Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical systematical study of atmospheric effects on Earth-Space QKD

In this paper, the authors compare the security bounds for different quantum communication protocols with the numerically evaluated losses in the transmission channel, due to the interaction between the atmosphere and the photon, that is the information carrier. The analysis is carried out using a free-source library which can solve the radiative transfer equation for a parallel plane atmosphere. Laser Physics
Federated Search

Federated search (federated information retrieval or distributed information retrieval) is a technique for searching multiple text collections simultaneously. Queries are submitted to a subset of collections that are most likely to return relevant answers. The results returned by selected collections are integrated and merged into a single list. Federated search is preferred over centralized search alternatives in many environments. For example, commercial search engines such as Google cannot easily index uncrawlable hidden web collections while federated search systems can search the contents of hidden web collections without crawling. In enterprise environments, where each organization maintains an independent search engine, federated search techniques can provide parallel search over multiple collections. There are three major challenges in federated search. For each query, a subset of collections that are most likely to return relevant documents are selected. This creates the collection selection problem. To be able to select suitable collections, federated search systems need to acquire some knowledge about the contents of each collection, creating the collection representation problem. The results returned from the selected collections are merged before the final presentation to the user. This final step is the result merging problem. The goal of this work, is to provide a comprehensive summary of the previous research on the federated search challenges described above.
Application of data mining techniques for energy modeling of HVAC sub-systems

The Building Automation Systems (BAS) installed in commercial and institutional buildings collects a very large amount of data. These measurements present a gold mine of information which could be used for better understanding of the actual building operation and performance, or for fault detection. This study presents practical use of data mining to extract information from BAS measurements, for development of inverse models of building energy performance. The case study results show that the target variable (the building demand for chilled water) depends on the supply air temperature, humidity and enthalpy in the air-handling unit (AHU), the cooling coil valve modulation in both AHUs, and outside air enthalpy. About 75% of the variability of the dataset can be explained by using only four principal components calculated from the original dataset. The clustering analysis revealed that four daily profiles could describe the chilled water daily use in summer and autumn seasons.
Efficient data collection in wireless sensor networks with path-constrained mobile sinks

Recent work shows that sink mobility along a constrained path can improve the energy efficiency in wireless sensor networks. However, due to the path constraint, a mobile sink with constant speed has limited communication time to collect data from the sensor nodes deployed randomly. This poses significant challenges in simultaneously improving the amount of data collected and reduction in energy consumption. To address this issue, we propose a novel data collection scheme, called the Maximum Amount Shortest Path (MASP), that increases network throughput as well as conserves energy to optimize the assignment of sensor nodes. MASP is formulated as an integer linear programming problem and then solved with the help of a genetic algorithm. A two-phase communication protocol is designed to implement the MASP scheme. Simulations experiments using OMNET++ show that MASP outperforms the Shortest Path Tree (SPT) and static sink methods in terms of system throughput and energy efficiency.
What if all countries were actually in the same boat? A comparison of countries’ vulnerability based on Markov Switching Models

This article aims at assessing the main characteristics of the business cycle of 80 developed and developing countries. By comparing the possibility for these economies to enter or to exit a recession and the associated consequences, it aims at complementing existing literature with regard to scale and/or frequency of the study. Following the usual definition of a recession, an algorithmic classification tends to show that, surprisingly, developed and developing countries face similar probabilities to enter or to exit a recession, respectively around 5% and 18%. This aspect contradicts existing literature, which often advocates a greater volatility of developing countries’ business cycle with more frequent recessions. However emerging markets and economies face output per capita losses around twice as important as advanced ones when they undergo a recession. These observations are then tested using a non-linear parametric Markov-Switching Model. If the statistical validity of this method is bound by data availability, it echoes in a really good manner the pattern derived using a non-parametric approach. Estimating the model on the cyclical component of the series, derived using an HP filter, fits the best previous remarks. It also replicates other major characteristics. Indeed while developed countries form a rather homogeneous group, developing countries demonstrate greater heterogeneity. Latin American countries appear as the most vulnerable ones whereas Asian countries perform better than all other groups.
Color Image Segmentation and Multi-Level Thresholding by Maximization of Conditional Entropy

segmentation using higher order entropy as a textural feature for determination of thresholds over a two dimensional image histogram is discussed. A similar approach is applied to achieve multi-level thresholding in both grayscale and color images. The paper discusses two methods of color image segmentation using RGB space as the standard processing space. The threshold for segmentation is decided by the maximization of conditional entropy in the two dimensional histogram of the color image separated into three grayscale images of R, G and B. The features are first developed independently for the three (R, G, B) spaces, and combined to get different color component segmentation. By considering local maxima instead of the maximum of conditional entropy yields multiple thresholds for the same image which forms the basis for multilevel thresholding.
Real-Time Vision-Based Control of a Nonholonomic Mobile Robot

