Miracle-dz/UniFAR-FTUs huggingface dataset:
Research Statement Factor Graphs for Relational Regression

I am interested in Machine Learning and Pattern Recognition. My research focuses on understanding the rich contextual information associated with most data sets in a variety of real world domains and using it to infer complex patterns. To this end, I have worked on designing efficient inference and learning algorithms for models capable of handling the uncertainties and interdependencies among samples associated with large scale data sets. The class of models that I have explored is highly diverse, including Energy Based Models, Graphical Models, Deep Learning Architectures, and Relational Graphical Models. Furthermore, I have applied these models to real world problems in a variety of domains, such as in economics for predicting the real estate prices, in computer vision for object recognition, face verification, distance metric learning and data visualization, and in robotics for autonomous navigation. The importance of contextual information has been well acknowledged in the fields of human and machine vision. For instance, while detecting a car in an image, it is beneficial to use the knowledge that the car is usually parked on the road and sky is always above the ground. Context is equally essential for reasoning in a variety of other domains. Consider one such example where our approach improves substantially over the existing ones: real estate price prediction. The price of a house clearly depends on its individual features, such as the number of bedrooms – the intrinsic price. However, the price also depends on other factors, such as the quality or the desirability of the neighboring houses (the spatial context), and inflation (the temporal context). We capture this spatial context by learning a smooth latent “desirability” manifold over the neighborhood. The value at any point on the manifold can be interpreted as the desirability of that location. When it is combined with the intrinsic price, we get the actual price estimate of the house. The details of this application and the related general framework are discussed below. More generally, in such domains the notion of context is abstracted by the complex interdependencies among the samples. These interactions between samples often exhibit an underlying logical structure. I aim to capture this idea of context in different domains, to enable robust reasoning about a diverse set of complex real world phenomenon. Another dimension of my research involves answering questions which go beyond the simple “yes/no” questions (classification), or go beyond predicting the value of a function using only the individual features of an input sample (regression). For example, in addition to knowing whether a car is present in an image, it would be interesting to infer other objects and their respective positions. Similarly, instead of just predicting the price of a house, understanding how the economic properties of the entire neighborhood will change over time will be far more useful.
Speculative disassembly of binary code

Embedded software is rapidly increasing in complexity. To cope with this, developers rely on third-party IPs to accelerate product delivery. However, IP source code might not be available which limits verifiability. This creates a particular challenge especially in safety-critical applications, e.g., automotive. Static Binary Analysis (SBA) is a promising technique to address such a challenge by providing engineers with the ability to reason about the actual instructions executed for all possible inputs. Disassembly is the fundamental first step for any SBA where assembly instructions are recovered from binary code. Correct disassembly, however, is challenging since data is mixed with code in binaries. Moreover, variable-size ISA, e.g., Thumb and TriCore, allow a single byte sequence to have multiple valid interpretations. We introduce Spedi, an open source SPEculative DIsassembler for Thumb ISA. Spedi is based on a principled approach to disassembly where all possible basic blocks are speculatively recovered. Then, basic blocks are refined using conflict analyses to identify assembly instructions. Experiments using a wide range of benchmarks demonstrate that Spedi is both fast and effective. It outperforms IDA Pro, the de-facto industry standard disassembler, in terms of disassembly correctness. Spedi can also recover the majority of the call graph and switch table targets. It is resilient to obfuscation and doesn't use any symbol information which makes it a suitable front-end for a wide variety of SBA applications including security analysis.
Model inference and security testing in the spacios project

The SPaCIoS project has as goal the validation and testing of security properties of services and web applications. It proposes a methodology and tool collection centered around models described in a dedicated specification language, supporting model inference, mutation-based testing, and model checking. The project has developed two approaches to reverse engineer models from implementations. One is based on remote interaction (typically through an HTTP connection) to observe the runtime behaviour and infer a model in black-box mode. The other is based on analysis of application code when available. This paper presents the reverse engineering parts of the project, along with an illustration of how vulnerabilities can be found with various SPaCIoS tool components on a typical security benchmark.
From to ISOTiger – Community Driven Developments for Syntax Annotation in SynAF

In 2010, ISO published a standard for syntactic annotation, ISO 24615:2010 (SynAF). Back then, the document specified a comprehensive reference model for the representation of syntactic annotations, but no accompanying XML serialisation. ISO's subcommittee on language resource management (ISO TC 37/SC 4) is working on making the SynAF serialisation ISOTiger an ad-ditional part of the standard. This contribution addresses the current state of development of ISOTiger, along with a number of open issues on which we are seeking community feedback in order to ensure that ISOTiger becomes a useful extension to the SynAF reference model.
Low peak bone mass and attenuated anabolic response to parathyroid hormone in mice with an osteoblast-specific deletion of connexin43

