Miracle-dz/UniFAR-FTUs huggingface dataset:
Andro Lyze: A Distributed Framework for Efficient Android App Analysis

In recent years, the number of mobile applications has grown significantly. Not surprisingly, various security and privacy concerns associated with mobile applications have emerged. Several researchers are addressing these problems by analyzing the security properties of mobile application code. Most of the security checks rely on custom scripts and are quite heterogeneous with respect to dependencies, deployment and reporting. In this paper, we present AndroLyze, a distributed framework with unified logging and reporting functionality to perform security checks on large numbers of applications in an efficient manner. AndroLyze provides optimized scheduling algorithms for distributing static code analysis tasks across several machines. Moreover, AndroLyze can handle several versions of a single mobile application to generate a security track record over many versions. To demonstrate the benefits of AndroLyze, we have analyzed the Top Free 500 Android applications of all categories in Google Play collected over three years. The resulting data set consists of almost 40,000 mobile applications and requires about 227 GB of storage space.
A Decoupling Approach for Low-Complexity Vector Perturbation in Multiuser Downlink Systems

In this letter, we propose an efficient algorithm which reduces the complexity of conventional vector perturbation schemes by searching the real and imaginary components of a perturbation vector individually. To minimize a performance loss induced from the decoupled joint search, we apply diagonal precoding at the transmitter whose parameters are iteratively optimized to maximize the chordal distance between subspaces spanned by the real and imaginary components. We also propose a simple non-iterative method with a slight performance loss which can achieve a significant complexity reduction compared to the conventional vector perturbation schemes.
σE-dependent small RNAs of Salmonella respond to membrane stress by accelerating global omp mRNA decay

The bacterial envelope stress response (ESR) is triggered by the accumulation of misfolded outer membrane proteins (OMPs) upon envelope damage or excessive OMP synthesis, and is mediated by the alternative sigma factor, σE. Activation of the σE pathway causes a rapid downregulation of major omp mRNAs, which prevents further build‐up of unassembled OMPs and liberates the translocation and folding apparatus under conditions that require envelope remodelling. The factors that facilitate the rapid removal of the unusually stable omp mRNAs in the ESR were previously unknown. We report that in Salmonella the ESR relies upon two highly conserved, σE‐controlled small non‐coding RNAs, RybB and MicA. By using a transcriptomic approach and kinetic analyses of target mRNA decay in vivo, RybB was identified as the factor that selectively accelerates the decay of multiple major omp mRNAs upon induction of the ESR, while MicA is proposed to facilitate rapid decay of the single ompA mRNA. In unstressed bacterial cells, the two σE‐dependent small RNAs function within a surveillance loop to maintain envelope homeostasis and to achieve autoregulation of σE.
The method for finding a reference point in fingerprint images basing on an analysis of characteristic points

This study presents a method for finding a reference point in fingerprint images. The proposed method is based on chains of points of a graphical objects and the IPAN99 algorithm. The IPAN99 algorithm was adjusted in the study to detect high curvature points on friction ridges. It allows locating a reference point on a fingerprint image. Since the IPAN99 algorithm requires that the thickness of an analysed contour should be of one pixel, each fingerprint image was adequately prepared before submitting to the analysis with the IPAN99 algorithm. Evaluation of the efficiency of the method consisted in comparing the distances between coordinates of reference points determined with the use of the proposed method and indicated by an expert. The developed method was compared with other algorithms used for determining a reference point.
A Unied Theory of Attentional Control

Although diverse, theories of visual attention generally share the notion that attention is controlled by some combination of three distinct strategies: (1) exogenous cueing from locallycontrasting primitive visual features, such as abrupt onsets or color singletons (e.g., Itti et al., 1998); (2) endogenous gain modulation of exogenous activations, used to guide attention to task relevant features (e.g. Navalpakkam and Itti, 2005; Wolfe, 1994, 2007); and (3) endogenous prediction of likely locations of interest, based on task and scene gist (e.g., Torralba, Oliva, Castelhano, and Henderson, 2006). We propose a unifying conceptualization in which attention is controlled along two dimensions: the degree of task focus and the spatial scale of operation. Previously proposed strategies—and their combinations—can be viewed as instances of this mechanism. Thus, this theory serves not as a replacement for existing models, but as a means of bringing them into a coherent framework. We implement this theory and demonstrate its applicability to a wide range of attentional phenomena. The model successfully yields the trends found in visual search tasks with synthetic images and makes predictions that correspond well with human eye movement data for tasks involving real-world images. In addition, the theory yields an unusual perspective on attention that places a fundamental emphasis on the role of experience and task-related knowledge.
Online learning for audio clustering and segmentation

