Miracle-dz/UniFAR-FTUs huggingface dataset:
A recoverable hybrid C&C botnet

In this paper, we introduce the possible design of such a botnet called CoolBot which exploits a novel hybrid command and control (C&C) structure — hybrid P2P and URL Flux. The proposed CoolBot would have extremely desirable features — robustness and recoverability, that is, it could not only defend against popular attacks such as Sybil and routing table pollution attack but also could recover its C&C channel in a tolerable delay in case most of critical resources are destroyed, which promise to be appealing for botmasters. Our preliminary results show that the design of CoolBot is feasible and hard to defend against, consequently posing potential threat for Internet security. The goal of our work is to increase the understanding of advanced botnets which will promote the development of more efficient countermeasures. To conclude our paper, we suggest possible defenses against the emerging threat.
Multi-dimensional Resource Integrated Scheduling in a Shared Data Center

Resource scheduling is crucial to data centers. However, most existing resource scheduling algorithms focus only on one-dimensional resource models, ignoring the fact that multiple resources (e.g. CPU, memory, storage, and network bandwidth) are consumed simultaneously. Competition for multiple resources has become increasingly severe as cloud computing allows uncoordinated and heterogeneous users to share a data center. In this paper, we map such a resource scheduling problem to a bounded multi-dimensional scheduling problem (B-MDKP), taking into account the requirement dependency among multi-dimensional resources. Due to the NP hardness of B-MDKP, we present Multi-dimensional Resource Integrated Scheduling (MRIS), a novel heuristic algorithm to obtain the approximate optimal solution. To demonstrate the advantage of our scheduling scheme, we have implemented MRIS in Haizea, a resource management architecture and made a detailed comparison with existing studies. Our evaluation results show that MRIS achieves high efficiency and high performance for a diverse set of workloads.
Epigenetic Upregulation of lncRNAs at 13q14.3 in Leukemia Is Linked to the In Cis Downregulation of a Gene Cluster That Targets NF-kB

Non-coding RNAs are much more common than previously thought. However, for the vast majority of non-coding RNAs, the cellular function remains enigmatic. The two long non-coding RNA (lncRNA) genes DLEU1 and DLEU2 map to a critical region at chromosomal band 13q14.3 that is recurrently deleted in solid tumors and hematopoietic malignancies like chronic lymphocytic leukemia (CLL). While no point mutations have been found in the protein coding candidate genes at 13q14.3, they are deregulated in malignant cells, suggesting an epigenetic tumor suppressor mechanism. We therefore characterized the epigenetic makeup of 13q14.3 in CLL cells and found histone modifications by chromatin-immunoprecipitation (ChIP) that are associated with activated transcription and significant DNA-demethylation at the transcriptional start sites of DLEU1 and DLEU2 using 5 different semi-quantitative and quantitative methods (aPRIMES, BioCOBRA, MCIp, MassARRAY, and bisulfite sequencing). These epigenetic aberrations were correlated with transcriptional deregulation of the neighboring candidate tumor suppressor genes, suggesting a coregulation in cis of this gene cluster. We found that the 13q14.3 genes in addition to their previously known functions regulate NF-kB activity, which we could show after overexpression, siRNA–mediated knockdown, and dominant-negative mutant genes by using Western blots with previously undescribed antibodies, by a customized ELISA as well as by reporter assays. In addition, we performed an unbiased screen of 810 human miRNAs and identified the miR-15/16 family of genes at 13q14.3 as the strongest inducers of NF-kB activity. In summary, the tumor suppressor mechanism at 13q14.3 is a cluster of genes controlled by two lncRNA genes that are regulated by DNA-methylation and histone modifications and whose members all regulate NF-kB. Therefore, the tumor suppressor mechanism in 13q14.3 underlines the role both of epigenetic aberrations and of lncRNA genes in human tumorigenesis and is an example of colocalization of a functionally related gene cluster.
A preliminary study on progesterone antioxidation in promoting learning and memory of young ovariectomized mice

Introduction The aim of this preliminary study was to evaluate the effect of long-term progesterone (P4) treatment on structural and functional deficits associated with the hippocampus. Material and methods Mice served as sham controls or were bilaterally ovariectomized (Ovx), and a 90-day regimen of placebo or P4 was applied to the animal. After the administration, the acquisition and retrieval of mice in contextual fear conditioning (CFC) and a water maze were examined. Hippocampal tissues from some mice in each group were stained with cresyl violet, and the remainder were taken for determining the antioxidant power. Results Compared with placebo controls, the time spent on freezing was higher and the latencies were longer for mice given high-dose P4 (HP) (p < 0.05) in CFC, and the HP group also had longer searching time spent in the target quadrant (p < 0.05) in the water maze. Compared with placebo controls, the cell number of hippocampus CA1, CA3 and DG was significantly higher in the HP group (p < 0.05), and the thickness of the cell layer in CA1 and DG was also higher in the HP group (p < 0.05). All the oxidative stress biomarkers show that the antioxidative activity in hippocampus tissue from the HP and LP groups is higher than that in placebo controls (p < 0.05). Conclusions Ovx impairs learning and memory of mice, which can be rescued by a long-term regimen of HP via its antioxidant effects.
QUASAR OPTICAL VARIABILITY IN THE PALOMAR-QUEST SURVEY

