Miracle-dz/UniFAR-FTUs huggingface dataset:
Software Vulnerability Design and Approaches for Securing SCADA Control Systems

Despite growing awareness of security issues especially in SCADA networks, there exist little or scarce information about SCADA vulnerabilities and attacks. Where security has been a consideration, there has been no clear methodology to assess the security impacts brought about by attacks. Worst, there have been no or very little security tools that have been released publicly. This research aims to addresses the issues regarding security and vulnerability testing. Software program was created to simulate the vulnerability testing and carry out assessment methodologies to test existing SCADA software design and implementation. This paper also describes the application of a well know security testing approach known as the software implemented fault injection as well as building the model for security vulnerabilities identification and analysis. Impact analysis was also performed to provide a better understanding of the attacks. Sufficient security measures are needed to block all possible intrusion points into the SCADA system to significantly reduce the chances of a successful
Video Optimization through Hybrid FQ-Codel Algorithm

Video data forms the major traffic in today’s internet. Quality Of internet video has a great impact on user engagement. Video encoding and streaming over wireless networks become a big concern. Real time video service requires low end-to-end delay, which mainly consists of video encoding delay, queuing delay, and transmission delay. The major concern is to reduce the queuing delay which has high impact on the video quality. Bufferbloat is one of the main reasons for experiencing high queue latency at the intermediate nodes. Several AQM techniques were used in reducing queue latency, but optimization of buffer size is never achieved. In this work, Hybrid FQ-CoDel mechanism is proposed which is the combination of both CoDel and Adaptive CoDel AQM mechanism with fair queuing for the reduction of queuing delay and to optimize video transmission. Here the Adaptive CoDel is used for video traffic whereas the CoDel is used for other network flow. The fairness in the network queuing is achieved through the mechanisms of network classifier and the scheduler. Hybrid FQ-CoDeL is used for effective reduction of the queuing delay and to increase the network bandwidth utilization for video streams which will result in better quality of video.
When are Minorities Worse Off? A Systematic Investigation of Size and Status

ABSTRACT Are smaller ethnic groups less advantaged than large groups? This question has not been systematically studied. Using two new datasets, we find that when group size and status are analyzed at national levels smaller groups are generally worse off than larger groups. By contrast, when group size and status are analyzed at subnational (regional or district) levels, smaller groups are better off than larger groups. “National” minorities are disadvantaged while “local” minorities are advantaged.We theorize that two factors are at work in generating this surprisingly consistent relationship. First, a synergy exists at national levels among three features of ethnic groups: size, power, and status. The second factor is based on social dynamics. Specifically, insofar as internal migration is characterized by positive selection, then migrants and their descendants should form the basis of small, privileged groups within the region that they migrate to. Insofar as distance enhances positive selection, this explains why smaller migrations are associated with more privileged groups and larger migrations with somewhat less privileged groups.
Performance Visualization of Parallel Programs - The PARvis Environment

PARvis is a visualization environment which translates a given trace le into a variety of graphical views, e.g., state diagrams, activity charts, time-line displays, and statistics. On the Intel Paragon, PARvis takes the ipd-generated trace le and extracts the graphical information automatically; there is no need to change the source code in any way. Furthermore, debug-mode compilation is not required. PARvis supports an animation mode that can help to locate performance bottlenecks, and it provides exible lter operations to reduce the amount of information displayed. Unlike most other tools used in this area, it has a powerful zooming feature that allows the user to identify problems at any level of detail.
Reducing Energy Consumption in Smart Cities: A Scatter Search Based Approach

This article proposes a conceptual approach for reducing carbon emissions related to the context of smart cities. In fact, we focus on the possibility of using small driverless electric vehicles for moving jointly persons and goods in urban areas. Next, we propose a heuristic approach related to our context in order to minimize energy consumption within the context of smart cities. An optimization problem based to the operational level of decisions is formulated and a scatter search algorithm is implemented to solve the problem. Our results show the efficiency of the scatter search algorithm in order to minimize energy consumption in urban areas.
Federation views as a basis for querying and updating database federations

