Miracle-dz/UniFAR-FTUs huggingface dataset:
Private Q-Learning with Functional Noise in Continuous Spaces

We consider privacy-preserving algorithms for deep reinforcement learning. State-of-the-art methods that guarantee differential privacy are not extendable to very large state spaces because the noise level necessary to ensure privacy would scale to infinity. We address the problem of providing differential privacy in Q-learning where a function approximation through a neural network is used for parametrization. We develop a rigorous and efficient algorithm by inspecting the reproducing kernel Hilbert space in which the neural network is embedded. Our approach uses functional noise to guarantee privacy, while the noise level scales linearly with the complexity of the neural network architecture. There are no known theoretical guarantees on the performance of deep reinforcement learning, but we gain some insight by providing a utility analysis under the discrete space setting.
Generalized Tensor Models for Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are very successful at solving challenging problems with sequential data. However, this observed efficiency is not yet entirely explained by theory. It is known that a certain class of multiplicative RNNs enjoys the property of depth efficiency --- a shallow network of exponentially large width is necessary to realize the same score function as computed by such an RNN. Such networks, however, are not very often applied to real life tasks. In this work, we attempt to reduce the gap between theory and practice by extending the theoretical analysis to RNNs which employ various nonlinearities, such as Rectified Linear Unit (ReLU), and show that they also benefit from properties of universality and depth efficiency. Our theoretical results are verified by a series of extensive computational experiments.
Design of Polar Codes for Parallel Channels with an Average Power Constraint

Polar codes are designed for parallel binary-input additive white Gaussian noise (BiAWGN) channels with an average power constraint. The two main design choices are: the mapping between codeword bits and channels of different quality, and the power allocation under the average power constraint. Information theory suggests to allocate power such that the sum of mutual information (MI) terms is maximized. However, a power allocation specific to polar codes shows significant gains.
Human-Centric Light Sensing and Estimation From RGBD Images: The Invisible Light Switch

Lighting design in indoor environments is of primary importance for at least two reasons: 1) people should perceive an adequate light; 2) an effective lighting design means consistent energy saving. We present the Invisible Light Switch (ILS) to address both aspects. ILS dynamically adjusts the room illumination level to save energy while maintaining constant the light level perception of the users. So the energy saving is invisible to them. Our proposed ILS leverages a radiosity model to estimate the light level which is perceived by a person within an indoor environment, taking into account the person position and her/his viewing frustum (head pose). ILS may therefore dim those luminaires, which are not seen by the user, resulting in an effective energy saving, especially in large open offices (where light may otherwise be ON everywhere for a single person). To quantify the system performance, we have collected a new dataset where people wear luxmeter devices while working in office rooms. The luxmeters measure the amount of light (in Lux) reaching the people gaze, which we consider a proxy to their illumination level perception. Our initial results are promising: in a room with 8 LED luminaires, the energy consumption in a day may be reduced from 18585 to 6206 watts with ILS (currently needing 1560 watts for operations). While doing so, the drop in perceived lighting decreases by just 200 lux, a value considered negligible when the original illumination level is above 1200 lux, as is normally the case in offices.
Decentralized Online Learning: Take Benefits from Others’ Data without Sharing Your Own to Track Global Trend

Decentralized online learning (online learning in decentralized networks) has been attracting more and more attention, since it is believed that decentralized online learning can help data providers cooperatively better solve their online problems without sharing their private data to a third party or other providers. Typically, the cooperation is achieved by letting the data providers exchange their models between neighbors, e.g., recommendation model. However, the best regret bound for a decentralized online learning algorithm is 𝒪(n√T), where n is the number of nodes (or users) and T is the number of iterations. This is clearly insignificant, since this bound can be achieved without any communication in the networks. This reminds us to ask a fundamental question: Can people really get benefit from the decentralized online learning by exchanging information? In this article, we studied when and why the communication can help the decentralized online learning to reduce the regret. Specifically, each loss function is characterized by two components: the adversarial component and the stochastic component. Under this characterization, we show that decentralized online gradient enjoys a regret bound \( {\mathcal {O}(\sqrt {n^2TG^2 + n T \sigma ^2})} \) , where G measures the magnitude of the adversarial component in the private data (or equivalently the local loss function) and σ measures the randomness within the private data. This regret suggests that people can get benefits from the randomness in the private data by exchanging private information. Another important contribution of this article is to consider the dynamic regret—a more practical regret to track users’ interest dynamics. Empirical studies are also conducted to validate our analysis.
Non Binary Polar Codes with Equidistant Transform for Transmission over the AWGN Channel

