Miracle-dz/UniFAR-FTUs huggingface dataset:
An Analysis of Malfeasant Activity Directed at a VoIP Honeypot

This paper analyses data collected over a nine month period in a simple VoIP honeypot based on simple design initially put forward by Usken(2009). The honeypot collected 2083 events of malfeasant activity directed towards commonly used VoIP ports. These events resulted in a range of activity being recorded from simple enumeration to advanced probing and attempts to compromise the victim honeypot. The analysis involved traditional statistics from packet analysis, using customised scripts for extraction of data and graphical analysis using i2 Analyst Workstation. The analysis has uncovered an escalation of network activity directed towards the honeypot over a nine month period. Initial geographical IP resolutions also see the majority of traffic emanating from the Chinese IP space. There is strong evidence to suggest that there is a botnet or worm like malcode being directed or developed for VoIP routers.
Degrees of freedom for energy savings in practical adaptive wireless systems

We present a new design concept for adaptive wireless communications with new trade-offs between system performance and energy consumption. The system performance, in terms of bit error rate, outage probability, and achieved spectral efficiency, depends on constellation signaling, applied diversity, and channel estimation. Resources dedicated to channel estimation and feedback traffic contribute to the overall system and network energy consumption, and the resulting CO2 emission. We consider the trade-offs among different methodologies and parameters toward an energy-efficient green communication system design. Below, we discuss the degrees of freedom in the design of communications systems with imperfect channel estimation and diversity, and investigate their energy saving options. We present the case studies of single- and multicarrier systems applying both margin-adaptive and rate-adaptive pilot-assisted transmission.
The filament-forming protein Pil1 assembles linear eisosomes in fission yeast

Eisosomes generate spatial domains in the plasma membrane of yeast cells. The core eisosome protein Pil1 is shown to form filaments in vitro and in cells. Pil1 filaments are stable at the cell cortex, and cytoplasmic Pil1 filament rods appear upon overexpression. This shows a role for self-assembly in organizing cortical domains.
Dominance Programming for Itemset Mining

Finding small sets of interesting patterns is an important challenge in pattern mining. In this paper, we argue that several well-known approaches that address this challenge are based on performing pair wise comparisons between patterns. Examples include finding closed patterns, free patterns, relevant subgroups and skyline patterns. Although progress has been made on each of these individual problems, a generic approach for solving these problems (and more) is still lacking. This paper tackles this challenge. It proposes a novel, generic approach for handling pattern mining problems that involve pair wise comparisons between patterns. Our key contributions are the following. First, we propose a novel algebra for programming pattern mining problems. This algebra extends relational algebras in a novel way towards pattern mining. It allows for the generic combination of constraints on individual patterns with dominance relations between patterns. Second, we introduce a modified generic constraint satisfaction system to evaluate these algebraic expressions. Experiments show that this generic approach can indeed effectively identify patterns expressed in the algebra.
Management, not climate, controls net CO2 fluxes and carbon budgets of three grasslands along an elevational gradient in Switzerland

In Switzerland, the traditional three-stage grassland farming system consists of grazed or cut grasslands along a gradient from lowland to alpine elevations. We measured carbon dioxide (CO2) fluxes at three grassland sites (400, 1000, 2000 m elevation) and estimated carbon sequestration for two years (2006 and 2007). Grasslands at higher elevations (>1000 m), managed at lower intensities, exhibited a larger net CO2 uptake compared to intensively managed grasslands at lower elevations (400 m). Nevertheless, net CO2 uptake rates during optimal growth were very similar for all three sites. Taking into account harvest outputs as well as manure inputs, we calculated the carbon stocks and their changes for grasslands at 400 m and 1000 m during two years. Similar to the cumulative net ecosystem CO2 fluxes, the seasonal course of carbon stock changes was strongly driven by management intensity, in particular by timing and amount of manure applications. Despite differences in environmental and management conditions with elevation, both grassland sites were carbon sinks during 2006 and 2007 (between 60 and 150 g C m a).
PROBABILITY FUZZY SUPPORT VECTOR MACHINES

In this paper, a model of probability fuzzy support vector machines (PFSVMs) based on the consideration both for fuzzy clustering and probability distributions is proposed. In many applications of traditional support vector machines (SVMs), there are over-fitting problems due to the fact that SVM is sensitive to outliers or noises. In order to solve the problem, the fuzzy support vector machines (FSVMs) model is established. However, in the case that two points are with the same membership, the more information of their influence cannot be carried out by FSVM. The proposed model is based on the consideration that there is not only existing classification distribution but also probability distribution among samples. Experiments show that compared with SVM and FSVM, PFSVM has a better prediction and the classification performance.
Energy-efficient Double Relay Communication Protocol in cellular networks

