Miracle-dz/UniFAR-FTUs huggingface dataset:
Convicted by memory: Automatically recovering spatial-temporal evidence from memory images

Saltaformaggio, Brendan D. Ph.D., Purdue University, December 2016. Convicted by Memory: Automatically Recovering Spatial-Temporal Evidence from Memory Images. Major Professors: Dongyan Xu and Xiangyu Zhang. Memory forensics can reveal “up to the minute” evidence of a device’s usage, often without requiring a suspect’s password to unlock the device, and it is oblivious to any persistent storage encryption schemes, e.g., whole disk encryption. Prior to my work, researchers and investigators alike considered data-structure recovery the ultimate goal of memory image forensics. This, however, was far from sufficient, as investigators were still largely unable to understand the content of the recovered evidence, and hence efficiently locating and accurately analyzing such evidence locked in memory images remained an open research challenge. In this dissertation, I propose breaking from traditional data-recovery-oriented forensics, and instead I present a memory forensics framework which leverages program analysis to automatically recover spatial-temporal evidence from memory images by understanding the programs that generated it. This framework consists of four techniques, each of which builds upon the discoveries of the previous, that represent this new paradigm of program-analysis-driven memory forensics. First, I present DSCRETE, a technique which reuses a program’s own interpretation and rendering logic to recover and present in-memory data structure contents. Following that, VCR developed vendor-generic data structure identification for the recovery of in-memory photographic evidence produced by an Android device’s cameras. GUITAR then realized an app-independent technique which automatically reassembles and redraws an app’s GUI from the multitude of GUI data elements found in a smartphone’s memory image. Finally, different from any traditional memory forensics technique,
Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons

The extraction of statistically independent components from high-dimensional multi-sensory input streams is assumed to be an essential component of sensory processing in the brain. Such independent component analysis (or blind source separation) could provide a less redundant representation of information about the external world. Another powerful processing strategy is to extract preferentially those components from high-dimensional input streams that are related to other information sources, such as internal predictions or proprioceptive feedback. This strategy allows the optimization of internal representation according to the information bottleneck method. However, concrete learning rules that implement these general unsupervised learning principles for spiking neurons are still missing. We show how both information bottleneck optimization and the extraction of independent components can in principle be implemented with stochastically spiking neurons with refractoriness. The new learning rule that achieves this is derived from abstract information optimization principles.
Learning from the Professions: Examining How, Why, and When Engineers Read and Write

Abstract With the advent of the Next Generation Science Standards in engineering (NGSS, 2013), teachers of multiple subject areas are being asked to do more than ever before—not only to teach engineering content in the K-12 classroom but also to engage students in authentic disciplinary reading and writing as part of content learning. These standards sound good, but they beg several questions; namely, what do we know about how, why, and when do engineers read and write as they do their work every day? What do teachers charged with engineering education know about the daily practices of engineers, let alone the literacy practices? In short, little is known about the literacy practices of engineers in the course of their daily work. This article draws on participant observation, interviews, and document analysis of one research engineering laboratory to illustrate the literacy practices of one group of engineers and begins to draw implications from this work for teacher practice in achieving the NGSS engineering education standards.
Visual Grasp Planning for Unknown Objects Using a Multifingered Robotic Hand

A method for fast visual grasping of unknown objects with a multifingered robotic hand is presented in this paper. The algorithm is composed of an object surface reconstruction algorithm and a local grasp planner, evolving in parallel. The reconstruction algorithm makes use of images taken by a camera carried by the robot arm. A virtual elastic reconstruction surface is placed around the object. The surface shrinks toward the object until some points intercept the object visual hull. Then, attractive forces with respect to the border of the visual hull are generated so as to compensate for the elastic forces: when an equilibrium between those forces is reached, the surface takes the form of the object shape. Running in parallel to the reconstruction algorithm, the grasp planner moves the fingertips on the current available reconstruction surface, toward points which are optimal (in a local sense) with respect to a number of indices weighting both the grasp quality and the kinematics configuration of the hand. This method, referred to as parallel visual grasp, may represent a valid candidate for applications where online grasp planning is required. A number of experiments are presented, showing the effectiveness of the proposed approach.
On Data Caching for Mobile Clouds