This paper considers the problem of vision-based control of a nonholonomic mobile robot. We describe the design and implementation of real-time estimation and control algorithms on a car-like robot platform using a single omni-directional camera as a sensor without explicit use of odometry. We provide experimental results for each of these vision-based control objects. The algorithms are packaged as control modes and can be combined hierarchically to perform higher level tasks involving multiple robots.
Summary for CIFE Seed Proposals for Academic Year 2019-20 Proposal number : 2019-09 Proposal title

up to 150 words) The problem: Having an accessible and constantly-updated digital model of a facility or construction site could lead to significant improvements in safety, energy efficiency and worker productivity. While recent advances in machine learning have made such a system feasible, the interactions between people and an automated system remain important for real-time modeling of a facility. The proposed solution: We will extend our Object R-CNN framework for dynamically updating building information models using computer vision. This framework allows people and automated systems to update the model of a facility. A web-based interface to observe and correct changes to the model will be developed so that an accurate digital model can be maintained. The proposed research approach: Our efforts will focus on improving our current algorithms for updating digital building models using a stream of RGB-D images, and exploring how this dynamic model can be made available and correctable to human stakeholders. 1 The PI(s) must be academic council member(s) at Stanford. 2 For this and the next points, delete the answers that don’t apply to your proposal.
Visible Light Positioning Using Fisheye Lens and Dual-facing Cameras for Coverage Area Expansion

Visible light communication (VLC) is a promising method for indoor localization because it enables accurate detection of absolute position and has minimal effect on the design of indoor environments. Additionally, localization landmarks, namely LED lights, do not require additional installation space or power supplies. VLC positioning (VLP) methods using the rolling shutters of CMOS image sensors have been studied recently. These methods enable data to be received at higher data rates than the frame rate of the camera on smart devices (such as smartphones or tablet devices). One of the challenges of VLP using a camera is the limitation of the coverage areas of localization. The coverage areas are limited by the signal spreading angle of landmarks and the field of view (FOV) of the camera. We propose a coverage area expansion method utilizing dual-facing cameras, which most recent smart devices are typically equipped with, and a fisheye lens adapter for expanding the FOV. The result of a simulation shows that coverage areas can be expanded five-fold using the fisheye lens and dual-facing cameras. We derive the formulas representing the geometrical relationship between the landmarks and the cameras, which can be analytically solved at a finite time. We conduct a localization experiment for the evaluation of positioning accuracy of the proposed method. The results show that the horizontal positioning errors are less than 10 cm in 90% of the measuring points. We also formulate dilution of precision (DOP) values for representing the reliability of positioning results. With DOP, it is easy for users to know if the results are erroneous. Keywords—camera; fisheye lens; LED light; smart device; visible light communication.
STUDIES ON THE DEVELOPMENT OF A TISSUE ENGINEERED MATRIX FOR BONE REGENERATION

Alternatives to conventional bone grafts are needed. Using a degradable polymer/ceramic composite system, our laboratory has developed porous matrices for use as trabecular bone graft replacements. Composed of 50:50 poly(lactide-co-glycolide) (PLAGA) and hydroxyapatite (HA), these matrices possess a three-dimensional (3D), porous structure based on microsphere technology. Our current studies examine the relationship between processing conditions and internal matrix structure. This has led to the development of three novel processes capable of fabricating a 3D porous structure: (1) a sintered microsphere method, (2) a solvent cast microsphere method, and (3) a gel microsphere method. Preliminary characterization of matrix structure was carried out by scanning electron microscopy (SEM). The solvent cast and sintered microsphere methods used prefabricated microspheres isolated through a solvent evaporation technique. SEM characterization of the sintered and solvent cast methods indicated a 3D network of fused microspheres with a range in pore diameter from 100450 µm and 50-250 µm, respectively. The gel microsphere method also used microspheres from the solvent evaporation technique, however, matrices were created from microspheres isolated before complete solvent removal. SEM characterization indicated a 3D internal porous network ranging in size from 100-300 µm. It was determined that the porous structure was due to the aggregation of “gel-like” microspheres.
Ja n 20 12 Asymptotic sampling formulae for Λ-coalescents

