Miracle-dz/UniFAR-FTUs huggingface dataset:
Poster: Crowdsourced Location Aware Wi-Fi Access Control

In recent years, Wi-Fi has seen extraordinary growth; however, due to the cost, performance and security issues, many Wi-Fi hotspot owners would like to restrict the network access only to individuals inside the physical property. Unfortunately, due to the nature of wireless, this is difficult to accomplish, especially with the off-the-shelf omni-antenna devices. In this work, we develop and implement CLaWa, a Crowdsourced Location Aware Wi-Fi Access Control scheme to address this challenge. Our system is based on observations of differing characteristics of physical layer information across physical boundaries such as walls and corners. CLaWa crowdsources both channel state information (CSI) and received signal strength (RSS) of already validated users to classify future users. We have also selected an appropriate machine learning algorithm for CLaWa. Evaluation results show that CLaWa can identify the boundary around a given area precisely, thus granting network access only to users inside the area while not validating users outside the boundary. Compared to indoor localization schemes, CLaWa is a lightweight solution which does not require expensive localization operations.
An Efficient Approach for Mining Frequent Patterns over Uncertain Data Streams

Knowledge discovery in big data is one of most interesting topics in state-of-the-art research, and frequent patterns mining is a major task. With the rapid growth of modern technology, high volumes of data—which are of different veracities (i.e., may be precise or uncertain)—are flowing at a high velocity all over the world. Properties of data temporally changes with changes in the people's interests, which make the data dynamic. Due to the uncertainty and dynamic properties of data, finding appropriate and efficient approach to ensure the efficient usage of available resources has become a great challenge. In this paper, we design a new memory-efficient data structure, called Uncertain Stream (US)-tree, which stores recent meta-data. We also develop a probabilistic, sliding window based, efficient algorithm—called Uncertain Stream Frequent Pattern (USFP)-growth—for mining frequent patterns from uncertain data streams. Our comprehensive performance evaluation shows that USFP-growth is correct and efficient when compared with recent related approaches.
Causal descriptions and collaborative physical design

In the design of physical artifacts, written causal descriptions serve as an important medium for communication among members of a design team. This paper describes recent research in getting programs to read a~d reason on the basis of such descriptions and explores the use of this technology in constructing programs that support collaborative design by (1) cataloging and comparing alternate specifications of physical behavior relating to a designed artifact, and (2) managing exchange of written causal descriptions among members of a design team.
Improving Student’s Performance Using Data Clustering and Neural Networks in Foreign-Language Based Higher Education

The academic performance of engineering and science students during their first year at university is a turning point in their educational path and usually encroaches on their General Point Average (GPA) in a decisive manner. A case of particular interest is when students have to learn their courses’ materials in a foreign language. Indeed, it usually cumulates an additional handicap as will be shown. In this paper, we present a hybrid procedure based on Neural Networks (NN) and Data Clustering that enables academicians to predict students’ GPA according to their foreign language performance at a first stage, then classify the student in a well-defined cluster for further advising and follow up by forming a new system entry. This procedure has mainly a twofold objective. It allows meticulous advising during registration and thus, helps maintain high retention rate, acceptable GPA and grant management. Additionally, it endows instructors an anticipated estimation of their students’ capabilities during team forming and in-class participation. The results demonstrated a high level of accuracy and efficiency in identifying slow, moderate and fast learners and in endowing advisors as well as instructors an efficient tool in tackling this specific aspect of the learners’ academic standards and path.
Research of Facial Expression Recognition Based on ASM Model and RS-SVM

The accurate and efficient facial expression recognition method was researched in this paper. The active shape model (ASM) was a usual method of pattern recognition such as the facial features localization and facial expression recognition etc. In this paper, the support vector machine (SVM) was researched for the facial expression recognition, on the basis of the research SVM theory, thus a new and improved facial expression recognition method was proposed based on ASM and rough set SVM (RS-SVM). Firstly, the ASM algorithm was used for the facial features location, the features were extracted effectively. Moreover, the attribute reduction algorithm in the theory of rough set was introduced for the selection and classification of the extracted feature, the invalid and redundant features were filtered, finally, the classification was implemented with SVM algorithm. Simulation and experiment were carried out based on the Jaffe face database. Simulation results show that the new method has higher facial expression recognition rate than the traditional SVM method, and the recognition efficiency is improved greatly, it shows prospective application value in the pattern recognition and face recognition fields.
Weakly supervised construction of a repository of iconic images