The ensemble variability properties of nearly 23,000 quasars are studied using the Palomar-QUEST Survey. The survey has covered 15,000 deg2 multiple times over 3.5 years using seven optical filters, and has been calibrated specifically for variability work. Palomar-QUEST allows for the study of rare objects using multiple epochs of consistently calibrated, homogeneous data, obviating the common problem of generating comparable measurements from disparate data sets. A power-law fit to the quasar structure function versus time yields an index of 0.432 ± 0.024 for our best measured sample. We see the commonly reported anticorrelation between average optical variability amplitude and optical luminosity, and measure the logarithmic decrease in variability amplitude to scale as the logarithm of the luminosity times 0.205 ± 0.002. Black hole mass is positively correlated with variability amplitude over three orders of magnitude in mass. Quasar variability amplitude is seen to decrease with Eddington ratio as a step function with a transition around Eddington ratio of 0.5. The higher variability at low Eddington ratios is due to excess power at timescales shorter than roughly 300 days. X-ray and radio measurements exist for subsets of the quasar sample. We observe an anticorrelation between optical variability amplitude and X-ray luminosity. No significant correlation is seen between average optical variability properties and radio luminosity. The timescales of quasar fluctuations are suggestive of accretion disk instabilities. The relationships seen between variability, Eddington ratio, and radio and X-ray emission are discussed in terms of a possible link between the behavior of quasars and black hole X-ray binaries.
SOLAR SYSTEM GEOMETRIES AND TERRESTRIAL CLIMATE

I report the discovery of the relation between the solar aspect angle geometry of celestial bodies and terrestrial climate on a time scale of more than 6000 years. A main impact will be shown can be related to the couple of the plutinos Quaoar and Pluto and its tide like heliocentric aspects, but minor impacts will be shown can be related to most of the planets including Jupiter and the planets beyond. The main cycle can be found to 1827 years from astronomical algorithm data of the two bodies, but because of the strong elliptic nature of Pluto tide like aspects occurs mostly three times over a time range of about 200 years. The discovered geometry fits the published well known terrestrial temperature proxies and reconstructions of the last two millenniums and before, and this gives cause for the forecast of the global terrestrial climate for more than 1000 years ahead. Solar resonance modes as suggested by Robert Ehrlich in 2006 were taken to simulate benthic forams data back to 1000 ky. Subject headings: terrestrial climate, solar geometry, solar tides, climate forcast, benthic forams
Data Handover: Reconciling Message Passing and Shared Memory

Data Handover (DHO) is a programming paradigm and interface that aims to handle data between parallel or distributed processes that mixes aspects of message passing and shared memory. It is designed to overcome the potential problems in terms of efficiency of both: (1) memory blowup and forced copies for message passing and (2) data consistency and latency problems for shared memory. Our approach attempts to be simple and easy to understand. It contents itself with just a handful of functions to cover the main aspects of coarse grained inter-operation upon data.
Selective Perception Learning for Tasks Allocation

This paper presents a learning algorithm used to allocate tasks to agents in an uncertain real-time environment. In such environment, tasks have to be analyzed and allocated really fast for the multiagent system to be effective. To analyze those tasks, described by a lot of attributes, we have used a selective perception technique to enable agents to narrow down the description of each task by choosing the attributes that it should be considering in each situation. By doing so, we have obtained a drastic reduction of the number of possible states. We have used this algorithm at two different levels for the problem of choosing the best fire to extinguish for each firefighter agent in the RoboCupRescue simulation environment. First, a center agent is using the a lgorithm to allocate a zone on fire for each firefighter agent. Then, those agents are choosing the best fire to extinguish in this zone. Our results show a good improvement in the agents capability to extinguish fires, as the agents become better at distinguishing the world states.
Human Rights Trials in an Era of Democratic Stagnation: The Case of Turkey

Domestic human rights trials are often conceptualized within the context of transition toward democracy. Yet, a recent cascade of trials in Turkey defies this argument. Between 2008 and 2015, Turkish courts oversaw fifteen trials examining individual criminal accountability for human rights violations carried out by the Turkish military during the conflict with the PKK in the 1990s. The cascade of trials has emerged and remained ongoing while the Turkish regime has become steadily more authoritarian. This article explains the emergence of these trials by the redistribution of power among elite actors, which created a window of opportunity allowing for ongoing legal mobilization to result in prosecutions. Drawing on original data from interviews conducted in Turkey between 2014–2015, this study demonstrates the importance of power redistribution for human rights during periods of democratic stagnation, while emphasizing the ability of trials to contribute to the contestation of hegemonic narratives.
An Analysis of Secure M2M Communication in WSNs Using DTLS