Network densification is a promising solution to increase the spectral efficiency of cellular networks that comes at the cost of a larger energy expenditure. Relays have been proposed as an energy-efficient alternative to network densification. However, deploying relays requires additional planning and infrastructure, which can be costly or even prohibitive for very dense networks. The Double Relay Communication Protocol (DRCP) [1] increases the spectral efficiency by combining spatial diversity and network coding principles. In contrast to other schemes based on relays, DRCP does not require deploying additional relays since it uses base stations as relays. However, its potential for providing an energy-efficient solution has not been explored yet. Therefore, in this paper we propose a power control mechanism that increases the energy efficiency of DRCP, while still achieving a larger spectral efficiency compared to other state-of-the-art approaches.
Drosophila Dicer-2 has an RNA interference–independent function that modulates Toll immune signaling

Fly Dicer-2 exhibits a non–RNA interference function in the posttranscriptional modulation of Toll protein expression and Toll signaling. Dicer-2 is the central player for small interfering RNA biogenesis in the Drosophila RNA interference (RNAi) pathway. Intriguingly, we found that Dicer-2 has an unconventional RNAi-independent function that positively modulates Toll immune signaling, which defends against Gram-positive bacteria, fungi, and some viruses, in both cells and adult flies. The loss of Dicer-2 expression makes fruit flies more susceptible to fungal infection. We further revealed that Dicer-2 posttranscriptionally modulates Toll signaling because Dicer-2 is required for the proper expression of Toll protein but not for Toll protein stability or Toll mRNA transcription. Moreover, Dicer-2 directly binds to the 3′ untranslated region (3′UTR) of Toll mRNA via its PAZ (Piwi/Argonaute/Zwille) domain and is required for protein translation mediated by Toll 3′UTR. The loss of Toll 3′UTR binding activity makes Dicer-2 incapable of promoting Toll signaling. These data indicate that the interaction between Dicer-2 and Toll mRNA plays a pivotal role in Toll immune signaling. In addition, we found that Dicer-2 is also required for the Toll signaling induced by two different RNA viruses in Drosophila cells. Consequently, our findings uncover a novel RNAi-independent function of Dicer-2 in the posttranscriptional regulation of Toll protein expression and signaling, indicate an unexpected intersection of the RNAi pathway and the Toll pathway, and provide new insights into Toll immune signaling, Drosophila Dicer-2, and probably Dicer and Dicer-related proteins in other organisms.
Discovery of Latent Factors in High-dimensional Data Using Tensor Methods

Author(s): Huang, Furong | Advisor(s): Anandkumar, Animashree | Abstract: Unsupervised learning aims at the discovery of hidden structure that drives the observations in the real world. It is essential for success in modern machine learning and artificial intelligence. Latent variable models are versatile in unsupervised learning and have applications in almost every domain, e.g., social network analysis, natural language processing, computer vision and computational biology. Training latent variable models is challenging due to the non-convexity of the likelihood objective function. An alternative method is based on the spectral decomposition of low order moment matrices and tensors. This versatile framework is guaranteed to estimate the correct model consistently. My thesis spans both theoretical analysis of tensor decomposition framework and practical implementation of various applications. This thesis presents theoretical results on convergence to globally optimal solution of tensor decomposition using the stochastic gradient descent, despite non-convexity of the objective. This is the first work that gives global convergence guarantees for the stochastic gradient descent on non-convex functions with exponentially many local minima and saddle points. This thesis also presents large-scale deployment of spectral methods (matrix and tensor decomposition) carried out on CPU, GPU and Spark platforms. Dimensionality reduction techniques such as random projection are incorporated for a highly parallel and scalable tensor decomposition algorithm. We obtain a gain in both accuracies and in running times by several orders of magnitude compared to the state-of-art variational methods. To solve real world problems, more advanced models and learning algorithms are proposed. After introducing tensor decomposition framework under latent Dirichlet allocation (LDA) model, this thesis discusses generalization of LDA model to mixed membership stochastic block model for learning hidden user commonalities or communities in social network, convolutional dictionary model for learning phrase templates and word-sequence embeddings, hierarchical tensor decomposition and latent tree structure model for learning disease hierarchy in healthcare analytics, and spatial point process mixture model for detecting cell types in neuroscience.
Familial occurrences of a variety of premalignant diseases and uncommon malignant neoplasms

A medical genetic study has been undertaken of a large segment of an inbred Dutch kindred containing more than 2000 members. Noteworthy are occurrences of several hereditary precancerous diseases among the family members. These include two siblings with xeroderma pigmentosum and two siblings from another sibship who manifest a syndrome which may possibly be a variant of Fanconi's aplastic anemia. Four patients had histologically verified testicular malignant neoplasms. Other generally rarely occurring cancers included Wilms' tumor, thymic carcinoma, and astrocytoma. At present there is no satisfactory etiologic explanation for these events in this family. Environmental factors common to the family cannot be excluded. Hereditary factors conditioned strongly by consanguinity may be operating in concert with as yet unknown non‐genetic factors such as an oncogenic virus.
Use of the Ten-Point Clock Test to Compare Executive Functioning across 24 Months in Patients with Subcortical Vascular Dementia