Connexin43 (Cx43) is involved in bone development, but its role in adult bone homeostasis remains unknown. To overcome the postnatal lethality of Cx43 null mutation, we generated mice with selective osteoblast ablation of Cx43, obtained using a Cx43fl allele and a 2.3-kb fragment of the α1(I) collagen promoter to drive Cre in osteoblasts (ColCre). Conditionally osteoblast-deleted ColCre;Cx43–/fl mice show no malformations at birth, but develop low peak bone mass and remain osteopenic with age, exhibiting reduced bone formation and defective osteoblast function. By both radiodensitometry and histology, bone mineral content increased rapidly and progressively in adult Cx43+/fl mice after subcutaneous injection of parathyroid hormone (PTH), an effect significantly attenuated in ColCre;Cx43–/fl mice, with Cx43–/fl exhibiting an intermediate response. Attenuation of PTH anabolic action was associated with failure to increase mineral apposition rate in response to PTH in ColCre;Cx43–/fl, despite an increased osteoblast number, suggesting a functional defect in Cx43-deficient bone-forming cells. In conclusion, lack of Cx43 in osteoblasts leads to suboptimal acquisition of peak bone mass, and hinders the bone anabolic effect of PTH. Cx43 represents a potential target for modulation of bone anabolism.
GMM-Based KLT-Domain Switched-Split Vector Quantization for LSF Coding

For quantization of line spectral frequency (LSF), Gaussian mixture model (GMM) based switched split vector quantization (SSVQ) has been reported as the best performing intra-frame coding method. However, GMM-SSVQ partly recovers correlations between the subvectors of split vector quantization (SVQ). In the proposed GMM-SSVQ with the Karhunen-Loève Transform (KLT), KLT-domain quantization for each mixture with a novel region-clustering algorithm is applied to GMM-SSVQ. Compared with SVQ and GMM-SSVQ, it provides 4 and 1 bit higher performance in terms of average spectral distortion and outliers, respectively. Computational complexity and memory requirements are similar to GMM-SSVQ.
Variability-aware parsing in the presence of lexical macros and conditional compilation

In many projects, lexical preprocessors are used to manage different variants of the project (using conditional compilation) and to define compile-time code transformations (using macros). Unfortunately, while being a simple way to implement variability, conditional compilation and lexical macros hinder automatic analysis, even though such analysis is urgently needed to combat variability-induced complexity. To analyze code with its variability, we need to parse it without preprocessing it. However, current parsing solutions use unsound heuristics, support only a subset of the language, or suffer from exponential explosion. As part of the TypeChef project, we contribute a novel variability-aware parser that can parse almost all unpreprocessed code without heuristics in practicable time. Beyond the obvious task of detecting syntax errors, our parser paves the road for further analysis, such as variability-aware type checking. We implement variability-aware parsers for Java and GNU C and demonstrate practicability by parsing the product line MobileMedia and the entire X86 architecture of the Linux kernel with 6065 variable features.
Outsourcing and competence hollowing-out: Systems integrator vs. knowledge integrator?

The paper analyses the organization of the new product development process at FIAT from a resource-based perspective. The focus is on organizational resources for integrating dispersed specialist knowledge required in the development of complex products. The analysis shows how applying a resource-based perspective is able to uncover negative long-term effects of outsourcing on the competence base, despite beneficial short-term effects on cost. The results question whether to be a successful systems integrator also requires the complementary capability to be a knowledge-integrator.
XML Retrieval : A Survey

Rapid advances in electronic and computer technology have ushered in what we call an age of information. The exponential growth of the Internet and the Web has flooded us with huge quantities of data in different formats on a variety of subjects. To manage this colossal amount of data and extract useful information out of it, we need efficient and effective means of organizing and indexing the data. Though these data are available in different forms and the amount of available multimedia data (images, speech, audio, video etc) is rapidly increasing, textual data continues to be a fundamental and very widely prevalent form of storing information. Textual information can be broadly classified into three categories based on their structure : (i) unstructured data (ii) structured data and (iii) semi-structured data.
A large planar camera array for multiple automated guided vehicles localization

In this work, we create a large planar camera array in our intelligent environment. It extremely extends the scope of surveillance area, and more precisely localizes robots with high response speed for AGV system. In this camera array system, color based particle filter tracking algorithm efficiently performs the task of localizing and identifying robots. The tracking algorithm intelligently controls the camera array and significantly reduces the redundancy of image data to be processed. Therefore, the whole camera array can be controlled by one normal PC rather than the expensive and powerful computer cluster or embedding the micro-processor into cameras. An expansion resetting of particle filter makes our system also can automatically re-calibrate cameras if the pose of camera changes during an accident. Experiments show the camera array consisting of 15 cameras can localize 5 robots with 18 Hz~54 Hz.
Using the Knowledge Base on Middle Schools and Leadership to Improve the Quality of Young Adolescent Learning