We present a first attempt at semi-automatically harvesting a dataset of iconic images, namely images that depict objects or scenes, which arouse associations to abstract topics. Our method starts with representative topic-evoking images from Wikipedia, which are labeled with relevant concepts and entities found in their associated captions. These are used to query an online image repository (i.e., Flickr), in order to further acquire additional examples of topic-specific iconic relations. To this end, we leverage a combination of visual similarity measures, image clustering and matching algorithms to acquire clusters of iconic images that are topically connected to the original seed images, while also allowing for various degrees of diversity. Our first results are promising in that they indicate the feasibility of the task and that we are able to build a first version of our resource with minimal supervision.
Lane detection based on spiking neural network and hough transform

In the field of the unmanned automobile and the automobile auxiliary driving system, the real-time and accurate detection of the lane is very important. Based on the previous research on the lane detection, the paper introduces the spiking neural network with the parallel mechanism to detect the lane. Firstly, the region of interests (ROI) is set on the origin image that collected by a vehicle on-board camera. In order to reduce processing time, areas outside the road are excluded in the ROI. Then the image preprocessing is applied to the ROI, including RGB to grayscale, gray stretch and median filtering to eliminate noise. Edge detection of the lane is the key to determine whether the Hough transform can detect the lane. In this paper, the spiking neural network is used to detect the edge of the lane. Finally, Hough transform is used to detect the lane. Experimental results show that this method is more accurate and robust than other methods.
Dynamic bargaining of content shares in wireless cooperative systems

Mobile devices provided with cellular and short-range radio network interface cards offer the user potentials for new communication paradigms based on a cooperative behavior. The idea is that devices in close proximity exchange, through available short-range links, complementary shares of information downloaded from the cellular connections. Results in the literature have proven equal benefits for all partners in terms of energy consumption, achieved data rate, and transfer time, under homogeneous conditions. Notwithstanding, there could be heterogeneous cooperative scenarios in which gains are not perceived, unless novel smart policies for the content splitting among cooperating entities are implemented. In this paper we design a novel cooperation strategy based on a multi-criteria optimization algorithm for the bargaining of the content fraction each cooperating unit shall download according to the predicted cellular throughput. It will be shown that it is able to always match the expectations of the cooperating partners.
The Politics of Poverty Reduction

Globally, there is a commitment to eliminate poverty; and yet the politics that have caused anti-poverty policies to succeed in some countries and to fail in others have been little studied. The Politics of Poverty Reduction focuses on these political processes. Analysis is based partly on global comparisons and partly on case-studies of nine countries that span the developing world. Where governments are politically weak, they need to make alliances with other groups to stay in power, and where these have been with low-income groups, the result may be a lasting and effective pro-poor strategy. Often pro-poor policies have been brought in not with progressive intentions, but out of fear that the state will fall apart unless pro-poor elements are incorporated into government, and the most effective regimes in reducing poverty have seldom been the kindest and most benevolent. The ability to provide the poor with access to key markets, and in particular labour and capital, is crucial, and this in turn requires fiscal strength. Two crucial elements in the story are the ability to frame labour-intensive policies (given that labour is often the only thing that poor people are able to sell) and the design of effective tax and expenditure policies. Aid donors can make a key contribution, partly through reinforcing recipients' fiscal capacity, but much more through providing technical support of the right kind.
Stock market analysis using clustering techniques: the impact of foreign ownership on stock volatility in Vietnam

Data mining techniques have been used for various aspects of the financial market, such as prediction on stock index and price, portfolio risk management, and trend detection. In the stock market, there are a huge amount of data, including firms' profile, characteristics, and historical trading data. This paper investigates the impact of foreign ownership on stock market volatility in Vietnam using daily live trading of 100 major stocks on Ho Chi Minh Stock Exchange (HOSE) over the period from September 1st, 2015 to September 1st, 2016. Such companies are often categorized according to their business operations, but this is not necessarily reflected in the way their market valuations fluctuate. It is therefore interesting to analyze stock prices to identify companies that are trading in a similar way. K-mean cluster algorithm and hierarchical clustering methods are used to visualize the analysis on net trading volume, price volatility and return volatility ratio. Based on the visualized patterns of the stock market, we can observe the impact of foreign capital on the market volatility. Furthermore, those patterns indicate the investment behaviors that can help optimize the portfolio investment management.
Cholinergic Interneurons Control Local Circuit Activity and Cocaine Conditioning