The Ten-point Clock Test can be used to identify early forms of Alzheimer's disease because it is reliable, well accepted, and easily administered at the bedside. Nevertheless, its clinical role in the detection of early dementia and its correlations with other cognitive processes is still under investigation. Vascular dementia is an uncertain nosological entity, in which unevenly distributed patterns of cognitive deficits comprising slowing of cognitive processing and impairment of executive function occur. The present study assessed how the Clock Test scores correlated with a number of other neuropsychological and functional tests in a sample of 144 patients with vascular dementia, who were followed for a period of 24 mo. At baseline, at 12 mo. and at 24 mo. subjects were administered a battery of tests, including the Mini-Mental State Examination, word fluency, visuospatial skills, an evaluation of hetero- and autotopognosia (knowledge of self), the Proverbs Test, and the Ten-point Clock Test. For these patients scores on the Clock Test correlated significantly with semantic abilities, with abstract reasoning capacities, visuospatial perception, and with right and left recognition.
Localized Coverage Connectivity Based on Shape and Area Using Mobile Sensor Robots in Wireless Sensor Networks

A wireless sensor network (WSN) is spatially distributing independent sensors to monitor physical and environmental characteristics such as temperature, sound, pressure and also provides different applications such as battlefield inspection and biological detection. The Constrained Motion and Sensor (CMS) Model represents the features and explain k-step reach ability testing to describe the states. The description and calculation based on CMS model does not solve the problem in mobile robots. The ADD framework based on monitoring radio measurements creates a threshold. But the methods are not effective in dynamic coverage of complex environment. In this paper, a Localized Coverage based on Shape and Area Detection (LCSAD) Framework is developed to increase the dynamic coverage using mobile robots. To facilitate the measurement in mobile robots, two algorithms are designed to identify the coverage area, (i.e.,) the area of a coverage hole or not. The two algorithms are Localized Geometric Voronoi Hexagon (LGVH) and Acquaintance Area Hexagon (AAH). LGVH senses all the shapes and it is simple to show all the boundary area nodes. AAH based algorithm simply takes directional information by locating the area of local and global convex points of coverage area. Both these algorithms are applied to WSN of random topologies. The simulation result shows that the proposed LCSAD framework attains minimal energy utilization, lesser waiting time, and also achieves higher scalability, throughput, delivery rate and 8% maximal coverage connectivity in sensor network compared to state-of-art works.
The Effect of Frontal Lobe Function on Proverb Interpretation in Parkinson's Disease

The cognitive impairment associated with PD involves a broad range of deficits including difficulty with executive functions such as working memory, inhibition, decision-making, and cognitive multitasking, learning, and visuoperceptual skills. Even early in the disease, when motor symptoms tend to predominate, there is evidence that cognitive functions can be compromised. Owing to the presence of executive-type dysfunction in PD, some researchers likened the cognitive deficit of PD to that seen with frontal lobe damage. The anatomical basis of PD, however, suggests otherwise. Dopamine depletion in the basal ganglia, and the downstream depletion of dopamine in the frontostriatal circuitry is often thought to be the foundation of the cognitive deficits associated with PD. In addition to cognitive impairments, a language deficit attends Parkinson’s disease (PD) alongside the other motor and non-motor aspects of the disease. This language deficit is characterized by difficulty processing various types of figurative language, and has been associated with various PD-related cognitive deficits, such as deficits in working memory (WM). Varied assessment tools have been used to characterize the neuropsychological functions associated with this language deficit, most of which involve some measure of frontally-based cognitive skills. The purpose of this study was to examine the influence of frontal lobe function on higher-level language function in the non-demented PD (NDPD) population. To investigate this influence, the performance of two groups of participants (i.e., one NDPD
Antiseizure drugs differentially modulate theta‐burst induced long‐term potentiation in C57BL/6 mice

Cognitive comorbidities are increasingly recognized as an equal (or even more disabling) aspect of epilepsy. In addition, the actions of some antiseizure drugs (ASDs) can impact learning and memory. Accordingly, the National Institute of Neurological Disorders and Stroke (NINDS) epilepsy research benchmarks call for the implementation of standardized protocols for screening ASDs for their amelioration or exacerbation of cognitive comorbidities. Long‐term potentiation (LTP) is a widely used model for investigating synaptic plasticity and its relationship to learning and memory. Although the effects of some ASDs on LTP have been examined, none of these studies employed physiologically relevant induction stimuli such as theta‐burst stimulation (TBS). To systematically evaluate the effects of multiple ASDs in the same preparation using physiologically relevant stimulation protocols, we examined the effects of a broad panel of existing ASDs on TBS‐induced LTP in area CA1 of in vitro brain slices, prepared in either normal or sucrose‐based artificial cerebrospinal fluid (ACSF), from C57BL/6 mice.
ATC Assessment with transient stability constraints