The research is very consistent in showing that highly successful schools are led by strong, competent leaders (Clark & Clark, 2004; Jackson & Davis, 2000; Useem, Christman, Gold, & Simon, 1997; Valentine, Clark, Hackmann, & Petzko, 2004; Valentine, Trimble, & Whitaker, 1997). In fact, Leithwood and Riehl (2003), in a comprehensive review of leadership research and practice, found that school leadership had significant effects on student learning. They concluded that leadership effects were second only to the effects of the quality of curriculum and teachers' instruction.
Short Communication: TopoToolbox 2 – MATLAB-based software for topographic analysis and modeling in Earth surface sciences

Abstract. TopoToolbox is a MATLAB program for the analysis of digital elevation models (DEMs). With the release of version 2, the software adopts an object-oriented programming (OOP) approach to work with gridded DEMs and derived data such as flow directions and stream networks. The introduction of a novel technique to store flow directions as topologically ordered vectors of indices enables calculation of flow-related attributes such as flow accumulation ∼20 times faster than conventional algorithms while at the same time reducing memory overhead to 33% of that required by the previous version. Graphical user interfaces (GUIs) enable visual exploration and interaction with DEMs and derivatives and provide access to tools targeted at fluvial and tectonic geomorphologists. With its new release, TopoToolbox has become a more memory-efficient and faster tool for basic and advanced digital terrain analysis that can be used as a framework for building hydrological and geomorphological models in MATLAB.
Distributed Optimization in an Energy-Constrained Network

We consider a distributed optimization problem whereby two nodes Si, S2 wish to jointly minimize a common convex quadratic cost function f(x<sub>1</sub>,x<sub>2</sub>), subject to separate local constraints on x<sub>1</sub> and x<sub>2</sub>, respectively. Suppose that node S<sub>1</sub> has control of variable x<sub>1</sub> only and node S<sub>2</sub> has control of variable x<sub>2</sub> only. The two nodes locally update their respective variables and periodically exchange their values over a noisy channel. Previous studies of this problem have mainly focused on the convergence issue and the analysis of convergence rate. In this work, we focus on the communication energy and study its impact on convergence. In particular, we consider a class of distributed stochastic gradient type algorithms implemented using certain linear analog messaging schemes. We study the minimum amount of communication energy required for the two nodes to compute an ∈-minimizer of f(x<sub>1</sub>,x<sub>2</sub>) in the mean square sense. Our analysis shows that the communication energy must grow at least at the rate of Ω(∈<sup>-1</sup>). We also derive specific designs which attain this minimum energy bound, and provide simulation results that confirm our theoretical analysis. Extension to the multiple node case is described.
A safety message broadcasting scheme with QoS guarantee for VANET

Reliable and rapid network-wide safety message broadcasting is critical for safe driving and smart navigation, which would avoid instantaneous large-scale traffic congestion caused by vehicle accidents. Traditional network packet flooding algorithm would easily cause broadcast-storm, which leads to network congestion and increases transmission delay. Node location information and queue mechanism based on traditional flooding algorithm is introduced in this paper to present a new algorithm with Safety Message Broadcasting Scheme (SMBS) and QoS guarantee. In multi-application networks, SMBS would give priority to safety message and broadcast the message, which could ensure safety service quality. Analysis and evaluations show that SMBS is much more efficient than the traditional flooding scheme.
Genetic partitioning of interleukin-6 signalling in mice dissociates Stat3 from Smad3-mediated lung fibrosis

Idiopathic pulmonary fibrosis (IPF) is a fatal disease that is unresponsive to current therapies and characterized by excessive collagen deposition and subsequent fibrosis. While inflammatory cytokines, including interleukin (IL)‐6, are elevated in IPF, the molecular mechanisms that underlie this disease are incompletely understood, although the development of fibrosis is believed to depend on canonical transforming growth factor (TGF)‐β signalling. We examined bleomycin‐induced inflammation and fibrosis in mice carrying a mutation in the shared IL‐6 family receptor gp130. Using genetic complementation, we directly correlate the extent of IL‐6‐mediated, excessive Stat3 activity with inflammatory infiltrates in the lung and the severity of fibrosis in corresponding gp130757F mice. The extent of fibrosis was attenuated in B lymphocyte‐deficient gp130757F;µMT−/− compound mutant mice, but fibrosis still occurred in their Smad3−/− counterparts consistent with the capacity of excessive Stat3 activity to induce collagen 1α1 gene transcription independently of canonical TGF‐β/Smad3 signalling. These findings are of therapeutic relevance, since we confirmed abundant STAT3 activation in fibrotic lungs from IPF patients and showed that genetic reduction of Stat3 protected mice from bleomycin‐induced lung fibrosis.
Effects of Intervertebral Disc Degeneration on Biomechanical Behavior of the L 4L 5 Lumbar Functional Spinal Unit