We present a robust method which translates information on the speed of coming down from infinity of a genealogical tree into sampling formulae for the underlying population. We apply these results to population dynamics where the genealogy is given by a Λcoalescent. This allows us to derive an exact formula for the asymptotic behavior of the site and allele frequency spectrum and the number of segregating sites, as the sample size tends to ∞. Some of our results hold in the case of a general Λ-coalescent that comes down from infinity, but we obtain more precise information under a regular variation assumption. In this case, we obtain results of independent interest for the time at which a mutation uniformly chosen at random was generated. This exhibits a phase transition at α = 3/2, where α ∈ (1, 2) is the exponent of regular variation. AMS 2000 Subject Classification. 60J25, 60F99, 92D25
Computer aided pronunciation learning system using speech recognition techniques

This paper describes a speech-enabled Computer Aided Pronunciation Learning (CAPL) system HAFSS © . This system was developed for teaching Arabic pronunciations to non-native speakers. A challenging application of HAFSS © is teaching the correct recitation of the holy Qur'an. HAFSS © uses a state of the art speech recognizer to detect errors in user recitation. To increase accuracy of the speech recognizer, only probable pronunciation variants, that cover all common types of recitation errors, are examined by the speech decoder. A module for the automatic generation of pronunciation hypotheses is built as a component of the system. A phoneme duration classification algorithm is implemented to detect recitation errors related to phoneme durations. The decision reached by the recognizer is accompanied by a confidence score to reduce effect of misleading system feedbacks to unpredictable speech inputs. Performance evaluation using a data set that includes 6.6% wrong speech segments showed that the system correctly identified the error in 62.4% of pronunciation errors, reported "Repeat Request" for 22.4% of the errors and made false acceptance of 14.9% of total errors. Index Terms: Pronunciation error detection, Qur’an recitation
Movie Recommender System Comparison of User-based and Item-based Collaborative Filtering Systems

Collaborative Filtering (CF) is a method that is widely used in recommendation systems. There are two approaches that are often used in CF, namely User-based CF and Item-based CF. The User-based CF approach requires several similar users to predict the rating of a new item. Meanwhile, Item-based CF requires several similar items to predict the rating of a new item. The number of similar users or similar items involved in predicting ratings will affect the computing load. This research aims to see the effect of the number of neighbors (similar user or similar items) used on the level of accuracy of rating predictions for an item. By using different numbers of neighbors for both User-based CF and Item-based CF, the results of the experiment show that the number of neighbors affects the level of accuracy although not too significant.
MOMENTUM STRATEGIES: EVIDENCE FROM PACIFIC BASIN STOCK MARKETS

We investigate the profitability of momentum investment strategy in six Asian stock markets. Unrestricted momentum investment strategies do not yield significant momentum profits. Although we find that a diversified country-neutral strategy generates small but statistically significant returns during 1981-1994, when we control for size and turnover effects, we find that the country-neutral profits dissipate. Our evidence suggests that the factors that contribute to the momentum phenomenon in the United States are not prevalent in the Asian markets.
Throughput maximization in uncooperative spectrum sharing networks

We consider an opportunistic communication system in which a secondary transmitter communicates over the unused time slots of a primary user. In particular, we consider a system in which the primary user is uncooperative and transmits whenever its buffer is nonempty, and the secondary user relies on feedback from its receiver in order to decide when to transmit. The objective of the secondary user is to maximize its own throughput without degrading the throughput of the primary user. We analyze the maximum achievable throughput of the secondary user by formulating the problem as a partially observable Markov decision process. We derive bounds on the optimal solution and find a channel access policy for the secondary user that is near-optimal when the primary user's exogenous arrival rate is low. These results are then used to characterize the set of arrival rates to the primary and secondary users that may be stably supported by the system.
Strong Update for Object-Oriented Flow-Sensitive Points-To Analysis