This paper addresses the problem of how to query and update so-called database federations. A database federation provides for tight coupling of a collection of heterogeneous component databases into a global integrated system. This problem of querying and updating a database federation is tackled by describing a logical architecture and a general semantic framework for precise specification of such database federations, with the aim to provide a basis for implementing a federation by means of relational database views. Our approach to database federations is based on the UML/OCL data model, and aims at the integration of the underlying database schemas of the component legacy systems to a separate, newly defined integrated database schema. One of the central notions in database modelling and in constraint specifications is the notion of a database view, which closely corresponds to the notion of derived class in UML. We will employ OCL (version 2.0) and the notion of derived class as a means to treat (inter-)database constraints and database views in a federated context. Our approach to coupling component databases into a global, integrated system is based on mediation. The first objective of our paper is to demonstrate that our particular mediating system integrates component schemas without loss of constraint information. The second objective is to show that the concept of relational database view provides a sound basis for actual implementation of database federations, both for querying and updating purposes.
Scene reconstruction using distributed microphone arrays

A method for the joint localization and orientation estimation of a directional sound source using distributed microphones is presented. By modeling the signal attenuation due to the microphone directivity, the source directivity, and the source-microphone distance, a multi-dimensional search over all possible sound source scene reconstruction algorithm is presented in the context of an experiment with 24 microphones and a dynamic speech source. At a signal-to-noise ratio of 20 dB and with a reverberation time of approximately 0.1 s, accurate location estimates (20 cm error) and orientation estimates (less than 10/spl deg/ average error) are obtained.
Examinations of Audiovisual Speech Processes, the McGurk Effect and the Heteromodal Superior Temporal Sulcus in the Human Brain Across Numerous Approaches

Speech can be perceived through the speech sounds we hear and the facial/lip movements we see. In this dissertation, I explore the fundamental question of how the brain processes audiovisual (AV) speech using different approaches. In Chapter 2, we present a general framework for the comparison of AV speech neuroimaging experiments in terms of whether the AV signals were dissimilar (conflicting AV speech) or equivalent (validating AV speech). Using this framework, we conduct an Activation Likelihood Estimation meta-analysis to determine which brain regions are consistently activated for experiments that stressed conflicting vs. validating AV speech. Our main finding reveals that bilateral posterior temporal regions, left inferior parietal lobule and other dorsal-stream regions are recruited for conflicting AV speech. In Chapter 3, we focus on the superior temporal sulcus (STS), which has been associated with AV speech (Calvert et al., 2000; Chapter 2). Across cognitive functions, we assess the consistent functional coactivation patterns associated with anatomical bilateral STS subregions using meta-analytic connectivity modeling. These results demonstrate that the posterior STS consistently coactivates with auditory/language dorsal-stream regions, whereas anterior STS consistently coactivates with ventral-stream regions (although less strongly). In Chapter 4, we examine the causal relationship between brain structure and AV speech perception in 33 lefthemisphere stroke survivors and 39 matched controls, using the McGurk paradigm. We investigate behavior, and brain structure and AV speech relationships using a multivariate lesion symptom mapping technique in the stroke cohort and complementary voxel-based morphometry
User profiles based on revisitation times

Our work is devoted to Web revisitation patterns of individual users. Everybody revisits Web pages, but their reasons for doing so can differ. We analyzed Web interaction logs of millions users to characterize how people revisit Web content. We revealed that each user have its own distribution of revisitation times. This distribution follows Power Law with some exponent, which captures specific user peculiarities.
THE NEARBY NEUTRON STAR RX J0720.4 − 3125 FROM RADIO TO X-RAYS