—Recent advances in smart device technologies have enabled a new computing paradigm in which large amounts of data are stored and processed on mobile devices. Despite the available powerful hardware, the actual capabilities of mobile devices are rather limited as they are often battery powered. This paper explores data caching for k -out-of- n computing in mobile cloud environments, with the goal of distributing data in a way that the expected future energy consumption for nodes to retrieve data is minimized, while preserving reliability. More speciﬁcally, we propose to place data caches (in addition to the originally stored data) based on the actual data access patterns and the network topology. Consequently, we formulate the cache placement optimization problem and propose a centralized caching framework that optimally solves the problem and a distributed solution that approximates the optimal solution. The distributed caching framework (DC) learns data access patterns by snifﬁng packets and informing a resident cache daemon about popular data items. Extensive evaluations are carried out through both simulations and a proof-of-concept hardware implementation. The results show that our proposed DC effectively improves the energy efﬁciency by up to 70% when compared with a no-caching framework, and even outperforms the centralized framework when taking the overhead into account.
Joint Mechanism That Mimics Elastic Characteristics in Human Running

Analysis of human running has revealed that the motion of the human leg can be modeled by a compression spring because the joints of the leg behave like a torsion spring in the stance phase. In this paper, we describe the development of a joint mechanism that mimics the elastic characteristics of the joints of the stance leg. The knee was equipped with a mechanism comprising two laminated leaf springs made of carbon fiber-reinforced plastic for adjusting the joint stiffness and a worm gear in order to achieve active movement. Using this mechanism, we were able to achieve joint stiffness mimicking that of a human knee joint that can be adjusted by varying the effective length of one of the laminated leaf springs. The equation proposed for calculating the joint stiffness considers the difference between the position of the fixed point of the leaf spring and the position of the rotational center of the joint. We evaluated the performance of the laminated leaf spring and the effectiveness of the proposed equation for joint stiffness. We were able to make a bipedal robot run with one leg using pelvic oscillation for storing energy produced by the resonance related to leg elasticity.
Magnetic Field Strengths in the Hot Spots and Lobes of Three Powerful Fanaroff-Riley Type II Radio Sources

We have made deep Chandra observations of three powerful Fanaroff-Riley type II (FR II) radio sources: two quasars (3C 263 and 3C 351) and one radio galaxy (3C 330). X-ray emission from hot spots and lobes, as well as from the active nucleus, is detected in each source. We model the hot spots' synchrotron spectra using VLA, BIMA, and Hubble Space Telescope data. In 3C 263 and 3C 330, the hot spots' X-ray emission is at a level consistent with being synchrotron self-Compton (SSC) emission, with a hot spot magnetic field close to the equipartition value. In the two hot spots of 3C 351, however, an SSC origin for the X-rays would require the magnetic field strength to be an order of magnitude below the equipartition value in our models; in addition, there are offsets between the radio, optical, and X-ray emission from the secondary hot spot that are hard to explain in a simple SSC model. We discuss the emission mechanisms that may be responsible for these observations. On our preferred model, the X-ray emission from the radio lobes of the three sources is due to inverse Compton scattering of the microwave background radiation. If this is the case, the magnetic field strengths in the lobes are typically about a factor of 2 below the equipartition values, assuming uniform lobe electron and magnetic field distributions. We detect extended X-ray emission, which we attribute to a cluster/group environment, around 3C 263 and 3C 330. This detection allows us to show that the lobes are close to pressure balance with their surroundings, as long as no nonradiating particles contribute to the internal pressure of the lobes.
Distributed Blind Calibration via Output Synchronization in Lossy Sensor Networks