The liberalization of the electricity market has generated significant changes in the planning, operation and management of electric power systems. The open accesses for Independent Power Producers (IPP) results in possible dynamic problems due to the small security margin that characterize competitive markets. For this reason, evaluation of on-line transients stability limits is essential. These limits could be evaluated with modern computed measurement instrumentations that measure in real time and high resolution the fields signal waves such as bus voltages, powers, rotor angles etc, and transmits the measured signals to a data processing center. In this paper we demonstrate by simulation calculations the use of the swing equation in the time domain and in the phase plane, at different configurations to evaluate dynamics constrain that can influence the availability of the transfer capability between two areas. The stability limits have to be added to the thermal and the voltage limits and together they bound the available transfer capability from the source bus to sink bus.
Storage modeling for power estimation

Power consumption is a major issue in today's datacenters. Storage typically comprises a significant percentage of datacenter power. Thus, understanding, managing, and reducing storage power consumption is an essential aspect of any efforts that address the total power consumption of datacenters. We developed a scalable power modeling method that estimates the power consumption of storage workloads. The modeling concept is based on identifying the major workload contributors to the power consumed by the disk arrays. To estimate the power consumed by a given host workload, our method translates the workload to the primitive activities induced on the disks. In addition, we identified that I/O queues have a fundamental influence on the power consumption. Our power estimation results are highly accurate, with only 2% deviation for typical random workloads with small transfer sizes (up to 8K), and a deviation of up to 8% for workloads with large transfer sizes. We successfully integrated our modeling into a power-aware capacity planning tool to predict system power requirements and integrated it into an online storage system to provide online estimation for the power consumed.
Cytokine Genotype Polymorphisms in Breast Carcinoma: Associations of TGF-β1 with Relapse

Markers of angiogenesis, cell proliferation, and cytokine regulation are associated with the development and course of autoimmune and malignant diseases. We investigated associations between cytokine production genotypes in breast cancer patients compared with controls and explored associations with known prognostic indices and relapse status. Eighty-eight females with breast carcinoma (BC) were studied in this case-control study comparing the cytokine genotypes of TNF-α TGF-ß1, IL-10, IL-6, and IFN-γ with controls. Cytokine polymorphisms were identified by sequence-specific primers for codons, introns, or promoters regulating cytokine production. Patient characteristics, such as estrogen and progesterone receptor status, DNA ploidy, Her-2 neu expression, lymph node involvement, tumor size, and relapse status were evaluated. Cytokine genotypes were not associated with breast cancer compared with controls. Correlations between TGF-β1 high-production genotypes and greater than four positive lymph nodes (OR = 2.3; p = ns) and TNF-α high-production genotype and the mean level of estrogen receptor expression (66 ± 24 vs. 34 ± 36, p = 0.016) were identified. The median patient follow-up interval from diagnosis to evaluation was 50.1 months (range 13–387 months). Relapse status was known for 84 of the patients. The odds of relapse in TGF-β1 codon 10 CC genotypes was 2.81 times that in TGF-β1 high-production genotypes (OR = 2.81; 95% CI for OR: 1.0, 7.8; p = 0.04). Mean progesterone receptor expression was decreased in relapsed patients (40.9 ± 29.9% vs. 23.1 ± 24.5, p = 0.05). The other cytokine genotypes studied (IL-10, IL-6, IFN-γ and TNF-α production were not associated with breast cancer overall or relapse status. In this study, TGF-β1 low-production genotypes (TGF-β1 10 CC) were associated with an increased odds of disease relapse. This finding should be confirmed in a longitudinal study to further investigate the regulatory function of cytokine production as a prognostic indicator of relapse.
Power Consumption Reduction Explorations in Processors by Enhancing Performance Using Small ESL Reprogrammable eFPGAs

Power consumption has become the biggest challenge in industry for chip design. We will present that by using small reprogrammable embedded FPGAs (eFPGA) coupled with a processor we can achieve power and energy reduction with a very small silicon overhead. Enhancement of computational power helps lowering down frequency (dynamic frequency scaling) to decrease dynamic power. By having same computational power at lower frequency can help fabricate chip in LP (low power) process compared to GP (general purpose) which helps to significantly reduce static power which is very crucial issue at and below 90 nm technologies. Use of reconfigurable accelerator (eFPGA) raises the natural question of its programming complexity, HW/SW partitioning and silicon overhead. We will present that silicon overhead of eFPGA is small compared to the benefits which can be obtained with it. We will present our profiling tool and Catapulttrade ESL tool of Mentor Graphicsreg to analyze the issue of programming complexity.