We present radio, optical, ultraviolet, and X-ray observations of the isolated, thermally-emitting neutron star RX J0720.4 − 3125 using the Parkes radio telescope, the Very Large Array, the Hubble Space Telescope , and the Chandra X-ray Observatory . From these data we show that the optical/UV spectrum of RX J0720.4 − 3125 is not well ﬁt by a Rayleigh-Jeans tail as previously thought, but is instead best ﬁt by either a single non-thermal power-law or a combination of a Rayleigh-Jeans tail and a non-thermal power-law. Taken together with the X-ray spectrum, we ﬁnd the best model for RX J0720.4 − 3125 to be two blackbodies plus a power-law, with the cool blackbody implying a radius of 11–13 km at an assumed distance of 300 pc. This is similar to many middle aged (10 5 − 6 yr) radio pulsars such as PSR B0656+14, evidence supporting the hypothesis that RX J0720.4 − 3125 is likely to be an oﬀ-beam radio pulsar. The radio data limit the ﬂux at 1.4 GHz to be < 0 . 24 mJy, or a luminosity limit of 4 πd 2 F < 3 × 10 25 d 2 300 ergs s − 1 , and we see no sign of extended nebulosity, consistent with expectations for a pulsar like RX J0720.4 − 3125.
ULFM-MPI Implementation of a Resilient Task-Based Partial Differential Equations Preconditioner

We present a task-based domain-decomposition preconditioner for partial differential equations (PDEs) resilient to silent data corruption (SDC) and hard faults. The algorithm exploits a reformulation of the PDE as a sampling problem, followed by a regression-based solution update that is resilient to SDC. We adopt a server-client model implemented using the User Level Fault Mitigation MPI (MPI-ULFM). All state information is held by the servers, while clients only serve as computational units. The task-based nature of the algorithm and the capabilities of ULFM are complemented at the algorithm level to support missing tasks, making the application resilient to hard faults affecting the clients. Weak and strong scaling tests up to ~115k cores show an excellent performance of the application with efficiencies above 90%, demonstrating the suitability to run at large scale. We demonstrate the resilience of the application for a 2D elliptic PDE by injecting SDC using a random single bit-flip model, and hard faults in the form of clients crashing. We show that in all cases, the application converges to the right solution. We analyze the overhead caused by the faults, and show that, for the test problem considered, the overhead incurred due to SDC is minimal compared to that from the hard faults.
Analysis of Microarray Image Spots Intensity: A Comparative Study

Microarray technology gives rise to a challenge to interpret the meaning of the immense amount of biological information formatted in numerical matrices from the expression levels of thousands of genes, possibly all genes in an organism. To meet this challenge various methods have been developed using both traditional and innovative techniques to extract, analyze and visualize gene expression data generated from DNA Chip. The large amount of information provided by microarray images requires automatic techniques to develop accurate and efficient processing. Each spot in the microarray contains the hybridization level of a single gene. In this paper, the comparative analysis of measurement of intensity of microarray spots using the intensity transformation methods such as gray level, logarithmic, gamma and contrast stretching after cropping a particular section of microarray image is performed, and the numeric data sheet is prepared for these transformations.
Dramatic Recovery in Acute Ischemic Stroke Is Associated With Arterial Recanalization Grade and Speed

Background and Purpose— Dramatic recovery (DR) is a predictor of stroke outcome among others. However, after successful recanalization, systematic favorable outcome is not the rule. We sought to analyze the impact of recanalization on DR in patients with acute ischemic stroke eligible for any revascularization strategies (either intravenous or endovascular). Methods— We analyzed data collected between April 2007 and May 2011 in our prospective clinical registry. All patients with acute ischemic stroke with National Institutes of Health Stroke Scale ≥10 at admission and an identification of arterial status before treatment were included. DR was defined as National Institutes of Health Stroke Scale ⩽3 at 24 hours or a decrease of ≥10 points within 24 hours. Results— DR occurred in 75 of 255 patients with acute ischemic stroke (29.4%). Patients with persistent occlusion had a low DR rate (11.1%) than those with no documented occlusion (36.5%) and those with occlusion followed by recanalization (35.3%; both P<0.001). Among patients with recanalization monitored by angiography, DR was higher among patients with complete recanalization than among those with partial recanalization (46.8% versus 14.3%; P<0.001) and increased with tertiles of time to recanalization (Ptrend=0.002). In multivariable logistic regression analysis, grade and time to recanalization appeared independently associated with DR; the adjusted ORs were 4.17 (95% CI, 1.61–10.77) for complete recanalization and 1.24 (95% CI, 1.04–1.48) for each 30-minute time decrease. Patients with versus without DR more frequently had modified Rankin Scale ⩽1 (67.6% versus 9.0%; P<0.001) and less frequently had hemorrhage (17.3% versus 33.9%; P=0.024). Conclusions— DR is strongly associated with favorable clinical outcome and is dependent on complete recanalization and time to recanalization.
Self-stopping worms

