Miracle-dz/UniFAR-FTUs huggingface dataset:
WeChecker: efficient and precise detection of privilege escalation vulnerabilities in Android apps

Due to the rapid increase of Android apps and their wide usage to handle personal data, a precise and large-scaling checker is in need to validate the apps' permission flow before they are listed on the market. Several tools have been proposed to detect sensitive data leaks in Android apps. But these tools are not applicable to large-scale analysis since they fail to deal with the arbitrary execution orders of different event handlers smartly. Event handlers are invoked by the framework based on the system state, therefore we cannot pre-determine their order of execution. Besides, since all exported components can be invoked by an external app, the execution orders of these components are also arbitrary. A naive way to simulate these two types of arbitrary execution orders yields a permutation of all event handlers in an app. The time complexity is O(n! ) where n is the number of event handlers in an app. This leads to a high analysis overhead when n is big. To give an illustration, CHEX [10] found 50.73 entry points of 44 unique class types in an app on average. In this paper we propose an improved static taint analysis to deal with the challenge brought by the arbitrary execution orders without sacrificing the high precision. Our analysis does not need to make permutations and achieves a polynomial time complexity. We also propose to unify the array and map access with object reference by propagating access paths to reduce the number of false positives due to field-insensitivity and over approximation of array access and map access. We implement a tool, WeChecker, to detect privilege escalation vulnerabilities [7] in Android apps. WeChecker achieves 96% precision and 96% recall in the state-of-the-art test suite DriodBench (for compairson, the precision and recall of FlowDroid [1] are 86% and 93%, respectively). The evaluation of WeChecker on real apps shows that it is efficient (average analysis time of each app: 29.985s) and fits for large-scale checking.
Interaction techniques with virtual humans in mixed environments

Homogenous virtual environments evolved into mixed environments thanks to recent augmented reality techniques. We have done research on novel interaction techniques in virtual environments exploiting the usage of 3D virtual space. We will briefly present our augmented reality set-up for mixed reality experiments and we will analyze two distinct mixed environment case studies, and present two new interaction techniques where we use virtual humans as mediators between the real and virtual world.
A Risky Time for Muslim Families: Professionalised Counter-radicalisation Networks

In July 2015, a new statutory duty was sanctioned in the UK for a range of professional practitioners, including social workers, to pay ‘due regard to preventing terrorism’. The duty has contributed to a shifting of social work practice and decision-making from the fields of advocacy and promotion of ethics, social justice and human rights, towards risk-work more analogous to that of the security services. Social workers are caught up in pre-emptive risk work, operating in a pre-crime space. Further, an ‘ethic of silence’ has emerged because social workers are not speaking back or challenging the duty due to the ensnared nature of the dominant securitised discourses, which prevent counter-discourses from emerging. Utilising an autoethnographic approach, this paper shows that the new duty is reorganising and rearranging new networks of practitioners with securitisation a dominant feature, and this significantly affects practice decisions. Latour’s actor network theory (ANT) helps us to examine the ethical and practical implications for decision-making. Shifting notions of ethics, rights and as yet unforeseen consequences of PREVENT concern us. This being said, humane and socially just social work practice within the duty is possible; strengths-based risk practices provide practical and ethical ways forward and these are discussed.
CRISPR/Cas9: a molecular Swiss army knife for simultaneous introduction of multiple genetic modifications in Saccharomyces cerevisiae

A variety of techniques for strain engineering in Saccharomyces cerevisiae have recently been developed. However, especially when multiple genetic manipulations are required, strain construction is still a time-consuming process. This study describes new CRISPR/Cas9-based approaches for easy, fast strain construction in yeast and explores their potential for simultaneous introduction of multiple genetic modifications. An open-source tool (http://yeastriction.tnw.tudelft.nl) is presented for identification of suitable Cas9 target sites in S. cerevisiae strains. A transformation strategy, using in vivo assembly of a guideRNA plasmid and subsequent genetic modification, was successfully implemented with high accuracies. An alternative strategy, using in vitro assembled plasmids containing two gRNAs, was used to simultaneously introduce up to six genetic modifications in a single transformation step with high efficiencies. Where previous studies mainly focused on the use of CRISPR/Cas9 for gene inactivation, we demonstrate the versatility of CRISPR/Cas9-based engineering of yeast by achieving simultaneous integration of a multigene construct combined with gene deletion and the simultaneous introduction of two single-nucleotide mutations at different loci. Sets of standardized plasmids, as well as the web-based Yeastriction target-sequence identifier and primer-design tool, are made available to the yeast research community to facilitate fast, standardized and efficient application of the CRISPR/Cas9 system.
Using case-based reasoning for autonomous vehicle guidance

