Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Distributed Denial-of-Service Attack Detection Scheme for Software Defined Networking Environments

Software-Defined networking (SDN), as a new paradigm, fixes the shortage that traditional network does not support the dynamic, scalable computing and storage needs of more computing environments. SDN, however, also faces security problems such as vulnerable to DDoS attacks. DDoS attacks are well-known and powerful attacks. DDoS detection and DDoS traffic separation for SDN environments are still an open research issue. DDoS attacks in SDN environments will not only bring damage to target server, but also takes exact impact on SDN system. In this paper, we identify a new type DDoS attack, specifically aiming SDN environment, which is harder to be detected. We propose a novel real-time DDoS detection scheme for SDN environment, by using Principal Component Analysis (PCA) scheme to analyze the network status on traffic packets data. We separate the network into different parts, to reduce the total calculation burden. We compare our scheme with sample entropy, showed our scheme achieves better detecting ability for DDoS attacks.
Finding Concepts with Unexpected Multi-Label Objects Based on Shared Subspace Method

We discuss a method of retrieving unexpected objects for a given query, where each data object is represented as a feature vector and assigned a multi-label as well. Given an object-feature matrix X1 and an object-label matrix X2, we try to simultaneously factorize X1 and X2 as X1 ≈ BV and X2 ≈ SW by means of Nonnegative Shared Subspace Method, where the basis S is a part (subspace) of the basis B. With the help of the shared subspace, thus, we can predict a multi-label for a query feature-vector with unknown labels. Our unexpected object for the query is defined as an object which is similar to the query in the feature space, but is dissimilar in the label space. In order to obtain unexpected objects from several viewpoints of similarity, we formalize our retrieval task as a problem of finding formal concepts satisfying a constraint w.r. t. the unexpectedness. We present an efficient depth-first branch-and-bound algorithm for extracting our target concepts.
Optimum Experimental Design by Shape Optimization of Specimens in Linear Elasticity

The identification of Lame parameters in linear elasticity is considered. An optimum experimental design problem is formulated, which aims at minimizing the size of an associated confidence ellipsoid by optimizing the shape of the specimen. Representations of the Eulerian shape derivative and the shape gradient are derived by means of shape calculus and adjoint techniques. Numerical experiments are conducted, yielding specimens of improved shape.
Anti-Oxidant and Fucoxanthin Contents of Brown Alga Ishimozuku (Sphaerotrichia divaricata) from the West Coast of Aomori, Japan

Fucoxanthin is a specific carotenoid in brown seaweeds with remarkable biological properties. Ishimozuku (Sphaerotrichia divaricata), an edible brown alga from northern Japan, has morphology that is almost identical to that of Okinawa-mozuku (Cladosiphon okamuranus) harvested off Okinawa, Japan. However, because of Ishimozuku’s lower availability compared to Okinawa-mozuku, the contents of its nutrient compounds remain unclear. The present study analyzed fucoxanthin and anti-oxidant compound contents of Ishimozuku harvested off the northern coast of Japan from 2014 to 2016. First, 80% ethanol extract solutions were prepared from Ishimozuku harvested from several west coast areas of Aomori, Japan. Then, polyphenol content was analyzed using the Folin–Ciocalteu method. Then anti-oxidative effects were analyzed by their 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity and hydrogen peroxide scavenging activity. Furthermore, fucoxanthin contents were measured using high performance liquid chromatography (HPLC) analysis. Fucoxanthin contents of Ishimozuku were 105.6–1148.5 μg/g dry weight. Total polyphenol contents of Ishimozuku were of 0.296–0.958 mg/g dry weight: higher than Okinawa-mozuku (0.082 ± 0.011 mg/g dry weight). The anti-oxidation effects of Ishimozuku accompanied the polyphenol content. These results suggest that Ishimozuku contains various anti-oxidant components and has high potential to provide the promotion of human health.
Investigating Domain-Specific Information for Neural Coreference Resolution on Biomedical Texts

Existing biomedical coreference resolution systems depend on features and/or rules based on syntactic parsers. In this paper, we investigate the utility of the state-of-the-art general domain neural coreference resolution system on biomedical texts. The system is an end-to-end system without depending on any syntactic parsers. We also investigate the domain specific features to enhance the system for biomedical texts. Experimental results on the BioNLP Protein Coreference dataset and the CRAFT corpus show that, with no parser information, the adapted system compared favorably with the systems that depend on parser information on these datasets, achieving 51.23% on the BioNLP dataset and 36.33% on the CRAFT corpus in F1 score. In-domain embeddings and domain-specific features helped improve the performance on the BioNLP dataset, but they did not on the CRAFT corpus.
An Approach of Fog Detecting Magnitude Using Referenceless Perceptual Image Defogging