Modern network worms spread with tremendous speed-potentially covering the planet in mere seconds. However, for most worms, this prodigious pace continues unabated long after the outbreak's incidence has peaked. Indeed, it is this ongoing infection activity that is typically used to identify compromised hosts. In principle, a stealthier worm might eliminate this telltale sign by coordinating its members to halt infection activity after the vulnerable population is subverted. Thus, after a short initial spreading period all infected hosts could become quiescent "sleep eragents. " In this paper, we show that such "self-stopping" capabilities are trivial to add to existing worms, and can be efficiently implemented without any explicit coordination or additional network traffic.
Learning Higher-Order Interactions for User and Item Profiling Based on Tensor Factorization

User profiling techniques play a central role in many Recommender Systems (RS). In recent years, multidimensional data are getting increasing attention for making recommendations. Additional metadata help algorithms better understanding users' behaviors and decisions. Existing user/item profiling techniques for Collaborative Filtering (CF) RS in multidimensional environment mostly analyze data through splitting the multidimensional relations. However, this leads to the loss of multidimensionality in user-item interactions; whereas the interactions are naturally multidimensional since users' choices are often affected by contextual information. In this paper, we propose a unified profiling approach which models users/items with latent higher-order interaction factors. We demonstrate that the proposed profiling approach is intimately related to two-dimensional profiling based on Matrix Factorization techniques. We further propose to integrate the profiling approach into three neighborhood-based CF recommenders for item recommendation. Finally, we empirically show on real-world social tagging datasets that the proposed recommenders outperform state-of-the-art CF recommendation approaches in accuracy.
Convolutional Neural Associative Memories: Massive Capacity with Noise Tolerance

The task of a neural associative memory is to retrieve a set of previously memorized patterns from their noisy versions using a network of neurons. An ideal network should have the ability to 1) learn a set of patterns as they arrive, 2) retrieve the correct patterns from noisy queries, and 3) maximize the pattern retrieval capacity while maintaining the reliability in responding to queries. The majority of work on neural associative memories has focused on designing networks capable of memorizing any set of randomly chosen patterns at the expense of limiting the retrieval capacity. In this paper, we show that if we target memorizing only those patterns that have inherent redundancy (i.e., belong to a subspace), we can obtain all the aforementioned properties. This is in sharp contrast with the previous work that could only improve one or two aspects at the expense of the third. More specifically, we propose framework based on a convolutional neural network along with an iterative algorithm that learns the redundancy among the patterns. The resulting network has a retrieval capacity that is exponential in the size of the network. Moreover, the asymptotic error correction performance of our network is linear in the size of the patterns. We then ex- tend our approach to deal with patterns lie approximately in a subspace. This extension allows us to memorize datasets containing natural patterns (e.g., images). Finally, we report experimental results on both synthetic and real datasets to support our claims.
Novel microstrip bandpass filters based on complementary split-ring resonators