The consideration of biomechanical alterations due to intervertebral disc (IVD) degeneration is crucial for the accurate analysis of spine biomechanics. In this study, finite element (FE) models of the L4-L5 functional spinal unit with full coverage of the degeneration grades from healthy IVD to severe degeneration were developed. The effects of IVD degeneration on spine biomechanics were analyzed under physiological loading conditions using compressive forces and bending moments. Severe IVD degeneration showed lower inter-segmental rotations in flexion-extension and lateral bending, lower intradiscal pressure in all motions and higher facet joint forces in lateral bending and axial rotation in all motions versus the healthy IVD. These findings could provide fundamental information for understanding the characteristics of the biomechanical behavior of the degenerated lumbar spine.
Efficient Sampling of Disparity Space for Fast And Accurate Matching

A simple stereo matching algorithm is proposed that visits only a small fraction of disparity space in order to find a semi-dense disparity map. It works by growing from a small set of correspondence seeds. Unlike in known seed-growing algorithms, it guarantees matching accuracy and correctness, even in the presence of repetitive patterns. This success is based on the fact it solves a global optimization task. The algorithm can recover from wrong initial seeds to the extent they can even be random. The quality of correspondence seeds influences computing time, not the quality of the final disparity map. We show that the proposed algorithm achieves similar results as an exhaustive disparity space search but it is two orders of magnitude faster. This is very unlike the existing growing algorithms which are fast but erroneous. Accurate matching on 2-megapixel images of complex scenes is routinely obtained in a few seconds on a common PC from a small number of seeds, without limiting the disparity search range.
Global Transcriptional Response to Hfe Deficiency and Dietary Iron Overload in Mouse Liver and Duodenum

Iron is an essential trace element whose absorption is usually tightly regulated in the duodenum. HFE-related hereditary hemochromatosis (HH) is characterized by abnormally low expression of the iron-regulatory hormone, hepcidin, which results in increased iron absorption. The liver is crucial for iron homeostasis as it is the main production site of hepcidin. The aim of this study was to explore and compare the genome-wide transcriptome response to Hfe deficiency and dietary iron overload in murine liver and duodenum. Illumina™ arrays containing over 47,000 probes were used to study global transcriptional changes. Quantitative RT-PCR (Q-RT-PCR) was used to validate the microarray results. In the liver, the expression of 151 genes was altered in Hfe−/− mice while dietary iron overload changed the expression of 218 genes. There were 173 and 108 differentially expressed genes in the duodenum of Hfe−/− mice and mice with dietary iron overload, respectively. There was 93.5% concordance between the results obtained by microarray analysis and Q-RT-PCR. Overexpression of genes for acute phase reactants in the liver and a strong induction of digestive enzyme genes in the duodenum were characteristic of the Hfe-deficient genotype. In contrast, dietary iron overload caused a more pronounced change of gene expression responsive to oxidative stress. In conclusion, Hfe deficiency caused a previously unrecognized increase in gene expression of hepatic acute phase proteins and duodenal digestive enzymes.
Oving K-Means Clustering using discretization technique in Network Intrusion Detection System

Network Intrusion Detection Systems (NIDSs) have always been designed to enhance and improve the network security issue by detecting, identifying, assessing and reporting any unauthorized and illegal network connections and activities. The purpose of this research is to improve on the existing Anomaly Based Intrusion Detection (ABID) method using K-Means clustering technique as to maximize the detection rate and accuracy while minimizing the false alarm. The problem with outliers may disturb the K-Means clustering process as it might be avoided in the clustering process from mixing with the normal data that make the NIDSs become less accurate. Thus this research aims to improve the performance of the ABID systems that balance the loss of information or ignored data in clustering. An integrated machine learning algorithm using K-Means Clustering with discretization technique and Naïve Bayes Classifier (KMC-D+NBC) is proposed against ISCX 2012 Intrusion Detection Evaluation Dataset. The outcome depicts that the proposed method generates better detection rate and accuracy up to 99.3% and 99.5% respectively and reduces the false alarm to 1.2% with better efficiency of 0.03 seconds time taken to build model.
Cagra Dependable Distributed Storage System for 3D Computer Graphics Rendering

Cagra is a distributed storage system optimized for distributed rendering. Enterprise-class distributed rendering requires computers with considerable storage space and computational power. An single frame in an average 3D CG movie requires approximately 1 GB to 20 GB of input data and takes 3 to 10h to render. The large-scale computations in rendering are carried out by specialized clusters, or render farms. However, a centralized storage system is used for storing input data. Cagra is designed as a lightweight distributed key-value store optimized for use in a rendering environment. In this paper, we discuss the design and implementation of Cagra and its various features such as high dependability, dynamic node insertion, and zero configuration. We also discuss details of its optimization for rendering in order to increase the rendering throughput.