Finding fast polarizing transforms is an important problem as polar codes suffer from slow finitelength performance. This paper considers non binary polar codes for transmission over the AWGN channel and designs polarizing transforms with better distance characteristics using a simple procedure for signal sets. The main idea of the paper is to define Equidistant Polarizing Transforms and show that they achieve the best distance spectrum bound. We provides an example of such transform for q = 5. In this case PSK-type signal set is used. Finally, we show performance gains and some comparison with other methods.
Classifier Suites for Insider Threat Detection

Better methods to detect insider threats need new anticipatory analytics to capture risky behavior prior to losing data. In search of the best overall classifier, this work empirically scores 88 machine learning algorithms in 16 major families. We extract risk features from the large CERT dataset, which blends real network behavior with individual threat narratives. We discover the predictive importance of measuring employee sentiment. Among major classifier families tested on CERT, the random forest algorithms offer the best choice, with different implementations scoring over 98% accurate. In contrast to more obscure or black-box alternatives, random forests are ensembles of many decision trees and thus offer a deep but human-readable set of detection rules (>2000 rules). We address performance rankings by penalizing long execution times against higher median accuracies using cross-fold validation. We address the relative rarity of threats as a case of low signal-to-noise (< 0.02% malicious to benign activities), and then train on both under-sampled and over-sampled data which is statistically balanced to identify nefarious actors.
Short-term Demand Forecasting for Online Car-hailing Services Using Recurrent Neural Networks

ABSTRACT Short-term traffic flow prediction is one of the crucial issues in intelligent transportation system, which is an important part of smart cities. Accurate predictions can enable both the drivers and the passengers to make better decisions about their travel route, departure time, and travel origin selection, which can be helpful in traffic management. Multiple models and algorithms based on time-series prediction and machine learning were applied to this issue and achieved acceptable results. Recently, the availability of sufficient data and computational power motivates us to improve the prediction accuracy via deep-learning approaches. Recurrent neural networks have become one of the most popular methods for time-series forecasting; however, due to the variety of these networks, the question that which type is the most appropriate one for this task remains unsolved. In this paper, we use three kinds of recurrent neural networks including simple RNN units, GRU, and LSTM neural network to predict short-term traffic flow. The dataset from TAP30 Corporation is used for building the models and comparing RNNs with several well-known models, such as DEMA, LASSO, and XGBoost. The results show that all three types of RNNs outperform the others; however, more simple RNNs such as simple recurrent units and GRU perform work better than LSTM in terms of accuracy and training time.
Reference-less Quality Estimation of Text Simplification Systems

The evaluation of text simplification (TS) systems remains an open challenge. As the task has common points with machine translation (MT), TS is often evaluated using MT metrics such as BLEU. However, such metrics require high quality reference data, which is rarely available for TS. TS has the advantage over MT of being a monolingual task, which allows for direct comparisons to be made between the simplified text and its original version. In this paper, we compare multiple approaches to reference-less quality estimation of sentence-level text simplification systems, based on the dataset used for the QATS 2016 shared task. We distinguish three different dimensions: gram-maticality, meaning preservation and simplicity. We show that n-gram-based MT metrics such as BLEU and METEOR correlate the most with human judgment of grammaticality and meaning preservation, whereas simplicity is best evaluated by basic length-based metrics.
Using score distributions to compare statistical significance tests for information retrieval evaluation

Statistical significance tests can provide evidence that the observed difference in performance between 2 methods is not due to chance. In information retrieval (IR), some studies have examined the validity and suitability of such tests for comparing search systems. We argue here that current methods for assessing the reliability of statistical tests suffer from some methodological weaknesses, and we propose a novel way to study significance tests for retrieval evaluation. Using Score Distributions, we model the output of multiple search systems, produce simulated search results from such models, and compare them using various significance tests. A key strength of this approach is that we assess statistical tests under perfect knowledge about the truth or falseness of the null hypothesis. This new method for studying the power of significance tests in IR evaluation is formal and innovative. Following this type of analysis, we found that both the sign test and Wilcoxon signed test have more power than the permutation test and the t‐test. The sign test and Wilcoxon signed test also have good behavior in terms of type I errors. The bootstrap test shows few type I errors, but it has less power than the other methods tested.
Generalized Coordinated Multipoint (GCoMP)-Enabled NOMA: Outage, Capacity, and Power Allocation

