Miracle-dz/UniFAR-FTUs huggingface dataset:
NEAT: A NEighbor AssisTed spectrum decision protocol for resilience against PUEA

In this paper, we propose a distributed spectrum decision protocol resilient to primary user emulation attacks (PUEA) in dynamic spectrum access (DSA) networks. PUEA is a type of denial-of-service attack where malicious users can deprive legitimate secondary users (SUs) of spectrum access by mimicking the primary transmitter. Most studies in the literature discuss how each individual SU can develop mechanisms to detect PUEA. We developed the first centralized protocol to help SUs fuse their individual detection results with the help of a centralized controller, to better mitigate PUEA. Here, we develop a distributed spectrum decision protocol in which SUs make individual spectrum decisions and then exchange individual sensing results with their one-hop neighbors to increase resilience to PUEA. We call this protocol, NEAT: NEighbor AssisTed spectrum decision protocol. We also present a detailed analysis of the protocol in terms of the probability of successful PUEA under Byzantine attacks, where the malicious users can lie about their individual sensing results. We show that with negligible communication overhead, the proposed protocol reduces the probability of successful PUEA by up to three orders of magnitude in the presence of Byzantine attacks. To the best of our knowledge, this is the first distributed protocol proposed to mitigate PUEA.
Multi-label Learning via Structured Decomposition and Group Sparsity

In multi-label learning, each sample is associated with several labels. Existing works indicate that exploring correlations between labels improve the prediction performance. However, embedding the label correlations into the train- ing process significantly increases the problem size. Moreo ver, the mapping of the label structure in the feature space is not clear. In this paper, we propose a novel multi-label learning method "Structured Decomposition + Group Sparsity (SDGS)". In SDGS, we learn a feature subspace for each label from the structured decomposition of the training data, and predict the labels of a new sample from its group sparse representation on the multi-subspace obtained from the struc- tured decomposition. In particular, in the training stage, we decompose the data matrix X2 R np as X = P k=1 L i +S, wherein the rows of L i associated with samples that belong to label i are nonzero and consist a low-rank matrix, while the other rows are all-zeros, the residual S is a sparse matrix. The row space of Li is the feature subspace corresponding to label i. This decomposition can be efficiently obtained via randomized optimization. In the pr ediction stage, we es- timate the group sparse representation of a new sample on the multi-subspace via group lasso. The nonzero representation coefficients tend to concentra te on the subspaces of labels that the sample belongs to, and thus an effective prediction can be obtained. We evaluate SDGS on several real datasets and compare it with popular methods. Results verify the effectiveness and effic iency of SDGS.
FastSLAM 2.0: Least-Squares Approach

In this paper, we present a set of robust and efficient algorithms with O(N) cost for the following situations: object detection with a laser ranger; mobile robot pose estimation and a FastSLAM improved implementation. Objected detection is mainly based on a novel multiple line fitting method, related with walls at the environment. This method assumes that walls at the environment constitute a regular constrained angles. A line-based pose estimation method is also proposed, based on least-squares (LS). This method performs the matching of detected lines and estimated map lines and it can provide the global pose estimation under assumption of known data-association. FastSLAM 1.0 has been improved by considering the estimated pose with the LS-approach to re-allocate each particle of the posterior distribution. This approach has a lower computational cost than EKF approach in FastSLAM 2.0. The three algorithms have been combined in order to perform an efficient self-localization and map building process, tested for indoor environments with real data. And results show that the ideas proposed in this paper could aim in closing the loop and also to improve the overall estimation performance
Young People in Residential Care, their Participation and the Influencing Factors

ABSTRACT In 1992, Ireland committed to the right of children to participate in decision-making that impacts on their lives. The extent to which this right is upheld for young people in the care of the State who live in residential childcare centres is unknown. A small qualitative case study was carried out in four such centres in the West of Ireland in 2012 which aimed to establish young people's level of participation and the factors which influence it. The findings show that they were relatively satisfied with decision-making in everyday matters though this was fraught with difficulties because of control battles. They had limited involvement in decisions about admission to residential care and the process of admission, family access, education/training and their future plans. These young people were unfamiliar with their right to participate and the majority of social care workers in the study had not received training on participation. Factors were identified as enabling participation or creating obstacles to it. They include the value placed on the voice of the child, the capacity of young people to participate, their access to effective advocates and the nature of the available participatory spaces. The knowledge, skills, values and personalities of relevant professionals were key determinants of youth participation.
Learning Bilingual Sentiment Word Embeddings for Cross-language Sentiment Classification