Vehicle guidance in complex scenarios such as inner-city traffic requires an in-depth understanding of the current situation. In order to select the appropriate behavior for an autonomous vehicle, an analysis of the situation is needed. The analysis consists of an estimation of the situation's development with respect to the selected behavior. This can only be done using higher-level reasoning techniques. In this paper, an approach for situation interpretation for autonomous vehicles is presented. The approach relies on case-based reasoning in order to predict the evolvement of the current situation and to select the appropriate behavior. Case-based reasoning allows to utilize prior experiences in the task of situation assessment.
Natural Resource Management: Contributions of System Dynamics to Research, Policy and Implementation

This special issue contains a series of papers in the area of System Dynamics modelling (Forrester, 1961; 1968). Perhaps System Dynamics – SD has not always been at the centre of ‘Systems Science’ in institutional terms. It is also distinctive in terms of some of its ideas: employing the framework of Richardson (1991), SD grew out of a ‘servo-mechanisms thread’ of feedback thinking, in contrast to the ‘cybernetics thread’ from which rather more systems approaches developed. However, looking more broadly one can see that SD is firmly grounded in the notion that holistic thinking provides a better way of understanding the world and of crafting effective interventions. The project to draw SD into the family of systems approaches is now well into its third decade and has involved connections being made from outside SD by mainstream system scientists (e.g. Bloomfield, 1982; Jackson & Keys, 1984; Keys, 1988) and from within SD itself (Richardson, 1991; Lane, 1994) to the point that SD is frequently treated as selfevidently being a member of the range of systems approaches (e.g. Jackson, 2003). An indicator of this is this journal’s interest in the subject generally: SD is one of the journal’s designated systems fields and therefore has its own editor and the number of SD submissions and publications continues to rise. Further evidence of SD’s embrace by Systems Science is this journal’s support for the ‘European System Dynamics Workshop’ (EuSDW) series, bi-
Mathematical experiences and parental involvement of parents who are and who are not mathematicians

Previous studies suggest that parental involvement in children’s mathematics education is more established for parents who feel competent in mathematics. This qualitative study aimed to gain an in-depth insight into the experiences of parental involvement of two different groups of parents: those who are mathematicians and those who are not. Data were collected through narrative interviews with parents. A thematic analysis of the data revealed findings within two distinct but interrelated themes: parents’ mathematical experiences and parental involvement in their children’s mathematics education. The findings indicated that the two groups of participating parents differ in their own experiences of mathematics as well as in their parental involvement. The main difference in parental involvement was indicated in the area of children’s school mathematics, since mathematician parents, compared to non-mathematician parents, according to their narratives almost never get involved in their children’s mathematics homework. In addition, the data revealed a large gap in the coverage and content of the mathematical activities that parents in both groups provided to their children.
Cervical surgical techniques for the treatment of cervical spondylotic myelopathy.

OBJECT The objective of this systematic review was to use evidence-based medicine to compare the efficacy of different surgical techniques for the treatment of cervical spondylotic myelopathy (CSM). METHODS The National Library of Medicine and Cochrane Database were queried using MeSH headings and keywords relevant to anterior and posterior cervical spine surgery and CSM. The guidelines group assembled an evidentiary table summarizing the quality of evidence (Classes I-III). The group formulated recommendations that contained the degree of strength based on the Scottish Intercollegiate Guidelines network. Validation was done through peer review by the Joint Guidelines Committee of the American Association of Neurological Surgeons/Congress of Neurological Surgeons. RESULTS A variety of techniques have improved functional outcome after surgical treatment for CSM, including anterior cervical discectomy with fusion (ACDF), anterior cervical corpectomy with fusion (ACCF), laminoplasty, laminectomy, and laminectomy with fusion (Class III). Anterior cervical discectomy with fusion and ACCF appear to yield similar results in multilevel spine decompression for lesions at the disc level. The use of anterior plating allows for equivalent fusion rates between these techniques (Class III). If anterior fixation is not used, ACCF may provide a higher fusion rate than multilevel ACDF but also a higher graft failure rate than multilevel ACDF (Class III). Anterior cervical discectomy with fusion, ACCF, laminectomy, laminoplasty, and laminectomy with arthrodesis all provide near-term functional improvement for CSM. However, laminectomy is associated with late deterioration compared with the other types of anterior and posterior surgeries (Class III). CONCLUSIONS Multiple approaches exist with similar near-term improvements; however, laminectomy appears to have a late deterioration rate that may need to be considered when appropriate.
Using Social Network to Predict the Behavior of Active Members of Online Communities