In this paper a novel distributed algorithm for blind macro calibration in sensor networks based on output synchronization is proposed. The algorithm is formulated as a set of gradient-type recursions for estimating parameters of sensor calibration functions, starting from local criteria defined as weighted sums of mean square differences between the outputs of neighboring sensors. It is proved, on the basis of an originally developed methodology for treating higher-order consensus (or output synchronization) schemes, that the algorithm achieves asymptotic agreement for sensor gains and offsets, in the mean square sense and with probability one. In the case of additive measurement noise, additive inter-agent communication noise, and communication outages, a modification of the original algorithm based on instrumental variables is proposed. It is proved using stochastic approximation arguments that the modified algorithm achieves asymptotic consensus for sensor gains and offsets, in the mean square sense and with probability one. Special attention is paid to the situation when a subset of sensors in the network remains with fixed characteristics. Illustrative simulation examples are provided.
Pre-rejection of distorted speech for speech recognition in wireless communication channel

This paper introduces a pre-rejection technique for wireless channel distorted speech with application to automatic speech recognition (ASR). We investigate the characteristics of distorted speech over wireless communication channel with a survey on the physical phenomena of the propagation channel. Based on the analysis of the channel distorted speech signal, we propose a method to reject the channel distorted speech with a small computational load. From a number of simulation results, we can discover that the pre-rejection algorithm enhances the robustness of speech recognition operation.
Screening of Environmental DNA Libraries for the Presence of Genes Conferring Lipolytic Activity onEscherichia coli

ABSTRACT Environmental DNA libraries prepared from three different soil samples were screened for genes conferring lipolytic activity onEscherichia coli clones. Screening on triolein agar revealed 1 positive clone out of 730,000 clones, and screening on tributyrin agar revealed 3 positive clones out of 286,000 E. coli clones. Substrate specificity analysis revealed that one recombinant strain harbored a lipase and the other three contained esterases. The genes responsible for the lipolytic activity were identified and characterized.
Relation between Deterministic Boltzmann Machine Learning and Neural Properties

The Inverse Delayed (ID) model is a novel neural network system, which has been proposed by Prof. Nakajima et al. However, the learning method of the ID-model, which is an important feature of the neural network model, has not yet been developed. The ID model can be used for various networks, but why it is interesting for the Deterministic Boltzmann Machine (DBM) is because it is relatively suitable for realising a compact neurochip. DBM has proven to be much faster than the original Boltzmann Machine on some problems. Interest lies however in making it even faster. By changing DBM:s classical structure and let it be based on the ID model, this was investigated. The properties of the ID model, such as destabilisation of states and a speedup effect of state changes, due to negative resistance, bring hope that learning will become faster. In order to evaluate its performance, the XOR function was used and the results from the different network structures were compared. The performance of the networks was investigated with various network sizes and activation functions and the result was evaluated in terms of convergence likelihood but mainly by time span until stability. The ID model with negative resistance proved to be more successful than the non-negative resistance version and the classical DBM. Further two cases of the ID model with negative resistance were evaluated, where neurons with single well potential made learning faster than neurons with double well potential. Sammanfattning ID-modellen, som har föreslagits av Professor Nakajima m.fl. , är en ny modell för artificiella neuronnät, men inlärningsmetoden som är en viktig del av modellen, har ännu inte utvecklats. ID-modellen kan användas för olika nätverk, men varför den är intressant för den Deterministiska Boltzmann Maskinen (DBM), är för att DBM är relativt användbar vid konstruktion av neurochip. Den har dessutom visat sig vara mycket snabbare än den ursprungliga Boltzmann Maskinen på en del problem. Intresse ligger dock i att göra den ännu snabbare. Genom att ändra DBM:s klassiska struktur och låta den baseras på ID-modellen, undersöktes detta. ID-modellens egenskaper, såsom att snabbare kunna förändra tillstånd och även kunna göra stabila tillstånd instabila, på grund av negativ resistans, ger hopp om att inlärningen kommer att snabbas upp. För att nätverkets prestation skulle kunna utvärderas, användes XOR-funktionen och resultat från olika nätverksstrukturer jämfördes. Nätverkets prestation för olika storlekar på nätverket och olika aktiveringsfunktioner undersöktes och resultatet utvärderades i termer av sannolikhet för konvergens men huvudsakligen i tidsspann innan stabilisering. ID-modellen med negativ resistans visade sig vara mer framgångsrik än samma modell utan negativ resistans och den klassiska DBM. Därtill undersöktes två olika fall av ID-modellen, där neuroner med en potentialfunktion med utseendet av en enkel brunn hade snabbare inlärning än neuroner med potential av typ dubbelbrunn.
Towards a Mapping from ERDF(S) to Take Vocabulary