Audio segmentation is an essential problem in many audio signal processing tasks which tries to segment an audio signal into homogeneous chunks, or segments. Most current approaches rely on a change-point detection phase for finding segment boundaries, followed by a similarity matching phase which identifies similar segments. In this thesis, we focus instead on joint segmentation and clustering algorithms which solve both tasks simultaneously, through the use of unsupervised learning techniques in sequential models. Hidden Markov and semi-Markov models are a natural choice for this modeling task, and we present their use in the context of audio segmentation. We then explore the use of online learning techniques in sequential models and their application to real-time audio segmentation tasks. We present an existing online EM algorithm for hidden Markov models and extend it to hidden semi-Markov models by introducing a different parameterization of semi-Markov chains. Finally, we develop new online learning algorithms for sequential models based on incremental optimization of surrogate functions.
Near Infrared Spectroscopy Study of the Frontopolar Hemodynamic Response and Depressive Mood in Children with Major Depressive Disorder: A Pilot Study

AIM The aim of this study was to evaluate the frontopolar hemodynamic response and depressive mood in children with mild or moderate major depressive disorder during six weeks treatment without medication. METHODS The subjects were 10 patients with mild or moderate depression. They were depressive drug-naive children and adolescents. The scores of Depression Self Rating Scale (DSRS), the results of the Verbal Fluency Test (VFT), and the concentrations of oxy-hemoglobin (Oxy-Hb) of frontal pole brain assessed by two-channel near infrared spectroscopy (NIRS) after six weeks of treatment was compared with those of initial treatment. RESULTS The score of DSRS was significantly reduced after six weeks of initial treatment (p<0.001, t-test). The word number of VFT was not significantly changed after six weeks of treatment. The oxy-Hb concentration significantly increased after six weeks of treatment (p<0.001, t-test). CONCLUSIONS This study demonstrated that the concentration of oxy-Hb of frontopolar cortex in children with mild and moderate depression improved along with their depressive mood. These results suggested that concentration of oxy-Hb using NIRS may be used as the state maker for change in depressive mood of children having depression, similar to that in adults.
Application of CMAC and FPGA to a twin rotor MIMO system

This paper utilizes a cerebellar model articulation controller (CMAC) to a MIMO control problem. The objective of this paper is to control a twin rotor MIMO system (TRMS) to move quickly and accurately to the desired attitudes, both the pitch angle and the azimuth angle in decoupled condition. A conventional CMAC and an adaptive CMAC are applied to control the TRMS in step response and sine wave response. For real-time control, Altera Nios II FPGA (Field Programmable Gate Array) is employed to construct a hardware-in-the-loop system through writing C and VHDL codes on this FPGA. Simulations show that the proposed CMAC controller can successfully control the TRMS to follow desired references.
I/O Feature-based File Prefetching for Multi-Applications

Applications such as multimedia, date mining- and scientific computing may exhibit sophisticated data access patterns that are different from one application to another. I/O prefetching is considered to be effective in improving I/O performance. However, the efficiency depends on identification of patterns for future I/O accesses, which is difficult to achieve in multi-application environment. This paper presents the design, implementation, and evaluation of an I/O feature-based prefetching strategy (FFP) for multiple application systems. The key idea of FFP is to extract I/O features from the collected file access information to guide prefetching. We first analyzed the I/O access patterns and designed a simple and effective feature notation to represent patterns. Then we developed a toolkit to generate and update I/O features automatically. Finally, we designed and implemented an I/O feature-based file prefetching thread library for the multi-application environment. Experimental results show that the proposed prefetching mechanism (FFP) improves I/O performance significantly for applications with complex I/O data access patterns.
CS 264 : Beyond Worst-Case Analysis Lecture # 20 : From Unknown Input Distributions to Instance Optimality ∗

For small α, this is the strongest possible and least controversial notion that one could hope for. Indeed, the guarantee is so strong that, for many problems, no instance-optimal algorithms exist (recall Homework #1). A relaxation of instance optimality that can enable meaningful positive results is to replace the condition “for every algorithm B” by “for every algorithm B belonging to a class C of ‘natural’ algorithms. ” Obviously, the strength of such a guarantee increases with the set C of “competing algorithms. ” ∗ c ©2014, Tim Roughgarden. †Department of Computer Science, Stanford University, 474 Gates Building, 353 Serra Mall, Stanford, CA 94305. Email: tim@cs.stanford. edu.
A case study of an intentional friendship between a volunteer and adult with severe intellectual disability: “My life is a lot richer!”

