Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards long-lead forecasting of extreme flood events: a data mining framework for precipitation cluster precursors identification

The development of disastrous flood forecasting techniques able to provide warnings at a long lead-time (5-15 days) is of great importance to society. Extreme Flood is usually a consequence of a sequence of precipitation events occurring over from several days to several weeks. Though precise short-term forecasting the magnitude and extent of individual precipitation event is still beyond our reach, long-term forecasting of precipitation clusters can be attempted by identifying persistent atmospheric regimes that are conducive for the precipitation clusters. However, such forecasting will suffer from overwhelming number of relevant features and high imbalance of sample sets. In this paper, we propose an integrated data mining framework for identifying the precursors to precipitation event clusters and use this information to predict extended periods of extreme precipitation and subsequent floods. We synthesize a representative feature set that describes the atmosphere motion, and apply a streaming feature selection algorithm to online identify the precipitation precursors from the enormous feature space. A hierarchical re-sampling approach is embedded in the framework to deal with the imbalance problem. An extensive empirical study is conducted on historical precipitation and associated flood data collected in the State of Iowa. Utilizing our framework a few physically meaningful precipitation cluster precursor sets are identified from millions of features. More than 90% of extreme precipitation events are captured by the proposed prediction model using precipitation cluster precursors with a lead time of more than 5 days.
Problems in Dynamic Traffic Grooming in Optical Networks ∗

Traffic grooming has recently emerged as an important area in wide area wavelength-routed optical networks research. This research area addresses the problem of multiplexing lower rate traffic demands into wavelength channels of high bandwidth, so as to reduce network equipment cost by maximizing the optical (wavelength) routing and minimizing electronic routing. Recently, there have been efforts to extend studies of this kind to cases of dynamic traffic. However, this area is still comparatively new, and most of the proposed approaches ignore the optical routing maximization aspect in favor of simply considering blocking probability. In this position paper, we describe a range of problems that we consider appropriate for consideration under the umbrella of dynamic grooming, and show what the various dimensions are along which various flavors of this class of problems may be found. We provide some sample formulations describing some of these problems precisely.
Incremental rule induction based on updates of statistical indices

This paper proposes a new framework for incremental learning based on incremental sampling scheme and rule layers constrained by inequalities of accuracy and coverage. Incremental sampling scheme shows that the number of patterns of updates of accuracy and coverage is four, which give two important inequalities of accuracy and coverage for induction of probabilistic rules. By using these two inequalities, the proposed method classifie a set of formulae into four layers: the rule layer, subrule layer (in and out) and the non-rule layer. Using these layers, updates of probabilistic rules are equivalent to their movement between layers. The proposed method was evaluated on datasets regarding headaches and meningitis, and the results show that the proposed method outperforms the conventional methods.
Signal space diversity based cooperative inter-cell downlink transmission for cell boundary users

In this paper, we present a novel cooperative downlink beamforming framework for the cell edge users based on the local channel state information (CSI). In the proposed scheme, the neighboring two base stations (BSs) transmit and remain silent alternatively via integrating signal space diversity into inter-cell time sharing. Pairwise error probability analysis demonstrates that the multi-cell spatial diversity gain can be achieved for each data stream. Thus the inter-cell links become beneficial rather than detrimental. For comparison, some existing practical cooperative beamforming strategies are also discussed, such as straightforward time sharing and distributed cooperative beamforming. Both theoretical analysis and simulation results confirm that the proposed scheme outperforms the existing relevant strategies. It is shown that in the interested range of noise powers, the proposed cooperative inter-cell scheduling can significantly improve the error performance in a distributed manner while maintaining the same multiplexing gain.
Codeword or Noise? Exact Random Coding Exponents for Joint Detection and Decoding

We consider the problem of coded communication, where in each time frame, the transmitter is either silent or transmits a codeword from a given (randomly selected) codebook. The task of the decoder is to decide whether transmission has taken place, and if so, to decode the message. We derive the optimum detection/decoding rule in the sense of the best tradeoff among the probabilities of decoding error, false alarm, and misdetection. For this detection/decoding rule, we then derive single-letter characterizations of the exact exponential rates of these probabilities for the average code in the ensemble. It is shown that previously proposed decoders are in general strictly suboptimal.
A Parallel Learning Algorithm for Bayesian Inference Networks

We present a new parallel algorithm for learning Bayesian inference networks from data. Our learning algorithm exploits both properties of the MDL-based score metric, and a distributed , asynchronous, adaptive search technique called nagging. Nagging is intrinsically fault tolerant, has dynamic load balancing features, and scales well. We demonstrate the viability, eeectiveness, and scalability of our approach empirically with several experiments using on the order of 20 machines. More speciically, we show that our distributed algorithm can provide optimal solutions for larger problems as well as good solutions for Bayesian networks of up to 150 variables.
Immersive design of DMA molecules with a tangible interface

