Generating Predicate Callback Summaries for the Android Framework

One of the challenges of analyzing, testing and debugging Android apps is that the potential execution orders of callbacks are missing from the apps' source code. However, bugs, vulnerabilities and refactoring transformations have been found to be related to callback sequences. Existing work on control flow analysis of Android apps have mainly focused on analyzing GUI events. GUI events, although being a key part of determining control flow of Android apps, do not offer a complete picture. Our observation is that orthogonal to GUI events, the Android API calls also play an important role in determining the order of callbacks. In the past, such control flow information has been modeled manually. This paper presents a complementary solution of constructing program paths for Android apps. We proposed a specification technique, called Predicate Callback Summary (PCS), that represents the callback control flow information (including callback sequences as well as the conditions under which the callbacks are invoked) in Android API methods and developed static analysis techniques to automatically compute and apply such summaries to construct apps' callback sequences. Our experiments show that by applying PCSs, we are able to construct Android apps' control flow graphs, including inter-callback relations, and also to detect infeasible paths involving multiple callbacks. Such control flow information can help program analysis and testing tools to report more precise results. Our detailed experimental data is available at: http://goo.gl/NBPrKsComment: 11 page

Cristina Valdes: Pianist, in recital

Program listing performers and works performe

Learning to Skim Text

Recurrent Neural Networks are showing much promise in many sub-areas of natural language processing, ranging from document classification to machine translation to automatic question answering. Despite their promise, many recurrent models have to read the whole text word by word, making it slow to handle long documents. For example, it is difficult to use a recurrent network to read a book and answer questions about it. In this paper, we present an approach of reading text while skipping irrelevant information if needed. The underlying model is a recurrent network that learns how far to jump after reading a few words of the input text. We employ a standard policy gradient method to train the model to make discrete jumping decisions. In our benchmarks on four different tasks, including number prediction, sentiment analysis, news article classification and automatic Q\&A, our proposed model, a modified LSTM with jumping, is up to 6 times faster than the standard sequential LSTM, while maintaining the same or even better accuracy

Carbonation of Concrete Incorporating High Volume of Micro and Low Volume of Nano Palm Oil Fuel Ash

This research determines the carbonation resistance of concrete containing micro and nano palm oil fuel ash (POFA) using accelerate carbonation testing. Microstructures were studied using XRF, XRD and SEM. Workability and sorptivity rate were also tested. The results show that 10% micro POFA with 0.5% nano POFA had the highest carbonation resistance. Further increasing the amount of POFA would decrease the carbonation resistance of concrete

Do consumers care about environmentally sustainable attributes along the food supply chain? —A systematic literature review

The agri-food market has shown a clear signal of "green" consumption that drives an increasing interest in studying consumers' willingness to pay (WTP) for food products with environmentally sustainable attributes, such as eco-friendly and carbon neutral. Whilst many existing studies have focused on a general idea of green attributes or on-farm practices that are regarded to be most relevant to the attributes, the agri-food industry has started to address consumers' concerns about the negative environmental impacts of agri-food production across the whole supply chain, including the processing, transportation, and consumption process. It is therefore the purpose of this study to conduct a systematic review of the existing literature on consumers' intentions of purchasing and WTP for food products with environmentally sustainable attributes, with a special interest in understanding the connections between consumer behaviours and different stages of the food supply chain. Results of the study revealed three main research gaps: the lack of clear definitions of environmentally sustainable attributes; ignorance of connections between the characteristics of environmentally sustainable attributes and different stages of the food supply chain; and lacking effective information processing among the key players along the supply chain, leading to inefficient communication between the supply and demand side. The findings of the study help form a conceptual framework for future studies to associate environmentally sustainable attributes to the whole food supply chain that helps the agri-food industry to effectively process market information, communicate with consumers, and satisfy the market demand

Sparsity in Dynamics of Spontaneous Subtle Emotions: Analysis \& Application

Spontaneous subtle emotions are expressed through micro-expressions, which are tiny, sudden and short-lived dynamics of facial muscles; thus poses a great challenge for visual recognition. The abrupt but significant dynamics for the recognition task are temporally sparse while the rest, irrelevant dynamics, are temporally redundant. In this work, we analyze and enforce sparsity constrains to learn significant temporal and spectral structures while eliminate irrelevant facial dynamics of micro-expressions, which would ease the challenge in the visual recognition of spontaneous subtle emotions. The hypothesis is confirmed through experimental results of automatic spontaneous subtle emotion recognition with several sparsity levels on CASME II and SMIC, the only two publicly available spontaneous subtle emotion databases. The overall performances of the automatic subtle emotion recognition are boosted when only significant dynamics are preserved from the original sequences.Comment: IEEE Transaction of Affective Computing (2016

Correlated Dirac Particles and Superconductivity on the Honeycomb Lattice

We investigate the properties of the nearest-neighbor singlet pairing and the emergence of d-wave superconductivity in the doped honeycomb lattice considering the limit of large interactions and the $t-J_1-J_2$ model. First, by applying a renormalized mean-field procedure as well as slave-boson theories which account for the proximity to the Mott insulating state, we confirm the emergence of d-wave superconductivity in agreement with earlier works. We show that a small but finite $J_2$ spin coupling between next-nearest neighbors stabilizes d-wave symmetry compared to the extended s-wave scenario. At small hole doping, to minimize energy and to gap the whole Fermi surface or all the Dirac points, the superconducting ground state is characterized by a $d+id$ singlet pairing assigned to one valley and a $d-id$ singlet pairing to the other, which then preserves time-reversal symmetry. The slightly doped situation is distinct from the heavily doped case (around 3/8 and 5/8 filling) supporting a pure chiral $d+id$ symmetry and breaking time-reversal symmetry. Then, we apply the functional Renormalization Group and we study in more detail the competition between antiferromagnetism and superconductivity in the vicinity of half-filling. We discuss possible applications to strongly-correlated compounds with Copper hexagonal planes such as In$_3$Cu$_{2}$VO$_9$. Our findings are also relevant to the understanding of exotic superfluidity with cold atoms.Comment: 13 pages, 8 figure

Quantum Spin Hall Insulators with Interactions and Lattice Anisotropy

We investigate the interplay between spin-orbit coupling and electron-electron interactions on the honeycomb lattice combining the cellular dynamical mean-field theory and its real space extension with analytical approaches. We provide a thorough analysis of the phase diagram and temperature effects at weak spin-orbit coupling. We systematically discuss the stability of the quantum spin Hall phase toward interactions and lattice anisotropy resulting in the plaquette-honeycomb model. We also show the evolution of the helical edge states characteristic of quantum spin Hall insulators as a function of Hubbard interaction and anisotropy. At very weak spin-orbit coupling and intermediate electron-electron interactions, we substantiate the existence of a quantum spin liquid phase.Comment: 7 pages, 9 figures, final versio