The increasing economy is resulting in heavy pollutions and foggy environments in my regions, which raises up a problem of detecting the magnitude of the fog or smog. Creating an alert system to classify the magnitude of fog is for numerous fields, such as the transportation security, manufacturing management, and disease prevention. However, current fog detection solutions require a long training process from feature extractions to classifications. This paper focuses on the issue of the fog detection and proposes a new approach for classifying the magnitude of fog using the technique of image defogging. The proposed method, called Image Defogging-based Detection for Magnitude of Fog (IDDMF), emphasizes the perceptual fog density considering image corpus from both foggy and fog-free images. The experiment evaluations have provided an assessment for demonstrating the performance of the proposed approach.
Profiling Contributors in the Human-Computer Cloud

The concept of human-computer cloud is an adaptation of a traditional cloud computing paradigm to applications that require human expertise for performing some of the information processing operations. In these environments, it is important to maintain rich contributor profile that would allow to automatically route task requests to the contributors who will most likely complete them with high quality. On the other hand, the burden of filling and updating the profile shouldn't be entirely on the shoulders of contributors. This paper describes the profile structure of human-computer cloud environment and several mechanisms for automatically filling it and keeping up-to-date.
Cost-Effective Redundancy Approach for Fail-Operational Autonomous Driving System

Driverless autonomous driving systems require cost-effective architecture satisfying design diversity and real-time performance to fulfill the fail-operational requirements that sustain system safety if a failure occurs during automated driving. However, conventional approaches cannot be applied to the systems due to design diversity. A key challenge in establishing a cost-effective multi-mode architecture is how to enhance the real-time capability of the mode switch. In this work, we propose three replication methods for fail-operational autonomous driving systems with design diversity: Input Backup Replication (IBR), Extended Primary Backup Replication (E-PBR), and Extended Leader Follower Replication (E-LFR). These methods enable accelerated recovery processing by utilizing input data and internal state backup in addition to partial hot standby. We implemented an autonomous driving prototype and found that (i) the proposed replication methods can satisfy the performance requirements for fail-operational systems, (ii) they can reduce 53.8 % of the CPU load compared with the hot standby approach in the normal mode, and (iii) the memory consumption ratio caused by the proposed methods is 0.01%. These results demonstrate that our proposed replication methods are feasible for fail-operational autonomous driving systems with design diversity.
An Approximate Parallel Vectors Operator for Multiple Vector Fields

The Parallel Vectors (PV) Operator extracts the locations of points where two vector fields are parallel. In general, these features are line structures. The PV operator has been used successfully for a variety of problems, which include finding vortex‐core lines or extremum lines. We present a new generic feature extraction method for multiple 3D vector fields: The Approximate Parallel Vectors (APV) Operator extracts lines where all fields are approximately parallel. The definition of the APV operator is based on the application of PV for two vector fields that are derived from the given set of fields. The APV operator enables the direct visualization of features of vector field ensembles without processing fields individually and without causing visual clutter. We give a theoretical analysis of the APV operator and demonstrate its utility for a number of ensemble data.
Field Tests and Simulation of Ground and Building Vibrations Caused by Metros on an Elevated Bridge

The vibration induced by metros is having a growing impact on people’s daily work and life. This paper proposes a propagation rule and an evaluation method for vibration based on experimental data on the ground and inside a 4-story building subjected to trains running on an elevated bridge in Guangzhou, China. This paper discusses the propagation characteristics of the vibration wave in different directions on the ground and inside the building. Then, a vibration level attenuation model of an elevated bridge section of Guangzhou was built using a linear regression method. Moreover, a finite-element model of a test building was built to study the influence of the building structure on the building vibration response. The results reveal that vertical vibrations are much larger than the horizontal vibrations on the ground and inside the building near the elevated bridge. This paper found a rebound acceleration area at a distance of 10–15 m away from the bridge. Inside the four-story building, the acceleration levels increased gradually with increasing floor level. Slab thickness had little effect on the vibration and the story height of the building along the elevated bridge should be limited to below 3.7 m. Moreover, proper column size can effectively reduce building vibrations.
Deformable Convolutional Neural Networks for Hyperspectral Image Classification

