cm]. The system is insensitive to light, easy to move and can be quickly set up. A series of successful real-time experiments with three different mobile robot types has been conducted.