This work presents an experimental immersive interface for designing DNA components for application in nanotechnology. While much research has been done on immersive visualization, this is one of the first systems to apply advanced interface techniques to a scientific design problem. This system uses tangible 3D input devices (tongs, a raygun, and a multipurpose handle tool) to create and edit a purely digital representation of DNA. The tangible controllers are associated with functions (not data) while a virtual display is used to render the model. This interface was built in collaboration with a research group investigating the design of DNA tiles. A user study shows that scientists find the immersive interface more satisfying than a 2D interface due to the enhanced understanding gained by directly interacting with molecules in 3D space.
Perceptions of Corporal Punishment among Creole and Maroon Professionals and Community Members in Suriname

Child discipline is a vital part of child-rearing in all cultures. The need for child discipline is generally recognised, but considerable debate exists regarding the best methods. Corporal punishment (CP) is a dominant practice in Caribbean cultures. This qualitative study investigated community perceptions of the function, legality and boundaries of CP in child-rearing practices in Suriname, in which CP is defined as hitting a child on their buttocks or extremities using an open hand. Twelve focus group discussions were conducted with adolescent and adult community members from Creole and Maroon backgrounds, as well as with professionals working with children. ATLAS.ti (version 7.5.4), a qualitative data package, was used to conduct the analyses. This study showed how violent forms of disciplining children are widely accepted and practised in Suriname. CP is considered a necessary and respected form of disciplining children, particularly by parents. Participants know about the existence of the United Nations Convention on the Rights of the Child that has been ratified in Suriname, but there is a lack of knowledge about its content. Developing appropriate policy responses to violence towards children requires understanding of the perception and use of CP. Such knowledge is needed to tackle the invisibility and social acceptance of violence in child discipline. Key Practitioner Messages Under certain circumstances, Corporal Punishment (CP) is accepted and applied in Suriname, despite growing knowledge of its harmful consequences. While participants considered the lawfulness of behaviour, they were primarily interested in the intentions of the caretakers when using CP If these were interpreted ‘in the best interest of the child’, CP was not considered maltreatment. Understanding of what motivates caregivers to use CP helps in developing appropriate policy responses in order to prevent and respond to violence towards children.
Population-Based Studies on the Epidemiology of Insulin Resistance in Children

Background. In view of the alarming incidence of obesity in children, insight into the epidemiology of the prediabetic state insulin resistance (IR) seems important. Therefore, the aim of this systematic review was to give an overview of all population-based studies reporting on the prevalence and incidence rates of IR in childhood. Methods. PubMed, Embase, and Cochrane library were searched in order to find all available population-based studies describing the epidemiology of IR in pediatric populations. Prevalence rates together with methods and cut-off values used to determine IR were extracted and summarized with weight and sex specific prevalence rates of IR if available. Results. Eighteen population-based studies were identified, describing prevalence rates varying between 3.1 and 44%, partly explained by different definitions for IR. Overweight and obese children had higher prevalence rates than normal weight children. In seven out of thirteen studies reporting sex specific results, girls seemed to be more affected than boys. Conclusion. Prevalence rates of IR reported in children vary widely which is partly due to the variety of definitions used. Overweight and obese children had higher prevalence and girls were more insulin resistant than boys. Consensus on the definition for IR in children is needed to allow for comparisons between different studies.
Multi-Task Multiple Kernel Relationship Learning

This paper presents a novel multitask multiple kernel learning framework that efficiently learns the kernel weights leveraging the relationship across multiple tasks. The idea is to automatically infer this task relationship in the \textit{RKHS} space corresponding to the given base kernels. The problem is formulated as a regularization-based approach called \textit{Multi-Task Multiple Kernel Relationship Learning} (\textit{MK-MTRL}), which models the task relationship matrix from the weights learned from latent feature spaces of task-specific base kernels. Unlike in previous work, the proposed formulation allows one to incorporate prior knowledge for simultaneously learning several related tasks. We propose an alternating minimization algorithm to learn the model parameters, kernel weights and task relationship matrix. In order to tackle large-scale problems, we further propose a two-stage \textit{MK-MTRL} online learning algorithm and show that it significantly reduces the computational time, and also achieves performance comparable to that of the joint learning framework. Experimental results on benchmark datasets show that the proposed formulations outperform several state-of-the-art multitask learning methods.
Notes on Maxwell & Delaney 5.2 Planned vs. Post-hoc Comparisons