In this paper we use the social networks of active members (i.e. authors) in online communities to predict their future behavior. We limit our discussion to web forums, the most generic type of online communities. We first address the problem of measuring the ties between authors where there is (almost) no direct information (i.e. citations) about the tie between two authors. We developed a surrogate metrics to this problem so that a corresponding social network can be constructed. However, while such networks are suitable for describing authors' behavior, they are not very suitable for predicting their future behavior. Here we suggest a very specific method for creating networks of authors that is specially designed for prediction. The main characteristic of our approach is that we take into account all available data while putting more weight on more recent data (messages). In addition to networks of authors, we also use the two-mode network of threads (topics) to generate predictions.All network data and other information related to the posting habits of the authors are thus used to develop an algorithm that predicts the future behavior of the authors. The ultimate goal of our research is to generate explicit recommendations for authors of online communities about which authors and which topics/threads to follow. Finally, these predictions/recommendations can be used as the basis for a more user-friendly forum interface.
Segmentation of Brain Tumor from MRI Using Skull Stripping and Neural Network

- Brain tumor is an alarming disease if not noticed on time. Several researchers have done their researches in this field to discover some new methods of brain tumor detection. In this paper, we present a method of brain tumor detection from MRI images. Segmentation is done by using Self-Organizing Map (SOM) and Neural network (NN). Stationary Wavelet Transform (SWT) is used to extract the features from an input image before the training process for segmentation. We proposed a new skull stripping algorithm for the purpose of effective skull stripping. We used BRAINIX medical images as a dataset for our method. The proposed method performs better than the methods discussed in the literature. It is easy to implement and robust.
A  Graph Partition-Based Soft Clustering Algorithm

Cluster analysis is one of the basic tools for discovering structure in data sets. Soft clustering enables the user to have a good overall view of the information contained in the data set that he has. However, existing soft algorithms suffer from various aspects. We propose GPSC (graph partition-based soft clustering), an efficient soft clustering algorithm based on a given graph model. This algorithm projected data set to a graph firstly, applied graph partition method to get an initial clustering result. Secondly, the core vertices and verge vertices have been defined to measure the membership for each vertex to clusters and relationships of neighbor clusters. Then the membership matrix and relationship matrix have been induced. From these two matrixes, we can find more fuzzy relations and latent clusters. Our experiments show that GPSC algorithm is able to discover clusters that cannot be detected by non-fuzzy algorithms, while maintaining a high degree of efficiency. Comparison with existing hard clustering algorithms like K-means and its variants shows that GPSC is both effective and efficient.
Golay sequence for parital Fourier and Hadamard compressive imaging

This paper introduces Golay sequence for partial Fourier and Hadamard compressive imaging. In the proposed system, the signal is pre-modulated by a binary Golay sequence before applying a random subsampled Fourier or Hadamard transform. The correpsonding sampling operator has been proved to be incoherent with the (block) DCT or the Haar wavelet transform. Empirical results show that they are also incoherent with the Daubechies wavelets. It is well known that natural images are sparse in the DCT and the wavelet basis. Hence, the proposed sampling operators are promising in many Fourier or Hadamard compressive imaging applications. In fact, they can achieve near-optimal reconstruction performance with small memory requirement and simple hardware implementation. Some simulation results and proof-of-concept experimental results are included to demonstrate the validity of the theory and the potential of the proposed sampling operators.
Semantic relevance measure between resources based on a graph structure

Upon developing an information system, establishing an effective information retrieval method is the main problem along with the management of the information. With ontologies, the semantic structure of knowledge can be represented and the resources can be managed along with their context information. Observing that within this knowledge space, resources are not isolated but connected to each other by various types of relations, we believe that utilising those relations can aid information provisioning. In this paper, we explore the concept of the semantic relevance between resources and the semantic structure of a knowledge space, and present a relevance measuring algorithm between resources based on a graph structure, with two implementation examples.
Abstract: Matrices Over Runtime Systems at Exascale

The goal of Matrices Over Runtime Systems at Exascale (MORSE) project is to design dense and sparse linear algebra methods that achieve the fastest possible time to an accurate solution on large-scale multicore systems with GPU accelerators, using all the processing power that future high end systems can make available. In this poster, we propose a framework for describing linear algebra algorithms at a high level of abstraction and delegating the actual execution to a runtime system in order to design software whose performance is portable accross architectures. We illustrate our methodology on three classes of problems: dense linear algebra, sparse direct methods and fast multipole methods. The resulting codes have been incorporated into Magma, Pastix and ScalFMM solvers, respectively.
SUBWAVE: a methodology for modeling digital substrate noise injection in mixed-signal ICs