Convolutional neural networks (CNNs) have recently been demonstrated to be a powerful tool for hyperspectral image (HSI) classification, since they adopt deep convolutional layers whose kernels can effectively extract high-level spatial–spectral features. However, sampling locations of traditional convolutional kernels are fixed and cannot be changed according to complex spatial structures in HSIs. In addition, the typical pooling layers (e.g., average or maximum operations) in CNNs are also fixed and cannot be learned for feature downsampling in an adaptive manner. In this letter, a novel deformable CNN-based HSI classification method is proposed, which is called deformable HSI classification networks (DHCNet). The proposed network, DHCNet, introduces the deformable convolutional sampling locations, whose size and shape can be adaptively adjusted according to HSIs’ complex spatial contexts. Specifically, to create the deformable sampling locations, 2-D offsets are first calculated for each pixel of input images. The sampling locations of each pixel with calculated offsets can cover the locations of other neighboring pixels with similar characteristics. With the deformable sampling locations, deformable feature images are then created by compressing neighboring similar structural information of each pixel into fixed grids. Therefore, applying the regular convolutions on the deformable feature images can reflect complex structures more effectively. Moreover, instead of adopting the pooling layers, the strided convolution is further introduced on the feature images, which can be learned for feature downsampling according to spatial contexts. Experimental results on two real HSI data sets demonstrate that DHCNet can obtain better classification performance than can several well-known classification methods.
Theory and Engineering of Scheduling Parallel Jobs

Scheduling is very important for an efficient utilization of modern parallel computing systems. In this thesis, four main research areas for scheduling are investigated: the interplay and distribution of decision makers, the efficient schedule computation, efficient scheduling for the memory hierarchy and energy-efficiency. The main result is a provably fast and efficient scheduling algorithm for malleable jobs. Experiments show the importance and possibilities of scheduling considering the memory hierarchy.
A Review on the Extraction of Quantitative Retinal Microvascular Image Feature

Digital image processing is one of the most widely used computer vision technologies in biomedical engineering. In the present modern ophthalmological practice, biomarkers analysis through digital fundus image processing analysis greatly contributes to vision science. This further facilitates developments in medical imaging, enabling this robust technology to attain extensive scopes in biomedical engineering platform. Various diagnostic techniques are used to analyze retinal microvasculature image to enable geometric features measurements such as vessel tortuosity, branching angles, branching coefficient, vessel diameter, and fractal dimension. These extracted markers or characterized fundus digital image features provide insights and relates quantitative retinal vascular topography abnormalities to various pathologies such as diabetic retinopathy, macular degeneration, hypertensive retinopathy, transient ischemic attack, neovascular glaucoma, and cardiovascular diseases. Apart from that, this noninvasive research tool is automated, allowing it to be used in large-scale screening programs, and all are described in this present review paper. This paper will also review recent research on the image processing-based extraction techniques of the quantitative retinal microvascular feature. It mainly focuses on features associated with the early symptom of transient ischemic attack or sharp stroke.
Diet Can Impact Microbiota Composition in Children With Autism Spectrum Disorder

Diet is one of the most influential environmental factors in determining the composition of the gastrointestinal microbiota. Microbial dysbiosis in children with Autism Spectrum Disorder (ASD) and the impact of some bacterial taxa on symptoms of ASD has been recognized. Children with ASD are often described as picky eaters with low intake of fiber-rich foods, including fruits and vegetables. However, the impact of diet on the microbiota composition in children with ASD is largely unknown. Herein, fecal samples, 3 day food diaries and the Youth and Adolescence Food Frequency questionnaire (YAQ) were collected from children with ASD (ASD; n = 26) and unaffected controls (CONT; n = 32). Children's ASD symptoms were determined using the Pervasive Developmental Disorder Behavior Inventory Screening Version (PDDBI-SV). Differences in the microbiota composition at the phyla, order, family, and genus level between ASD and CONT were observed. Microbiota composition of children with ASD was investigated in relation to feeding behavior, nutrient and food group intake as well as dietary patterns derived from the YAQ. In children with ASD, two distinct dietary patterns (DP) were associated with unique microbial profiles. DP1, characterized by higher intakes of vegetables, legumes, nuts and seeds, fruit, refined carbohydrates, and starchy vegetables, but lower intakes of sweets, was associated with lower abundance of Enterobacteriaceae, Lactococcus, Roseburia, Leuconostoc, and Ruminococcus. DP2, characterized by low intakes of vegetables, legumes, nuts and seeds and starchy vegetables, was associated with higher Barnesiellaceae and Alistipes and lower Streptophyta, as well as higher levels of propionate, isobutyrate, valerate, and isovalerate. Peptostreptococcaceae and Faecalibacterium predicted social deficit scores in children with ASD as measured by the PDDBI-SV. Diet-associated microbial profiles were related to GI symptoms, but no significant interaction between nutrition and microbiota in predicting social deficit scores were observed. In conclusion, dietary patterns associated with fecal microbiota composition and VFA concentrations in children with ASD were identified. Future studies using a larger sample size and measuring other behaviors associated with ASD are needed to investigate whether dietary intake may be a modifiable moderator of ASD symptoms.
Multilingual Seq2seq Training with Similarity Loss for Cross-Lingual Document Classification