When conducting a statistical test, we typically set α = . 05 or α = . 01 so that the probability of making a Type I error is . 05 or . 01. Suppose, however, we conduct 100 such tests. Further suppose that the null hypothesis for each test is, in fact, true. Although α for each individual test may be . 05, the probability of making at least one Type I error across the entire set of 100 tests is much greater than . 05. Why? Because the more tests we do, the greater the chances of making an error. More precisely, the probability of making at least one Type I error is P (at least one Type I error) = α F W = 1 − (1 − α P C) C (1) where C is the number of tests performed 1. α P C is the per comparison Type I error rate; it represents the probability of making a Type I error for each test. α F W , on the other hand, is the familywise Type I error rate, and it represents the probability of making a Type I error across the entire family, or set, of tests. For the one-way designs we are considering, α F W also equals the α level for the entire experiment, or the experimentwise error rate (α EW). For the current example, α = . 05, C = 100, and so α F W = 0.994079, which means that it is very likely that we would make at least one Type I error in our set of 100 tests. Here, in a nutshell, is the problem of conducting multiple tests of group means: the probability of making a Type I error increases with the number of tests. If the number of tests, C, is large, then it becomes very likely that we will make a Type I error. When we are conducting multiple tests on group means, we generally want to minimize Type I errors across the entire experiment, and so we need some way of maintaining α EW at some reasonably low level (e.g., .05). One obvious way of controlling α EW is to rearrange Equation 1 to calculate the α P C that is required for a given α F W and C: α P C = 1 − (1 − α F W) According to Equation 2, when C = 100 and we want …
Analysis of biomedical EEG signals using Wavelet Transforms and Multifractal Analysis

Fractal Analysis is the well developed theory in the Non-linear Analysis of Biomedical Signals such as Electroencephalogram (EEG). EEG signal is essentially multi scale fractal, i.e. Multifractal. Therefore Multifractal measures such as Generalized Fractal Dimensions (GFD), could be a useful tool to compute the degree of disorders, complexity, irregularity and chaotic nature of the Biomedical Signals of the Epileptic patients. We organized a novel scheme for detecting epileptic seizures from EEG data recorded from Healthy subjects and Epileptic patients. The scheme was based on GFD and the Discrete Wavelet Transform (DWT) analysis of EEG signals. First EEG signals were decomposed into approximation and detail coefficients using DWT and then GFD values of the original EEGs, approximation and detail coefficients were computed. Significant differences were found between the GFD values of the Healthy and Epileptic EEGs showing us to detect seizures with high accuracy. Without DWT as preprocessing step, it was shown that the detection rate is very less. The proposed idea was demonstrated through the graphical and statistical tools. Hence we conclude that the Multifractal Analysis based on GFD and the Wavelet Decomposition through DWT are the strong detectors and indicators of the state of illness of the Epileptic Patients.
Multi-robot assignment algorithm for tasks with set precedence constraints

In this paper, we present task allocation (assignment) algorithms for a multi-robot system where the tasks are divided into disjoint groups and there are precedence constraints between the task groups. Existing auction-based algorithms assume the task independence and hence can not be used directly to solve the class of multi-robot task assignment problems that we consider. In our model, each robot can do a fixed number of tasks and obtains a benefit (or incurs a cost) for each task. The tasks are divided into groups and each robot can do only one task from each group. These constraints arise when the robots have to do a set of tasks that have precedence constraints and each task takes the same time to be completed. We extend the auction algorithm to provide an almost optimal solution to the task assignment problem with set precedence constraints (the theoretical guarantees are the same as that of the original auction algorithm for unconstrained tasks). In other words, we guarantee that we will get a solution within a factor of O(nte) of the optimal solution, where nt is the total number of tasks and ε is a parameter that we choose. We first present our algorithm using a shared memory model and then indicate how consensus algorithms can be used to make the algorithm totally distributed.
Towards multi-policy support for IaaS clouds to secure data sharing

Infrastructure as a service (IaaS) is a cloud service model that provides storage and computation services for users at a low price. A recent report from Gartner indicates that IaaS will be the fastest growing area among all of the cloud service models in the near future, and thus it is strongly envisioned that multiple companies will use IaaS clouds to share information among them. However, the current access control mechanisms in IaaS platforms do not have the ability to enable flexible data sharing among companies while addressing security problems such as information and privacy leaking. In this paper, we propose two IaaS cloud reference architectures that enforce cloud-level Chinese Wall security (CWS) policy to prevent information leaking among companies. The new architectures are also able to support customized domain level access control policies such as role-based access control (RBAC), privacy-preserving information retrieval, and single sign on (SSO). The reference architectures were implemented using Eucalyptus and its data storage service called Walrus; therefore, our approach can also be applied to commercial clouds like Amazon S3. The result of performance analysis has shown that our architectures are feasible, scalable, and efficient.
Survivable control plane establishment with live control service backup and migration in SD-EONs