A novel generalized coordinated multi-point transmission (GCoMP)-enabled non-orthogonal multiple access (NOMA) scheme is proposed. In particular, distributed base stations (BSs) in a network coverage area cooperate on the downlink to serve a set of user equipments (UEs) using the same transmission frequency band. Furthermore, all UEs associated to a BS and using a particular frequency band forms a single NOMA cluster. The number of BSs serving a UE in a particular frequency band is referred to as the order of clustering (or order of BS cooperation). To evaluate the proposed scheme, we derive a closed-form expression for the probability of outage for a UE with different orders of BS cooperation. To obtain important insights on the performance of the proposed system, approximate (asymptotic) expressions for the probability of outage and outage capacity are derived considering both perfect and imperfect channel state information (CSI) estimation. We observe that improved spectral efficiency with a large number of UEs per NOMA cluster can be achieved by increasing the clustering order (i.e., number of cooperating BSs per UE). Furthermore, an optimal transmission power allocation scheme that jointly allocates transmission power fractions from all cooperating BSs to all connected UEs is developed.
Multiobject Tracking in Videos Based on LSTM and Deep Reinforcement Learning

Multiple-object tracking is a challenging issue in the computer vision community. In this paper, we propose a multiobject tracking algorithm in videos based on long short-term memory (LSTM) and deep reinforcement learning. Firstly, the multiple objects are detected by the object detector YOLO V2. Secondly, the problem of single-object tracking is considered as a Markov decision process (MDP) since this setting provides a formal strategy to model an agent that makes sequence decisions. The single-object tracker is composed of a network that includes a CNN followed by an LSTM unit. Each tracker, regarded as an agent, is trained by utilizing deep reinforcement learning. Finally, we conduct a data association using LSTM for each frame between the results of the object detector and the results of single-object trackers. From the experimental results, we can see that our tracker achieves better performance than the other state-of-the-art methods. Multiple targets can be steadily tracked even when frequent occlusions, similar appearances, and scale changes happened.
Analysis of Contention-based relay selection mechanisms in autonomous multi-hop networks

A fundamental question of interest in multi-hop networks is whether routing should be done in a smaller number of longer hops, or larger number of shorter hops. However, underneath these aspects of the routing problem itself is the number of contending nodes involved in the relay selection taking place at each hop so as to realize whichever routing strategy adopted. A larger number of nodes increases the likelihood of finding an adequate relay, whereas increasing the selection overhead, and vice-versa. In this article we investigate both the expected cost (in time consumed) incurred by the relay selection strategy, and the effectiveness of the forwarding strategy (in spatial advancements) as a function of the number of the contending nodes, when using Contention-based Geographic Forwarding (CGF) strategies to relay packets in multi-hop scenarios. In particular, Probability Generating Functions (PGFs) are utilized to compute the distribution of Contention Resolution Intervals (CRIs) and quantify overhead, while stochastic geometry is utilized to model topological aspects of the network and quantify expected progress. Both a totally random and an auction-based Relay Selection Algorithms (RSAs) are studied and compared, with the advantages of the auction-based approach established analytically. From an overhead point of view, the selection of the furthest and the nearest relays are equivalent, but our results indicate that the long-hop routing approach is less sensitive to the number of contending nodes than the short-hop alternative.
An Imaging Survey for Extrasolar Planets around 45 Close, Young Stars with the Simultaneous Differential Imager at the Very Large Telescope and MMT

We present the results of a survey of 45 young (≲250 Myr), close (≲50 pc) stars with the Simultaneous Differential Imager (SDI) implemented at the VLT and the MMT for the direct detection of extrasolar planets. As part of the survey, we observed 54 objects, consisting of 45 close, young stars; two more distant (<150 pc), extremely young (≤10 Myr) stars; three stars with known radial velocity planets; and four older, very nearby (≤20 pc) solar analogs. Our SDI devices use a double Wollaston prism and a quad filter to take images simultaneously at three wavelengths surrounding the 1.62 μm methane absorption bandhead found in the spectrum of cool brown dwarfs and gas giant planets. By differencing adaptive optics-corrected images in these filters, speckle noise from the primary star is significantly attenuated, resulting in photon (and flat-field)-noise-limited data. In our VLT data, we achieved H-band contrasts ≳ 10 mag (5 σ) at a separation of 0.5'' from the primary star on 45% of our targets and H-band contrasts ≳ 9 mag at a separation of 0.5'' on 80% of our targets. With these contrasts, we can image (5 σ detection) a 7 MJ planet 15 AU from a 70 Myr K1 star at 15 pc or a 7.8 MJ planet at 2 AU from a 12 Myr M star at 10 pc. We detected no candidates with S/N > 2 σ which behaved consistently like a real object. From our survey null result, we can rule out (with 93% confidence) a model planet population where N(a) ∝ constant out to a distance of 45 AU.
WORK MOTIVATION AND PSYCHOLOGICAL WELL-BEING OF VOLUNTEERS AND NON-VOLUNTEERS