ABSTRACT Background Friendships between people with and without intellectual disability remain elusive. Little is known about factors that support the development of such friendships and what services can do to promote the likelihood that contact will develop into friendship. Method A case study approach was used to explore the qualities and development of a long-term friendship between 2 women, 1 of whom has severe intellectual disability. Qualitative methods of data collection and analysis were used including interviews and field notes from participant observation. Findings The relationship progressed through 3 stages of introduction, consolidation, and autonomy supported by the working practices and culture of the disability support organisation. Individualised activity, the role of a connector, and a culture of positive expectations underpinned the growth of the friendship. Conclusions Friendships do not happen by chance but require thought, attention, dedicated resources, and commitment to long-term outcomes to be achieved.
BiCMOS Implementation of Cell Signaling for Cell Survival/Death

: A well-structured and controlled design methodology, along with a supporting hierarchical design system, has been developed to optimally support the development effort on several programs requiring gate array and semi custom Very Large Scale Integration (VLSI) design. In this paper, we will present an application of VLSI in System Biology. This work examines signaling networks that control the survival decision treated with combinations of three primary signals the pro death cytokine, tumor necrosis factor-α (TNF), and the pro survival growth factors, epidermal growth factor (EGF) and insulin. We have made model by taking three inputs, than made the truth table, Boolean equation and than implement the equation using BiCMOS logic circuit.
Identifying Influential Nodes for Efficient Routing in Opportunistic Networks

—Opportunistic networks exploit human mobility and consequent device-to-device contacts to opportunistically create data paths over time. Identifying influential nodes as relay is a crucial problem for efficient routing in opportunistic networks. The degree centrality method is very simple but of little relevance. Although closeness centrality and betweenness centrality can effectively identify influential nodes, they are incapable to be applied in large-scale networks due to the high computational complexity. In this paper, we focus on designing an effective centrality ranking metric with low computational complexity in opportunistic networks. We propose the semi locally evaluated centrality metric to identify influential nodes for message forwarding in opportunistic networks. We also present a simple message forwarding algorithm, and employ real world mobility traces and synthetic mobility traces to evaluate the benefits of the proposed semi locally evaluated centrality metric. Results demonstrate the efficiency of the proposed metric in opportunistic networks.
Beyond clicks: dwell time for personalization

Many internet companies, such as Yahoo, Facebook, Google and Twitter, rely on content recommendation systems to deliver the most relevant content items to individual users through personalization. Delivering such personalized user experiences is believed to increase the long term engagement of users. While there has been a lot of progress in designing effective personalized recommender systems, by exploiting user interests and historical interaction data through implicit (item click) or explicit (item rating) feedback, directly optimizing for users' satisfaction with the system remains challenging. In this paper, we explore the idea of using item-level dwell time as a proxy to quantify how likely a content item is relevant to a particular user. We describe a novel method to compute accurate dwell time based on client-side and server-side logging and demonstrate how to normalize dwell time across different devices and contexts. In addition, we describe our experiments in incorporating dwell time into state-of-the-art learning to rank techniques and collaborative filtering models that obtain competitive performances in both offline and online settings.
Automatic Empirical Failure Avoidance for Concurrent Software

We propose Aviso, a system for avoiding failures due to concurrency errors. Aviso monitors program events during multithreaded execution. When a failure occurs, Aviso represents sequences of events from the failing execution as state-machines that we call SHIELDs. Aviso can use SHIELDs to avoid event sequences that lead to failures. After a failure, Aviso creates many candidate SHIELDs representing possible causes. Aviso then empirically vets them to determine which best avoid failures. We implemented Aviso and showed it increases reliability, and imposes low performance overhead (0-30%).
The Use of Word Sense Disambiguation in an Information Extraction System

This paper describes a rule-based methodology for word sense disambiguation and an application of the methodology to information extraction using rules generalized with the help of the WordNet system. The methodology creates word sense disambiguation rules based on user trained examples working in the domain of interest. It achieves accuracy rates comparable to the best competing methods and can be easily integrated into higher level applications.
SAVE: Energy-aware Virtual Data Center embedding and Traffic Engineering using SDN