A digital system simulation apparatus and method enable a user to interactively control, during simulation, the sampling of signals in the digital system whose behavior is being simulated. The behavior of the system can be approximated better by recognizing and allowing a precise definition of element outputs even though the element inputs can include undefined variables. The apparatus allows efficient and better utilization of system memories by providing the capability of defining multiple outputs of the circuit elements in a single instruction when those outputs all have common inputs.
Secure Neighbor Discovery in Wireless Sensor Networks

Wireless Sensor Networks are increasingly being used for data monitoring in commercial, industrial, and military applications. Security is of great concern from many different viewpoints: ensuring that sensitive data does not fall into wrong hands; ensuring that the received data has not been doctored; and ensuring that the network is resilient to denial of service attacks. We study the fundamental problem of Secure Neighbor Discovery problem, which is critical to protecting the network against a number of different forms of attacks. Sensor networks, deployed in hazardous environment, are exposed to a variety of attacks like eavesdropping, message tampering, selective forwarding, wormhole and sybil attacks. Attacks against the data traffic can be addressed using cryptographic techniques. We first present an efficient and scalable key-distribution protocol which is completely secure in the absence of colluding malicious nodes. Secure neighbor discovery can help to defend against a majority of the attacks against control traffic. We consider a static network and propose a secure one-hop neighbor discovery protocol. We show by analysis that this protocol effectively prevents two non-neighboring nodes from becoming neighbors even when both the nodes have been compromised by the adversary. We then extend this protocol so that it works even when nodes are incrementally deployed in the network. We also briefly study how this protocol could be modified for mobile sensor networks. Finally, we compare our protocol with existing neighbor discovery protocols and analyze the advantages and disadvantages of using these protocols.
Quantum algorithms to check Resiliency , Symmetry and Linearity of a Boolean function

In this paper, we present related quantum algorithms to check the order of resiliency, symmetry and linearity of a Boolean function that is available as a black-box (oracle). First we consider resiliency and show that the Deutsch-Jozsa algorithm can be immediately used for this purpose. We also point out how the quadratic improvement in query complexity can be obtained over the DeutschJozsa algorithm for this purpose using the Grover’s technique. While the worst case quantum query complexity to check the resiliency order is exponential in the number of input variables of the Boolean function, we require polynomially many measurements only. We also describe a subset of n-variable Boolean functions for which the algorithm works in polynomially many steps, i.e., we can achieve an exponential speed-up over best known classical algorithms. A similar kind of approach can be exploited to check whether a Boolean function is symmetric (respectively linear) or not. Given a Boolean function as an oracle, it is important to devise certain algorithms to test whether it has a specific property or it is -far from having that property. The efficiency of the algorithm is judged by the number of calls to the oracle so that one can decide, with high probability, between these two alternatives. We show that this can be achieved in O( − 1 2 ) query complexity. This is obtained by showing that the problem of checking symmetry or linearity can be efficiently reduced to testing whether a Boolean function is constant. This is a thoroughly revised and extended version of the paper “Quantum algorithms to check Resiliency of a Boolean function” by Kaushik Chakraborty and Subhamoy Maitra that has been presented in WCC 2013, April 15-19, 2013, Bergen, Norway. Sections 3, 4 contain additional materials over the workshop version. Kaushik Chakraborty Indian Statistical Institute, Kolkata 700 108, India E-mail: kaushik.chakraborty9@gmail.com Anupam Chattopadhyay UMIC Research Centre, RWTH Aachen University, Aachen, Germany E-mail: anupam@umic.rwth-aachen.de Subhamoy Maitra Applied Statistics Unit, Indian Statistical Institute, Kolkata 700 108, India E-mail: subho@isical.ac. in 2 Chakraborty, Chattopadhyay & Maitra
Dynamics of cognitive change in impaired HIV-positive patients initiating antiretroviral therapy