A methodology is presented for generating compact models of substrate noise injection in complex logic networks. For a given gate library, the injection patterns associated with a gate and an input transition scheme are accurately evaluated using device-level simulation. Assuming spatial independence of all noise generating devices, the cumulative switching noise resulting from all injection patterns is efficiently computed using a gate-level event-driven simulator. The resulting injected signal is then sampled and translated into an energy spectrum which accounts for fundamental frequencies as well as glitch energy. Preliminary results demonstrate the validity of the assumptions and the accuracy of the approach on a set of standard benchmark circuits.
Generalized beamforming (GBF) for MIMO amplify-and-forward relaying

In this paper, we consider the generalized beam-forming (GBF) scheme for the MIMO amplify-and-forward relay channel. Cooperative communication is viewed as a method for increasing diversity gain and reducing end to end path loss. The use of relays can create a virtual antenna array so that it allows users to exploit the advantages of multiple input multiple output (MIMO) techniques. In this paper we show that the generalized beamforming is beneficial for cooperative transmission with multiple relays. The efficiency of the generalized beamforming with cooperative diversity techniques is verified by simulation results.
Obesity and social marginalization: When do organized activities promote or hinder peer relationships?

ABSTRACT Overweight youth are often socially marginalized and have fewer friends than their nonoverweight peers. Participation in organized activities may be one way to promote friendships for overweight youth. In this study, we used a large nationally representative sample to test whether two aspects of participation promoted friendships, namely the number of activities and the social acceptance of activity co-participants. In contradiction to our hypotheses, participating in activities with high socially accepted peers was associated with significantly fewer friendships over time for overweight adolescents. Conversely, there were small differences between overweight and nonoverweight adolescents’ friendships when they participated in activities with low socially accepted co-participants. Our findings provide new insight that activities may not be universally beneficial for overweight adolescents’ peer relationships. We discuss the various peer mechanisms that explain why certain types of activities predict these friendship patterns for overweight youth.
Alternative Computational Protocols for Supercharging Protein Surfaces for Reversible Unfolding and Retention of Stability

Reengineering protein surfaces to exhibit high net charge, referred to as “supercharging”, can improve reversibility of unfolding by preventing aggregation of partially unfolded states. Incorporation of charged side chains should be optimized while considering structural and energetic consequences, as numerous mutations and accumulation of like-charges can also destabilize the native state. A previously demonstrated approach deterministically mutates flexible polar residues (amino acids DERKNQ) with the fewest average neighboring atoms per side chain atom (AvNAPSA). Our approach uses Rosetta-based energy calculations to choose the surface mutations. Both protocols are available for use through the ROSIE web server. The automated Rosetta and AvNAPSA approaches for supercharging choose dissimilar mutations, raising an interesting division in surface charging strategy. Rosetta-supercharged variants of GFP (RscG) ranging from −11 to −61 and +7 to +58 were experimentally tested, and for comparison, we re-tested the previously developed AvNAPSA-supercharged variants of GFP (AscG) with +36 and −30 net charge. Mid-charge variants demonstrated ∼3-fold improvement in refolding with retention of stability. However, as we pushed to higher net charges, expression and soluble yield decreased, indicating that net charge or mutational load may be limiting factors. Interestingly, the two different approaches resulted in GFP variants with similar refolding properties. Our results show that there are multiple sets of residues that can be mutated to successfully supercharge a protein, and combining alternative supercharge protocols with experimental testing can be an effective approach for charge-based improvement to refolding.
A Joint Segmentation Model of Consumers and Products Applied to the Sales of Music Albums

We develop a new model that simultaneously captures two principal sources of heterogeneity: variation across segments of consumers and variation across clusters of products. We focus on the recorded music industry as a managerial context, and construct a flexible duration-time model that can be used to capture the dynamic purchase patterns across a broad portfolio of different albums. Our basic premise is that a set of generic consumer segments exist and remain fixed across all albums, and each album (or each cluster of similar albums) can be viewed as drawing different proportions from each of these underlying segments. We allow explanatory variables to have a differential impact on both components of the model, i.e., accelerating purchase rates within segment, and/or changing the proportions drawn by each cluster. As a useful application and validity test for our model, we demonstrate how we can leverage the pooled information available from a set of existing albums to make early and accurate predictions about the sales performance of a new album.
A min-max model predictive control for a class of hybrid dynamical systems

This paper presents a min-max model predictive control algorithm for a class of hybrid systems by exploiting the equivalence between piecewise linear systems and mixed logical dynamical systems. The control algorithm consists of two control modes which are a state feedback mode and a min-max model predictive control mode. In the min-max model predictive control mode, the constrained positively invariant sets are used as the end set constraint, and an approach based on a min-max model predictive control formulation is employed. This control algorithm guarantees that the state converges to a union of constrained positively invariant sets with no constraint violation.