The sentiment classiﬁcation performance relies on high-quality sentiment resources. However, these resources are imbalanced in different languages. Cross-language sentiment classiﬁcation (CLSC) can leverage the rich resources in one language (source language) for sentiment classiﬁcation in a resource-scarce language (target language). Bilingual embeddings could eliminate the semantic gap between two languages for CLSC, but ignore the sentiment information of text. This paper proposes an approach to learning bilingual sentiment word embeddings (BSWE) for English-Chinese CLSC. The proposed B-SWE incorporate sentiment information of text into bilingual embeddings. Furthermore, we can learn high-quality BSWE by simply employing labeled corpora and their translations, without relying on large-scale parallel corpora. Experiments on NLP&CC 2013 CLSC dataset show that our approach outperforms the state-of-the-art systems.
Using multipath priority routing for solar powered wireless sensor networks

Constant growth in the wireless sensor network (WSN) needs routing protocol which can cater for renewable energy sources as compared to conventionally used non-renewable energy sources in sensor nodes. APPOLO algorithm is used to periodically and locally determine the topological knowledge range of each solar-powered node, based on an estimated energy budget for the following period. Energy budget includes current energy, the predicted energy consumption and the energy expected from the solar cell. PISA is a QoS aware routing used for route selection based on objective. Priority based routing in solar powered wireless sensor network is implemented using APOLLO and PISA. Thus energy saving is attained for real-time route discovery. In this paper, multipath non-redundant data dissemination approach is used for the priority based routing scheme to further optimize use of energy in wireless sensor network.
Real-Time Robot Trajectory Estimation and 3D Map Construction using 3D Camera

Our research objective is simultaneous localization and mapping (SLAM) in rubble environment. The map construction requires estimation of robot trajectory in 3D space. However, it is hard to estimate it by using odometry or gyro in rubble. In this paper, the authors proposed real-time SLAM based on 3D scan match. 3D camera is used for measurement of 3D shape and its texture in real-time. 3D map and robot trajectory are estimated by combining these 3D scan data. ICP algorithm is used for the matching method. The authors modified ICP algorithm as fast and robust one for real-time 3D map construction
Cooperative Multi-Agent Reinforcement Learning for Multi-Component Robotic Systems: guidelines for future research

Reinforcement Learning (RL) as a paradigm aims to develop algorithms that allow to train an agent to optimally achieve a goal with minimal feedback information about the desired behavior, which is not precisely specified. Scalar rewards are returned to the agent as response to its actions endorsing or opposing them. RL algorithms have been successfully applied to robot control design. The extension of the RL paradigm to cope with the design of control systems for Multi-Component Robotic Systems (MCRS) poses new challenges, mainly related to coping with scaling up of complexity due to the exponential state space growth, coordination issues, and the propagation of rewards among agents. In this paper, we identify the main issues which offer opportunities to develop innovative solutions towards fully-scalable cooperative multi-agent systems.
The Effects of Parental Divorce in Single and Romantically Involved Emerging Adults

ABSTRACT This study, based on a sample of 572 individuals, verified the impact of parental divorce on beliefs toward marriage and divorce, expectations of future relationship success, and marital intentions among single and romantically involved emerging adults. Findings revealed that, in comparison with singles from divorced families, singles from intact families were more likely to report positive views of marriage and to believe that divorce is avoidable. Conversely, beliefs toward marriage and divorce of romantically involved individuals were similar, whether these latter grew up in intact or nonintact families. Finally, for all emerging adults, relational beliefs were associated with relationship expectations and marital intentions.
Limits on the Benefits of Energy Storage for Renewable Integration

The high variability of renewable energy resources presents significant challenges to the operation of the electric power grid. Conventional generators can be used to mitigate this variability but are costly to operate and produce carbon emissions. Energy storage provides a more environmentally friendly alternative, but is costly to deploy in large amounts. This paper studies the limits on the benefits of energy storage to renewable energy: How effective is storage at mitigating the adverse effects of renewable energy variability? How much storage is needed? What are the optimal control policies for operating storage? To provide answers to these questions, we first formulate the power flow in a single-bus power system with storage as an infinite horizon stochastic program. We find the optimal policies for arbitrary net renewable generation process when the cost function is the average conventional generation (environmental cost) and when it is the average loss of load probability (reliability cost). We obtain more refined results by considering the multi-timescale operation of the power system. We view the power flow in each timescale as the superposition of a predicted (deterministic) component and an prediction error (residual) component and formulate the residual power flow problem as an infinite horizon dynamic program. Assuming that the net generation prediction error is an IID process, we quantify the asymptotic benefits of storage. With the additional assumption of Laplace distributed prediction error, we obtain closed form expressions for the stationary distribution of storage and conventional generation. Finally, we propose a two-threshold policy that trades off conventional generation saving with loss of load probability. We illustrate our results and corroborate the IID and Laplace assumptions numerically using datasets from CAISO and NREL.
Direct determination of neutrino mass parameters at future colliders