heap objects are represented by a tuple (h, hc, r) ∈ H×HC×R. H is the set of allocation sites, HC includes all heap contexts, and R = {R, NR} is a flag that indicates whether an object is most-recent (R) or non-most-recent (NR). If an abstract object is most-recent, it corresponds to a single concrete object, thus its fields can be strongly updated and should be tracked flow-sensitively. Nonmost-recent abstract objects, on the other hand, summarize the history of an allocation site and may represent many concrete objects; they are tracked flow-insensitively for efficiency. Object fields represent the fields of abstract objects, and are defined using a tuple (h, hc, r, fld). The first three arguments identify the object and fld specifies the field under consideration. As described in Section 3.2, contexts and heap contexts are used in context-sensitive analyses to distinguish reference variables, abstract heap objects and object fields that are analyzed in different executions. In our analysis, they are parameterized by three functions, RECORD, MERGE and MERGESTATIC, as listed in Figure 3.2. RECORD(h, c) provides the heap context for an object created at allocation site h in context c. MERGE(h, hc, r, pp, c) provides the callee analysis context for a dynamicdispatch method invocation at program point pp in a method analyzed in context c, where the receiver is the abstract heap object (h, hc, r). Note that the recency flag r is also included in the parameters to distinguish between two abstract objects (most-recent and non-most-recent), both of which can act as the receiver. MERGESTATIC(pp, c) provides the callee analysis context for a static method invocation at program point pp in a method analyzed in context c, without a receiver object. RECORD(h : H, c : C) = newHC : HC MERGE(h : H, hc : HC, r : R, callSite : P, c : C) = newC : C MERGESTATIC(callSite : P, c : C) = newC : C Figure 3.2: Context creation functions Program points P and inter-program points IP are especially important for flow-sensitivity and are not normally needed in a flow-insensitive analysis. They allow the analysis to take the program’s control flow into account. A program point can either correspond to a statement or serve as a point for method entry and exits. Prior to the points-to analysis, a program-point graph, Gpp, will be recorded and used as a reference to determine the control-flow edges along which points-to information flows. Inter-program points, on the other hand, represent the locations right before or after a program point
Third International Workshop on Experiences and Empirical Studies in Software Modelling

UML is a large notation offering many diagrams and a large set of constructs for each of them covering any possible modelling need. As a result its specification is a huge book, its metamodel is large, and defining/understanding its static and dynamic semantics is difficult. These features have a negative impact on the perception of the UML and lead in some cases to replace it by ad-hoc lean and simple DSLs. Thus, the following question arises: which are the most/less used UML diagrams/constructs? We would like to answer to this question by means of a survey, trying to detect which parts of the UML are the most used. To see how much a diagram is used we preliminarily investigate books, courses/tutorials, and tools covering UML. The less used diagrams will be then analysed to understand the reasons of their low usage.
RESEARCH OUTPUTS / RÉSULTATS DE RECHERCHE

An adaptive regularization algorithm using high-order models is proposed for solving partially separable convexly constrained nonlinear optimization problems whose objective function contains non-Lipschitzian (cid:96) q -norm regularization terms for q ∈ (0 , 1). It is shown that the algorithm using a p th-order Taylor model for p odd needs in general at most O ( (cid:15) − ( p +1) /p ) evaluations of the objective function and its derivatives (at points where they are deﬁned) to produce an (cid:15) -approximate ﬁrst-order critical point. This result is obtained either with Taylor models, at the price of requiring the feasible set to be “kernel centered” (which includes bound constraints and many other cases of interest), or with non-Lipschitz models, at the price of passing the diﬃculty to the computation of the step. Since this complexity bound is identical in order to that already known for purely Lipschitzian minimization subject to convex constraints [C. Cartis, N. I. M. Gould, and Ph. L. Toint, IMA J. Numer. Anal. , 32 (2012), pp. 1662–1695], the new result shows that introducing non-Lipschitzian singularities in the objective function may not aﬀect the worst-case evaluation complexity order. The result also shows that using the problem’s partially separable structure (if present) does not aﬀect the complexity order either. A ﬁnal (worse) complexity bound is derived for the case where Taylor models are used with a general convex feasible set.
Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA)

This talk revisits the first major program of research into interactive rule discovery. The Knowledge Factory is an interactive rule learning system developed in the 1990s. It has many novel features that still remain relevant today. The talk will cover the key techniques that the research developed, interpreting them in the light of subsequent developments in the field. Bio Geoff Webb is a Professor of Information Technology Research in the Faculty of Information Technology at Monash University, where he heads the Centre for Data Science. His primary research areas are machine learning, data mining, user modeling and computational structural biology. Many of his learning algorithms are included in the widely-used Weka machine learning workbench. A commercial implementation of his association discovery techniques, Magnum Opus, has been acquired by BigML, Inc. for inclusion in their cloud-based data mining solution. He was editor-in-chief of the highest impact data mining journal, Data Mining and Knowledge Discovery from 2005 to 2014. He is co-editor of the Springer Encyclopedia of Machine Learning, a member of the advisory board of Statistical Analysis and Data Mining, a member of the editorial board of Machine Learning and was a foundation member of the editorial board of ACM Transactions on Knowledge Discovery from Data. He has been Program Committee Co-Chair of the two top data mining conferences, ACM SIGKDD International Conference on Knowledge Discovery from Data (2015) and the IEEE International Conference on Data Mining (2010) and General Co-Chair of the 2012 IEEE International Conference on Data Mining. He is a technical advisor to BigML, Inc. He is an IEEE Fellow and has received the 2013 IEEE ICDM Service Award and a 2014 Australian Research Council Discovery Outstanding Researcher Award.