Few But Powerful Drug activation of the different types of acetylcholine receptors in cholinergic neurons often generates opposing or conflicting effects. Using optogenetic techniques in transgenic mice, Witten et al. (p. 1677) investigated the function of a rather enigmatic subpopulation of cholinergic neurons, the giant interneurons of the nucleus accumbens. Their excitation paradoxically reduced neighboring medium spiny neuron firing, while their inhibition increased medium spiny neuron firing. Furthermore, the giant interneurons were directly activated by cocaine, and silencing their drug-induced activity during cocaine exposure in freely behaving animals disrupted cocaine reward. Silencing giant interneurons and thereby exciting medium spiny neurons during cocaine-induced activity disrupts cocaine reward. Cholinergic neurons are widespread, and pharmacological modulation of acetylcholine receptors affects numerous brain processes, but such modulation entails side effects due to limitations in specificity for receptor type and target cell. As a result, causal roles of cholinergic neurons in circuits have been unclear. We integrated optogenetics, freely moving mammalian behavior, in vivo electrophysiology, and slice physiology to probe the cholinergic interneurons of the nucleus accumbens by direct excitation or inhibition. Despite representing less than 1% of local neurons, these cholinergic cells have dominant control roles, exerting powerful modulation of circuit activity. Furthermore, these neurons could be activated by cocaine, and silencing this drug-induced activity during cocaine exposure (despite the fact that the manipulation of the cholinergic interneurons was not aversive by itself) blocked cocaine conditioning in freely moving mammals.
A Foresight Study of Technology : Enhanced Professional Learning in Europe

The paper presents recent findings from a pan-European foresight study on the future of technology-enhanced professional training. First the paper reviews existing technology roadmapping methodologies and introduces a new approach to developing a roadmap for technology-enhanced professional training. The proposed methodology involves the key phases of identification of prevalent visions (foresight analysis) and gap analysis. Interesting findings from the first foresight analysis phase, which identified the future visions, are presented and discussed.
Young Stress Analyst Competition Finalists Dislocation mediated hardening and relaxation in nanocrystalline palladium films revealed by on-chip HRTEM in-situ nano mechanical testing

Objectives: Nanocrystalline (nc) metals present excellent mechanical properties such as strength and fatigue resistance but often show low ductility. Furthermore, nc systems show moderate to high rate sensitivity at room temperature which might help restoring the ductility but can have disadvantage of creep/relaxation effects in applications. Thin metallic films constitute ideal candidates for looking at the mechanics of nc systems as they can be easily produced with nano-grained structures frequently involving only one grain over the thickness (for high resolution transmission electron microscopy (HRTEM) observation) and sharp textures. Time dependent relaxation/creep mechanisms are amplified in nc materials compared to traditional microcrystalline systems which can be due to a change in deformation mechanisms. Different methods have been used to characterize the rate sensitive creep or relaxation behaviour at the nanoscale, involving nanoindentation, bending and direct tensile testing. The main shortcomings involve the difficulty to impose very small strain rates typical of real applications and to perform in-situ relaxation tests in order to characterize the deformation mechanisms. Also, the sensitivity to drift is increased at the nanoscale, which hampers imposing a constant load during long periods of time. In the present project a novel technique for stress and strain evolution measurement called "on-chip testing" and allowing in-situ HRTEM observations has been used for the first time for creep/relaxation experiments of nc Pd free standing beams. The concept of the on-chip method is to use the internal stress present in a long beam, 'the actuator' (30 nm-thick Si 3 N 4), to deform another material attached to it, 'the specimen' (90 nm-thick ~2 µm wide Pd ribbon) by selectively back etching the Si substrate and SiO 2 sacrificial layer yielding the actuator to contract and deform the Pd beams and enable direct in-plane TEM observation (Figure 1a). The stress and strain in the deformed Pd specimens are provided by the measurement of the specimen elongation. A complete stress-strain curve is generated by varying the actuator versus specimen length ratio to impose different 1 deformations. The relaxation experiment is performed by measuring the deformation at different time intervals. Note that both stress and strain vary with time. The stress and strain evolution during relaxation has been measured under pure uniaxial tension (Figure 1b). The fracture strain is around 3% and the yield stress ~500±60 MPa close to the internal stress measured after deposition. The mean activation volume was also measured for different plastic strains …
Sparsity-enabled signal transient feature extraction using wavelet basis and constrained optimization algorithm