If the observed light neutrino masses are induced by their Yukawa couplings to singlet right-handed neutrinos, the natural smallness of those makes direct collider tests of the electroweak scale neutrino mass mechanisms difficult in the simplest models. In the triplet Higgs seesaw scenario the smallness of light neutrino masses may come from the smallness of B - L breaking parameters, allowing sizable Yukawa couplings even for a TeV scale triplet. We show that, in this scenario, measuring the branching fractions of doubly charged Higgs to different same-charged lepton flavors at CERN LHC and/or ILC experiments will allow one to measure the neutrino mass parameters that neutrino oscillation experiments are insensitive to, including the neutrino mass hierarchy, lightest neutrino mass, and Majorana phases.
A note on dichotomies for metric transforms

We show that for every nondecreasing concave function w:R+ --> R+ with w(0)=0, either every finite metric space embeds with distortion arbitrarily close to 1 into a metric space of the form (X,w o d) for some metric d on X, or there exists a=a(w)>0 and n_0=n_0(w)\in N such that for all n>n_0, any embedding of {0,. ,n} into a metric space of the form (X,w o d) incurs distortion at least n^a.
V440 Per: the longest-period overtone Cepheid

V440 Per is a Population I Cepheid with a period of 7.57 d and low-amplitude, almost sinusoidal light and radial velocity curves. With no reliable data on the first harmonic, its pulsation mode identification remained controversial. We obtained a radial velocity curve of V440 Per with our new high-precision and high-throughput Poznan Spectroscopic Telescope. Our data reach an accuracy of 130 m s ―1 per individual measurement and yield a secure detection of the first harmonic with an amplitude of A 2 = 140 ± 15 m s ―1 . The velocity Fourier phase φ 21 of V440 Per is inconsistent at the 7. 25σ level with those of fundamental-mode Cepheids, implying that the star must be an overtone Cepheid, as originally proposed by Kienzle et al. Thus, V440 Per becomes the longest-period Cepheid with securely established overtone pulsations. We show that a convective non-linear pulsation hydrocode can reproduce the Fourier parameters of V440 Per very well. The requirement to match the observed properties of V440 Per constrains the free parameters of the dynamical convection model used in the pulsation calculations, in particular the radiative loss parameter.
Exports and Financial Shocks

A striking feature of many financial crises is the collapse of exports relative to output. In the 2008 financial crisis, real world exports plunged 17 percent while GDP fell 5 percent. This paper examines whether deteriorations in bank health can help explain the large drops in exports relative to output in the recent crisis. Our paper is the first to establish a causal link between the health of banks providing trade finance and growth in a firm's exports relative to its domestic sales. We overcome measurement and endogeneity issues by using a unique data set, covering the Japanese financial crises from 1990 through 2010, which enables us to match exporters with the main bank that provides them with trade finance. Our point estimates are economically and statistically significant, suggesting that the health of financial institutions is an important determinant of firm-level exports during crises.
Steganalysis of LSB Embedded Images Using Gray Level Co-Occurrence Matrix

This paper proposes a steganalysis technique for both grayscale and color images. It uses the feature vectors derived from gray level co-occurrence matrix (GLCM) in spatial domain, which is sensitive to data embedding process. This GLCM matrix is derived from an image. Several combinations of diagonal elements of GLCM are considered as features. There is difference between the features of stego and non-stego images and this characteristic is used for steganalysis. Distance measures like Absolute distance and Euclidean distance are used for classification. Experimental results demonstrate that the proposed scheme outperforms the existing steganalysis techniques in attacking LSB steganographic schemes applied to spatial domain.
Transfer Learning of Structured Representation for Face Recognition

Face recognition under uncontrolled conditions, e.g., complex backgrounds and variable resolutions, is still challenging in image processing and computer vision. Although many methods have been proved well-performed in the controlled settings, they are usually of weak generality across different data sets. Meanwhile, several properties of the source domain, such as background and the size of subjects, play an important role in determining the final classification results. A transferrable representation learning model is proposed in this paper to enhance the recognition performance. To deeply exploit the discriminant information from the source domain and the target domain, the bioinspired face representation is modeled as structured and approximately stable characterization for the commonality between different domains. The method outputs a grouped boost of the features, and presents a reasonable manner for highlighting and sharing discriminant orientations and scales. Notice that the method can be viewed as a framework, since other feature generation operators and classification metrics can be embedded therein, and then, it can be applied to more general problems, such as low-resolution face recognition, object detection and categorization, and so forth. Experiments on the benchmark databases, including uncontrolled Face Recognition Grand Challenge v2.0 and Labeled Faces in the Wild show the efficacy of the proposed transfer learning algorithm.
A new symmetry detection approach in IC layouts