Objective: To rigorously evaluate the time course of cognitive change in a cohort of individuals with HIV-associated neurocognitive disorders (HAND) initiating combination antiretroviral therapy (CART), and to investigate which demographic, laboratory, and treatment factors are associated with neuropsychological (NP) outcome (or “any NP improvement”). Methods: Study participants included 37 HIV+ individuals with mild to moderate NP impairment who initiated CART and underwent NP testing at 12, 24, 36, and 48 weeks thereafter. NP change was assessed using a regression-based change score that was normed on a separate NP-stable group thereby controlling for regression toward the mean and practice effect. Mixed-effect regression models adjusting for loss to follow-up were used to evaluate the time course of cognitive change and its association with baseline and time-varying predictors. Results: In persons with HAND initiating CART, cognitive improvement happens soon after initiation (13% at week 12), but more often 24, 36, and up to 48 weeks after initiation (up to 41%), with fewer than 5% demonstrating significant worsening. In multivariate analyses, unique predictors of NP improvement included more severe baseline NP impairment and higher CART CNS penetration index. Greater viral load decrease was associated with NP improvement only in univariate analyses. Conclusion: Clinically meaningful neuropsychological improvement seemed to peak around 24–36 weeks after combination antiretroviral therapy initiation and was prolonged over the 1-year study period. This study also provides new evidence that benefit may be maximized by choosing antiretroviral medications that reach therapeutic concentrations in the CNS.
The role of the left inferior parietal lobule in reading.

One of the regions that have consistently been included in the neurological models of reading is the left inferior parietal lobule (IPL), however, the precise functional and temporal contributions of this region to reading have not yet been fully established. There are three hypotheses concerning IPL contributions to visual word recognition. The first one claims that the IPL is the site of stored visual word forms although it remains unclear whether these are stored in supramarginal (SMG) or angular (ANG) fields of the IPL. The second hypothesis argues that the procedures for converting spelling-to-sound are a function of the IPL, but it is unclear whether these are specifically located in SMG or ANG, or both. Finally, a third hypothesis suggests that SMG and ANG preferentially contribute to phonological and semantic processing of written words, respectively. In this thesis, I empirically evaluated these hypotheses using repetitive transcranial magnetic stimulation (rTMS) to temporarily and selectively disrupt processing in left SMG and ANG during visual word recognition and measure the effect on reading behaviour. I also investigated the time course of SMG and ANG involvement to visual word recognition using double-pulse TMS. My research demonstrates that SMG contributes preferentially to phonological aspects of word processing and the processing begins early and over a sustained period of time (between 80 to 200 msec post-stimulus onset). ANG contributes preferentially to semantic aspects of word processing but the temporal dynamics of this contribution were not successfully revealed in this thesis and require further investigation. In addition, I empirically evaluated the efficiency of using functional magnetic resonance (fMRI) and TMS to functionally localize a target site for TMS experiments. I demonstrated that both methods are similarly accurate in identifying stimulation site but neither of them is 100% accurate.