In this paper we continue experiments where neural machine translation training is used to produce joint cross-lingual fixed-dimensional sentence embeddings. In this framework we introduce a simple method of adding a loss to the learning objective which penalizes distance between representations of bilingually aligned sentences. We evaluate cross-lingual transfer using two approaches, cross-lingual similarity search on an aligned corpus (Europarl) and cross-lingual document classification on a recently published benchmark Reuters corpus, and we find the similarity loss significantly improves performance on both. Furthermore, we notice that while our Reuters results are very competitive, our English results are not as competitive, showing room for improvement in the current cross-lingual state-of-the-art. Our results are based on a set of 6 European languages.
Vanguard: A Cache-Level Sensitive File Integrity Monitoring System in Virtual Machine Environment

Sensitive files in computer systems such as executable programs, configuration, and authorization information have a great importance of their own, in terms of both confidentiality and functionality. To protect sensitive files, an effective approach named as file integrity monitoring is proposed to detect aggressive behaviors by verifying all the actions on these sensitive files. However, due to semantic gap problems, current file integrity monitoring tools are incapable of monitoring files in memory, so that an illegal modification of a file may bypass the detection by deliberately hiding itself inside the cache without actually committing to the disk. In this paper, we propose a runtime sensitive file integrity monitoring system named Vanguard, to satisfy the requirement of cache-level file protection. It can monitor both IO operations and cache operations, thereby deterring the illegal file accesses. To achieve the cache-level monitoring, we explore the techniques to detect when sensitive files are loaded into and swapped out from the operating system page cache. Vanguard is isolated from the monitored system so it is hard to be subverted. We implement Vanguard on QEMU/KVM platform, and both Linux and Windows guest operating systems are supported. We conduct several experiments, and the experimental results show the effectiveness of Vanguard and imply that our method incurs acceptable overhead.
Cross-Layer Design for Network Lifetime Maximization in Underwater Wireless Sensor Networks

This paper investigates the cross-layer design problem with the goal of maximizing the network lifetime for energy-constrained underwater wireless sensor networks (UWSNs). We first jointly consider link scheduling, transmission power and transmission rate in a proposed optimization problem with the adoption of time division multiple access (TDMA) schedules. Then, we propose an iterative algorithm to solve the optimization problem. It alternates between (1) link scheduling and (2) computation of transmission powers and transmission rates. In fact, the convergence of such iterative algorithm can be mathematically and empirically supported. We evaluate our algorithm for several network topologies. Extensive simulation results demonstrate the superiority of the proposed approach.
Multi-Fidelity Black-Box Optimization with Hierarchical Partitions

Motivated by settings such as hyper-parameter tuning and physical simulations, we consider the problem of black-box optimization of a function. Multi-fidelity techniques have become popular for applications where exact function evaluations are expensive, but coarse (biased) approximations are available at much lower cost. A canonical example is that of hyper-parameter selection in a learning algorithm. The learning algorithm can be trained for fewer iterations – this results in a lower cost, but its validation error is only coarsely indicative of the same if the algorithm had been trained till completion. We incorporate the multi-fidelity setup into the powerful framework of black-box optimization through hierarchical partitioning. We develop tree-search based multi-fidelity algorithms with theoretical guarantees on simple regret. We finally demonstrate the performance gains of our algorithms on both real and synthetic datasets.
Exponentially Twisted Sampling for Centrality Analysis in Attributed Networks

In this paper, we conduct centrality analysis for attributed networks. An attributed network, as a generalization of a graph, has node attributes and edge attributes that represent the ``features'' of nodes and edges. Traditionally, centrality analysis of a graph is done by providing a sampling method, such as a random walk, for the graph. To take node attributes and edge attributes into account, the sampling method in an attributed network needs to be twisted from the original sampling method in the underlining graph. For this, we consider the family of exponentially twisted sampling methods and propose using path measures to specify how the sampling method should be twisted. For signed networks, we define the influence centralities by using a path measure from opinions dynamics and the trust centralities by using a path measure from a chain of trust. For attributed networks with node attributes, we also define advertisement-specific influence centralities by using a specific path measure that models influence cascades in such networks. Various experiments are conducted to further illustrate these centralities by using two real datasets: the political blogs and the MemeTracker dataset.
Body Part Extraction and Pose Estimation Method in Rowing Videos

This paper describes an image processing approach capable of estimating the pose of athletes exercising on indoor rowing machines in video sequences. The proposed algorithm finds and tracks the wrist, elbow, shoulder, ankle, knee, hip and head, and the line of the back also. Our contribution is twofold. The first contri- bution is a new background subtraction method, which can reliably separate the silhouette of athletes under some assumptions related to the videos. Furthermore, the paper introduces – as the second contribution – a skeleton fitting method to find the joints of the athletes based on the results of the background subtraction. This algorithm is based on anthropometric data and special movement patterns. The overall solution works on a real time setting in the test environment. Compar- ing the results, it is shown that our method surpasses the most accurate state-of-the-art general pose estima- tion solution for indoor rowing specific videos based on two commonly used metrics, as well.