This paper presents a robust methodology for symmetry detection algorithm for integrated circuits (IC) layout designs. Approaches used to detect IC layout symmetry depend on extracting information from the circuit design. A new approach is presented to detect IC layout symmetry between polygons using computer vision. The approach is based on matching between extracted features from the IC layout design as an image. This approach detects translation, scale, rotation and partial symmetries in the IC layout design. In comparison to famous symmetry detection algorithms like SIFT, the new approach succeeds to detect symmetric polygons with higher speed and more accurate results.
Optimizing Dialogue Strategy Learning Using Learning Automata

Modeling the behavior of the dialogue management in the design of a spoken dialogue system using statistical methodologies is currently a growing research area. This paper presents a work on developing an adaptive learning approach to optimize dialogue strategy. At the core of our system is a method formalizing dialogue management as a sequential decision making under uncertainty whose underlying probabilistic structure has a Markov Chain. Researchers have mostly focused on model-free algorithms for automating the design of dialogue management using machine learning techniques such as reinforcement learning. But in model-free algorithms there exist a dilemma in engaging the type of exploration versus exploitation. Hence we present a model-based online policy learning algorithm using interconnected learning automata for optimizing dialogue strategy. The proposed algorithm is capable of deriving an optimal policy that prescribes what action should be taken in various states of conversation so as to maximize the expected total reward to attain the goal and incorporates good exploration and exploitation in its updates to improve the naturalness of humancomputer interaction. We test the proposed approach using the most sophisticated evaluation framework PARADISE for accessing to the railway information system. Keywords—Dialogue management, Learning automata, Reinforcement learning, Spoken dialogue system
Item-based Hybrid Recommender System for newly marketed pharmaceutical drugs

Recommender systems are Information systems that predict user preferences and present product/item/service recommendations that are personalized and subjective. These systems have been extant in the field of e-commerce and research extensively. Our work intends to introduce such a perceptive system to the field of healthcare. New drugs and their variants enter the market quite so often and keeping track of each of them is a tedious task. Hence, the usage of such new drugs is skewed. Our system aims at insightfully recommending the new drugs, thereby enlightening the medical community on the newest introductions to the market. The working flow follows the ensuing steps: Gather information on every drug that forays into the market based on criteria such as its generic name, brand name and the purpose it serves and also gather user information through a sign-up survey form; Employing item-based Top-N recommendation algorithm to compare and contrast search histories of users with a common background and determining item-item similarities based on product features; ultimately Top-N recommendations are then presented to the user.
Web-based and combined language models: a case study on noun compound identification

This paper looks at the web as a corpus and at the effects of using web counts to model language, particularly when we consider them as a domain-specific versus a general-purpose resource. We first compare three vocabularies that were ranked according to frequencies drawn from general-purpose, specialised and web corpora. Then, we look at methods to combine heterogeneous corpora and evaluate the individual and combined counts in the automatic extraction of noun compounds from English general-purpose and specialised texts. Better n-gram counts can help improve the performance of empirical NLP systems that rely on n-gram language models.
Bias and Controversy in Evaluation Systems

Evaluation is prevalent in real life. With the advent of Web 2.0, online evaluation has become an important feature in many applications that involve information (e.g., video, photo, and audio) sharing and social networking (e.g., blogging). In these evaluation settings, a set of reviewers assign scores to a set of objects. As part of the evaluation analysis, we want to obtain fair reviews for all the given objects. However, the reality is that reviewers may deviate in their scores assigned to the same object, due to the potential "bias" of reviewers or "controversy" of objects. The statistical approach of averaging deviations to determine bias and controversy assumes that all reviewers and objects should be given equal weight. In this paper, we look beyond this assumption and propose an approach based on the following observations: 1) evaluation is "subjective," as reviewers and objects have varying bias and controversy, respectively, and 2) bias and controversy are mutually dependent. These observations underlie our proposed reinforcement-based model to determine bias and controversy simultaneously. Our approach also quantifies "evidence," which reveals the degree of confidence with which bias and controversy have been derived. This model is shown to be effective by experiments on real-life and synthetic data sets.