ABSTRACT This study aims to describe similarities and differences in the motivational profile and psychological well-being between two groups of 94 paid employees each, one of them are volunteering fire fighters, the others are non-volunteers. The scales of Motivation at Work (Gagne, et al., 2010, 2012) and Psychological Well-being (Ryff, 1989) were used. Results have shown that the non-volunteers scored significantly higher for external behavioral regulation/motivation and introjected motivation. Identified and intrinsic motivation was significantly higher in the group of volunteers. If behavior regulators are categorized in autonomous and controlled motivation, it is clear that in both groups autonomous work motivation is significantly higher than controlled motivation. As expected, based on Self-determination theory (Deci & Ryan, 2000), volunteers had a significantly higher score for autonomous motivation and a significantly lower score for controlled motivation. Although well-being correlated positively with autonomous motivation, no significant mean differences were found between the two groups. Key words: Psychological Well-being, Motivation, Volunteering
Recent progress in the boundary control method

The review covers the period 1997–2007 of development of the boundary control method, which is an approach to inverse problems based on their relations to control theory (Belishev 1986). The method solves the problems on unknown manifolds: given inverse data of a dynamical system associated with a manifold it recovers the manifold, the operator governing the system and the states of the system defined on the manifold. The main subject of the review is the extension of the boundary control method to the inverse problems of electrodynamics, elasticity theory, impedance tomography, problems on graphs as well as some new relations of the method to functional analysis and topology.
Dynamic Analysis of a Layered Cylinder Reinforced by Functionally Graded Carbon Nanotubes Distributions Subjected to Shock Loading using MLPG Method

In this paper, the meshless local Petrov-Galerkin (MLPG) method is exploited for dynamic analysis of functionally graded nanocomposite cylindrical layered structure reinforced by carbon nanotube subjected to mechanical shock loading. The carbon nanotubes (CNTs) are distributed across radial direction on thickness of cylinder, which can be simulated by linear and nonlinear volume fraction. Free vibration and elastic wave propagation are studied for various value of volume fraction exponent at various time intervals. The layered cylinder is assumed to be under axisymmetric and plane strain conditions. Four types of CNTs distributions including uniform and three kinds of functionally graded distributions along the radial of cylinder are considered. Material properties are simulated by a micro mechanical model. In the MLPG analysis, radial basis function (RBF) is used for approximation of displacement field in the weak form of governing equations and Heaviside function is used as test Function. For time domain analysis, the Newmark finite difference method is used. Effects of various distributions of carbon nanotubes on the propagation of elastic wave are illustrated and studied in details. The results obtained by the present analysis are compared and validated with those obtained by FEM, which were reported in previous published literatures.
PLAYING WITH DIVERSITY AND CONFORMITY IN DESIGN-ORIENTATED PROJECT MANAGEMENT EDUCATION

Teaching in higher education has been facing a series of challenges, both in terms of the nature of the demands and dynamics of the market, and also with respect to the continuous expansion and diversification of student populations. Thus, universities have been developing their own combined teaching strategies in order to teach contents. Instead of focusing only on rote learning, which is characteristic of conformity education, diversity education has emphasised the importance of reflection and individual inquiry, as well as problem-solving, collaborative learning and mastery of key concepts. In this contemporary educational scenario, feedback is considered as a powerful educational tool for promotion of student engagement and deep learning. Feedback can contribute to the improvement of both students and teachers, because it enables processes of reflection which lead to a better understanding of the content and provide valuable information to assist in the design of clearer guidelines. This study aims to demonstrate the importance of using feedback as a catalyst for combined practices for design-oriented project management education. Through the analysis of the case of a project management course offered to undergraduate students of design in a Brazilian university, it was found that the use of various feedback mechanisms, as a means of improving the course, can facilitate the combination of the conformity and diversity teaching strategies.