In this paper, a new methodology for the design of compact planar filters in microstrip technology is proposed. This is based on cascading filter stages consisting of the combination of complementary split-ring resonators (CSRRs), recently proposed by the authors, series capacitive gaps, and grounded stubs. By this means, we achieve the necessary flexibility to simultaneously obtain quite symmetric frequency responses, controllable bandwidths, and compact dimensions. Two prototype device bandpass filters are provided to illustrate the potentiality of the proposed approach. In the first prototype, the structure is periodic (i.e., composed of identical cells) and behaves as a left-handed transmission line with controllable bandwidth. In the second prototype device, periodicity is sacrificed with an eye toward the synthesis of a standard (Chebyshev) approximation. The measured frequency responses point out low insertion losses in the passband, as well as high-frequency selectivity with small dimensions. As compared to conventional parallel coupled line filters, reduction of device length by a factor of 2.4 is demonstrated. This is the first time that planar filters with controllable bandwidth based on CSRRs are achieved. These structures can be of interest in those applications where miniaturization and compatibility with planar circuit technology are key issues
Mechanisms of Immunity to Ehrlichia muris: a Model of Monocytotropic Ehrlichiosis

ABSTRACT Ehrlichia species can cause life-threatening infections or chronic persistent infections. Mechanisms of protective immunity were examined in an Ehrlichia muris mouse model of monocytotropic ehrlichiosis. C57BL/6 mice possessed strong genetic resistance to E. muris of an undetermined mechanism. CD8 T lymphocytes were particularly important, as revealed by 81% fatalities for E. muris-infected, major histocompatibility complex class I gene knockout mice compared with no deaths for wild-type C3H mice. Moreover, 80% of C3H mice depleted of CD8 and CD4 cells died of E. muris infection compared with only 44% of CD4 cell-depleted mice. CD8 T lymphocytes were demonstrated for the first time in an Ehrlichia infection to exhibit cytotoxic T-lymphocyte activity against Ehrlichia-infected target cells. Both gamma interferon and tumor necrosis factor were shown to play synergistic roles in protective immunity in vivo for the first time, as demonstrated by 75% fatalities when both cytokines were neutralized compared with minimal mortality when they were depleted separately. Passive transfer of antibodies, but not Fab fragments, to E. muris protected C3H/SCID mice against lethal infection. The mechanism of increased susceptibility (22% lethality) of C57BL/6 major histocompatibility complex class II gene knockout mice and CD4 cell-depleted C3H mice (i.e., through a gamma interferon or antibody mechanism), as well as the more important role of CD8 T lymphocytes (in the form of cytotoxic T-lymphocyte activity and/or gamma interferon production), remains to be elucidated. Protective immunity against monocytotropic E. muris is mediated by a combination of CD8 and CD4 T lymphocytes, gamma interferon, tumor necrosis factor alpha, and antibodies.
Tommy’s Tune

The following autoethnographic duet by faculty advisor and professor creates a dramatic and evocative account of the personal and cultural experience about a disabled student teacher. They blend storytelling and music which fuses a theoretical analysis about storytelling and life. Although sociocultural issues draw deep reflection about the emotional turmoil, cultural influences of language and social interaction provide details that critique social structures. As musician becoming teacher is a passionate yet complex endeavor, the faculty advisor shares first-hand a poetic but painful story about a disabled teacher being inducted into the teaching profession. By making explicit the personal-cultural connection, they use the life-changing epiphany to critique cultural issues about teaching and disability. As the faculty advisor approaches the professor for advice, his musicianship shifts her forward, backward, and sideways through feelings that evoke, invoke, and provoke a curriculum that does not transfer knowledge from educational method classes. Instead, it embeds musical language as a metaphorical conduit to interrogate the pros, cons and both sides of the complicated issue of disability that influences the completion of his teaching practicum for his undergraduate bachelor of education degree. An epiphany from music and story reveals the irony of living in a culture of both uniformity and diversity. They explore the constructs of ideology, abnormality, marginalization, and secrecy. Thus, by blending story and music, the authors resolve a transformative autoethnographic aspect about the personal and cultural influences that provoke new deeper ways of thinking about curriculum.