Cloud computing is a popular computing paradigm which provides the virtual resource as a form of VM to customers in an on demand manner. Existing provisioning solutions were only limited to provision computing resource (e.g., CPU, RAM and etc.), therefore, it was difficult to provide more advanced cloud services. Virtual Data Center (VDC) embedding, which is known as mapping the VDC resources to their physical counterparts, was recently introduced to provide more advanced cloud services. However, existing VDC embedding solutions were mostly focus on consolidating VMs in single physical data center. Therefore, in this work, 1) we expand the consolidated targets from VMs to network fabrics (e.g, paths and switches); 2) we also consider the VDC embedding problem in multiple physical data centers. In former point, a Traffic Engineering (TE) technique is required to realize the network fabrics consolidation. While, in latter point, a multi-data center VM live migration technique is required to realize the VM migration across the different data centers. SDN, which is a new network paradigm, can fulfill the requirements raised in above two points by providing flow-level virtualization and network address virtualization. With the help of SDN, we propose a Sdn Assisted Vdc Embedding solution - SAVE by consolidating network fabrics along with physical hosts. SAVE includes two VDC embedding algorithms with a TE heuristic, and a holistic system architecture is provided to realize the proposed algorithms. Our experiment results show the feasibility of the SAVE system architecture, and the effectiveness of the proposed algorithms.
Real-Time Approximate Subsurface Scattering

A technique for forming and applying a coated and fused flock material on an article is disclosed wherein a coating material and the flock material filaments are fused together on a predetermined surface of a substrate in order to form a decorative, low-friction, or wear-resistant surface. The flock material may be secured to the substrate and the coating applied thereto before fusing the coating material and flock material filaments simultaneously. Alternatively, the flock material may be pre-fused prior to the application of the coating material, followed by a fusing of the coating material and the pre-fused flock material. The coating material is preferably a thermosetting resin, but a thermoplastic coating material may alternatively used.
Incorporating machine-learning into music similarity estimation

Music is a complex form of communication in which both artists and cultures express their ideas and identity. When we listen to music we do not simply perceive the acoustics of the sound in a temporal pattern, but also its relationship to other sounds, songs, artists, cultures and emotions. Owing to the complex, culturally-defined distribution of acoustic and temporal patterns amongst these relationships, it is unlikely that a general audio similarity metric will be suitable as a music similarity metric. Hence, we are unlikely to be able to emulate human perception of the similarity of songs without making reference to some historical or cultural context.The success of music classification systems, demonstrates that this difficulty can be overcome by learning the complex relationships between audio features and the metadata classes to be predicted. We present two approaches to the construction of music similarity metrics based on the use of a classification model to extract high-level descriptions of the music. These approaches achieve a very high-level of performance and do not produce the occasional spurious results or 'hubs' that conventional music similarity techniques produce.
Imaging Light Responses of Foveal Ganglion Cells in the Living Macaque Eye

The fovea dominates primate vision, and its anatomy and perceptual abilities are well studied, but its physiology has been little explored because of limitations of current physiological methods. In this study, we adapted a novel in vivo imaging method, originally developed in mouse retina, to explore foveal physiology in the macaque, which permits the repeated imaging of the functional response of many retinal ganglion cells (RGCs) simultaneously. A genetically encoded calcium indicator, G-CaMP5, was inserted into foveal RGCs, followed by calcium imaging of the displacement of foveal RGCs from their receptive fields, and their intensity-response functions. The spatial offset of foveal RGCs from their cone inputs makes this method especially appropriate for fovea by permitting imaging of RGC responses without excessive light adaptation of cones. This new method will permit the tracking of visual development, progression of retinal disease, or therapeutic interventions, such as insertion of visual prostheses.
How Is Nonverbal Auditory Information Processed? Revisiting Existing Models and Proposing a Preliminary Model

Use of multimodal displays is getting more prevalent in Human Factors and Human-Computer Interaction. Existing information processing models and theories predict the benefits of multimodality in user interfaces. While the models have been refined regarding vision, more granularity is still required regarding audition. The existing models mainly account for verbal processing in terms of representation, encoding, and retrieving, but these models do not provide sufficient explanations for nonverbal processing. In the present paper, I point out research gaps in nonverbal information processing of the representative models at the working memory and attention level. Then, I propose a preliminary conceptual model supported by neural and behavioral level evidence, and provide evaluations of the model and future works.
Feature Selection By KDDA For SVM-Based MultiView Face Recognition

Applications such as Face Recognition (FR) that deal with high-dimensional data need a mapping technique that introduces representation of low-dimensional features with enhanced discriminatory power and a proper classifier, able to classify those complex features . Most of traditional Linear Discriminant Analysis (LDA) suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accuracy is affected by the "small sample size" (SSS) problem which is often encountered in FR tasks. In this short paper, we combine nonlinear kernel based mapping of data called KDDA with Support Vector machine (SVM) classifier to deal with both of the shortcomings in an efficient and cost effective manner. The proposed here method is compared, in terms of classification accuracy, to other commonly used FR methods on UMIST face database. Results indicate that the performance of the proposed method is overall superior to those of traditional FR approaches, such as the Eigenfaces, Fisherfaces, and D-LDA methods and traditional linear classifiers.