Ill-posed linear inverse problems (ILIP), such as restoration and reconstruction, are core topics of transient feature extraction in cyclostationary signal processing. A standard formulation for solving these problems consists of a constrained optimization problem with a regularization function minimized. In this paper, a method combining sparse representation and special wavelet basis is proposed to handle one class of constrained problems tailored to transient feature extraction applications. Simulation study concerning cyclic transients signal shows the effectiveness of this method. Application in transient feature extraction of fault gearbox vibration signal shows that the proposed method can extract the transient feature effectively.
Tiles Cards: a Card-based Design Game for Smart Objects Ecosystems

We present Tiles Cards, a tool to foster end-user design thinking of smart object applications. Tiles Cards facilitate participatory design by abstracting the complexity of IoT technology into a set of interaction primitives and composition rules accessible to nonexperts. Further, it promotes creative thinking by means of a mission-based game in which teams have to fulfill design tasks under constraints. Tiles Cards is a generic tool that can specialized to multiple application domains including learning and games. In this workshop we are presenting results from our pilot study discussing the opportunities of using card games for end-user design.
The Community College Penalty and Bachelor's Degree Completion: Fact or Fiction? Policy Research: IERC 2013-1.

Rationale: Research examining the relationship between initial community college enrollment and bachelor’s completion have shown mixed results with some studies indicating a clear penalty for community college enrollment and other studies showing no penalty, partly due to the point at which the given study began tracking the community college students: at initial community college entrance or after vertical transfer to a four-year college. We adopted the latter view while simultaneously controlling for student background characteristics, as well as high school and college contexts.
The Effect of Family Control on Corporate Performance

generated by the corporate cash flows from operations or by the attraction of new firm shareholders, is considered as the most important source of the business continuance over the long-run. Thus, profitability, defined as the capacity to generate resources or performance, has been one of the indicators most commonly used to define the success or failure (right or wrong decision making) of company management. It allows one to evaluate the efficiency of the implemented strategy and, therefore, to assess the company's competitive position. This indicator has become a concern for shareholders, who have invested their resources in the company, and a worry for the directors (managers/executives) of the companies because this result reflects on their capacity to keep the business running. Adequate profitability both compensates shareholders for their risk taking and may finance the growth of a company through internally generated funds, which, according to Gonzalez-Perez and Correa (1998), constitutes one of the preferred funding alternatives for a firm's expansion. At the same time, profitability facilitates efficient resource allocation as investors devote their resources to more promising alternatives. That is, unless a company reaches at least its competitors' profitability level, it will find it difficult to attract new capital. The alternative of involving financial institutions in the financing of the company's growth may impose certain limitations, as lenders would require compensation in the form of higher interest rate to cover the higher risk born.47
Part II : Designing the classifier

— Ageing effects, environmental changes, thermal drifts, soft and hard faults affect physical systems by changing their nature and behaviour over time. To cope with a process evolution adaptive solutions must be envisaged to track its dynamics; in this direction adaptive classifiers are generally designed by assuming the stationary hypothesis for the process generating the data with very few results addressing non-stationary environments. The paper proposes a methodology based on k-NN classifiers for designing adaptive classification systems able to react to changing conditions just-in-time, i.e., exactly when it is needed. k-NN classifiers have been selected for their computational-free training phase, the possibility to easily estimate the model complexity k and keep under control the computational complexity of the classifier through suitable data reduction mechanisms. A just-in-time classifier requires a temporal detection of a (possible) process deviation (aspect tackled in a companion paper) followed by an adaptive management of the knowledge base of the classifier to cope with the process change. The novelty of the proposed approach resides in the general framework supporting the real time update of the knowledge base of the classification system in response to novel information coming from the process both in stationary conditions (accuracy improvement) and in non stationary ones (process tracking) and in providing a suitable estimate of k. It is shown that the classification system grants consistency once the change targets the process generating the data in a new stationary state, as it is the case in many real applications.
Health Insurance Estimates for States

The U.S. Census Bureau Small Area Estimates Branch (SAEB) is developing model-based estimates of the number of people not covered by health insurance (i.e. uninsured) at the state and county levels. There are two primary motivations for conducting this research. First, there is a broad public interest in health insurance coverage issues. The number of uninsured people in the United States increased by roughly 10 million in the 1990s, despite a strengthening economy. With the failure of the universal health insurance coverage movement in the early 1990s, it became apparent that incremental policies would be the path to follow for increasing coverage in the population. In order to determine how to best target certain populations that may have disproportionate levels of non coverage, policy makers need to be able to accurately identify these groups.
Principles of OBJ2

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