It is known that software-defined elastic optical networks (SD-EONs) are programmable and application-aware. However, due to the centralized network control and management, SD-EONs are vulnerable to the network failures that can affect control plane operations. In this paper, we study the problem of survivable control plane establishment (SCPE), i.e., the controller placement and related communication planning for control service backup and migration to protect the control plane of an SD-EON against single node failures. We first propose a novel mutual backup model to improve the survivability of the control plane with high protection efficiency. An integer linear programming (ILP) model is then formulated to solve the SCPE problem with the objective to minimize both the number of deployed OpenFlow controllers (OF-Cs) and the average control channel latency. We also propose a time-efficient heuristic and use simulations to verify that it can obtain similar solutions to those of the ILP. On top of the theoretical investigation, we design and implement the system to facilitate live control service backup and migration with SCPE in an SD-EON control plane testbed. Experimental results demonstrate that the proposed scheme works efficiently, and compared with that using a single OF-C, our scheme achieves much shorter average provisioning latency in dynamic provisioning.
On an Algorithm for Nding All Interesting Sentences Extended Abstract

Knowledge discovery in databases (KDD), also called data mining, has recently received wide attention from practitioners and researchers. One of the basic problems in KDD is the following: given a data set r, a class L of sentences deening subgroups or properties of r, and an interestingness predicate , nd all sentences of L deemed interesting by the interestingness predicate. In this paper we analyze a simple and well-known levelwise algorithm for nding all such descriptions. We give bounds for the number of database accesses that the algorithm makes. We also consider the veriication problem of a KDD process: given r and a set of sentences T L, determine whether T is exactly the set of interesting statements about r. We show strong connections between the veriica-tion problem and the hypergraph transversal problem. The veriication problem arises in a natural way when using sampling to speed up the pattern discovery step in KDD.
Classical data compression with quantum side information

The problem of classical data compression when the decoder has quantum side information at his disposal is considered. This is a quantum generalization of the classical Slepian-Wolf theorem. The optimal compression rate is found to be reduced from the Shannon entropy of the source by the Holevo information between the source and side information.
Biclustering and classification analysis in gene expression using Nonnegative Matrix Factorization on multi-GPU systems

A great interest has been given to the Nonnegative Matrix Factorization (NMF) technique due to its ability of extracting highly-interpretable parts from data sets. Gene expression analysis is one of the most popular applications of NMF in Bioinformatics. Nonetheless, its usage is hindered by the computational complexity when processing large data sets. In this paper, we present two parallel implementations of NMF. The first version uses CUDA on a Graphics Processing Unit (GPU). Large input matrices are iteratively blockwise transferred and processed. The second implementation distributes data among multiple GPUs synchronized through MPI (Message Passing Interface). When analyzing large data sets with two and four GPUs, it performs respectively, 2.3 and 4.13 times faster than the single-GPU version. This represents about 120 times faster than a conventional CPU. These super linear speedups are achieved when data portions assigned to each GPU are small enough to be transferred only once.
Lossy Conservative Update sketch ∗

Count-Min (CM) sketch [2] is a compact summary data structure to store the frequencies of all items in the input stream. It uses hashing to map frequency of an item onto a small sketch without explicitly storing the item itself. CM sketch is a two-dimensional array with width w and depth d. The user chosen parameters ε and δ determines the width and depth of the two-dimensional array: w= 2 ε and d=log( 1 δ ). ε controls the amount of tolerable error in the returned count and δ controls the probability with which the returned count is not within this acceptable error. The depth d denotes the number of pairwise-independent hash functions. Each of these hash functions hk:{x1 . xN} → {1 . w}, 1 ≤ k ≤ d, takes an item from the input stream and maps it into a counter indexed by the corresponding hash function. For example, h2(x) = 5 indicates that the item “x” is mapped to the 5 position in the second row of the sketch. The space used is O(wd) that is sub-linear in the size of input. Update Procedure: When a new item “x” with count c, the sketch is updated by: ∀1≤ k ≤ d sketch[k,hk(x)]← sketch[k,hk(x)]+ c
DDoS attack detection based on neural network

DDoS attack is a major Internet security problem-DoS is that lots of clients simultaneously send service requests to certain server on the internet such that this server is too busy to provide normal services for others. Attackers using legitimate packets and often changing package information, so that traditional detection methods based on feature descriptions is difficult to detect it. This paper present an artificial intelligence DDoS attack detection method based on neural networks. In this method, analysis of server resources and network traffic, To training the ability of detection normal or abnormal, it have better results for detect DDoS attack.