The Internet of Things is a paradigm that is becoming more and more implemented in a wide variety of devices that communicate between themselves. Under the form of a Machine-to-Machine (M2M) communication, it is integrated by a highly heterogeneous community where many devices are constrained in resources, being yet transmitting sensible data. Therefore, it is necessary to guarantee that the M2M communication is equally safer that traditional networks, in which the HTTP Secure (HTTPS) protocol is used. With this aim, we propose in this paper the implementation of the Constrained Application Protocol (CoAP) together with the Datagram Transport Secure Layer (DTLS), all based on the Contiki operating system, this last being a very popular development platform for WSNs. Additionally, we present an analysis of the threats that occur in the previously described communication environments in order to identify key elements in the previous protocols to insure a secure M2M communication.
Measurement of microbubbles size by Granulometry approach

Image processing by granulometry method is used to measuring microbubbles size distribution in this paper. In this method the size distribution of microbubbles in an image determines without explicitly detecting or segmenting each bubble first. Microbubbles with average sizes about 20 to 5300 micron and air volume fraction from 3 to 42 percent, which have been generated by a microbubble generator device, were measured. Estimated maximum and minimum sizes of microbubbles were compared to direct measuring of images. Excellent comparable results suggested using this method to determination of size distribution, for an extensive domain of void fractions especially in high void fractions while other image processing methods were confined to processing images of fully disperse phases.
THE LOST SIBLINGS OF THE SUN

The anomalous chemical abundances and the structure of the Edgeworth-Kuiper belt observed in the solar system constrain the initial mass and radius of the star cluster in which the Sun was born to M 500-3000M ☉ and R 1-3 pc. When the cluster dissolved, the siblings of the Sun dispersed through the galaxy, but they remained on a similar orbit around the Galactic center. Today these stars hide among the field stars, but 10-60 of them are still present within a distance of ~100 pc. These siblings of the Sun can be identified by accurate measurements of their chemical abundances, positions, and their velocities. Finding even a few will strongly constrain the parameters of the parental star cluster and the location in the Galaxy where we were born.
Data collection for information security system

The security of information systems is generally related to the deployed tools to protect the network infrastructure from attacks. These latter are based on detection components that analyze the information transmitted through the network. The existing systems do not provide a full protection and present a high rate of false positives that could result from a lack in the analysis of the collected information (events). Thus, useful information should be determined to perform accurate detection and hence perform good protection. In this paper, a new approach to collect useful data from the network is proposed. It is based on new components called: observers. The system architecture is presented and the different components are described in details. Furthermore, the functioning of the different parts of the system is discussed.
AFRL-RX-WP-TP-2010-4064 AUTOMATED IDENTIFICATION AND CHARACTERIZATION OF SECONDARY & TERTIARY γ ’ PRECIPITATES IN NICKEL-BASED SUPERALLOYS ( PREPRINT )

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.
Experimental Evidence That Low Social Status Is Most Toxic to Well-Being When Internalized

What makes low social status toxic to well-being? To internalize social status is to believe the self is responsible for it. We hypothesized that the more people internalize low subjective social status, the more their basic psychological needs are thwarted. Experiment 1 randomly assigned participants to imagine themselves in low, middle, or high social status, and assessed their subjective social status internalization by independent ratings. The more participants internalized low status, the more they reported their basic psychological needs were thwarted. This effect did not appear among their higher status counterparts. Experiment 2 replicated and extended these findings using a behavioral manipulation of subjective social status and a self-report measure of internalization. We discuss implications for basic and action research.
Visual Quantification of the Circle of Willis: An Automated Identification and Standardized Representation

This paper presents a method for the visual quantification of cerebral arteries, known as the Circle of Willis (CoW). It is an arterial structure with the responsibility of supplying the brain with blood, however, dysfunctions can lead to strokes. The diagnosis of such a time‐critical/urgent event depends on the expertise of radiologists and the applied software tools. They use basic display methods of the volumetric data without any support of advanced image processing and visualization techniques. The goal of this paper is to present an automated method for the standardized description of cerebral arteries in stroke patients in order to provide an overview of the CoW's configuration. This novel representation provides visual indications of problematic areas as well as straightforward comparisons between multiple patients. Additionally, we offer a pipeline for extracting the CoW from Time‐of‐Flight Magnetic Resonance Angiography (TOF‐MRA) data sets together with an enumeration technique for labelling the arterial segments by detecting the main supplying arteries of the CoW. We evaluated the feasibility of our visual quantification approach in a study of 63 TOF‐MRA data sets and compared our findings to those of three radiologists. The obtained results demonstrate that our proposed techniques are effective in detecting the arteries and visually capturing the overall configuration of the CoW.
Identification of Differential Expression Genes in Leaves of Rice (Oryza sativa L.) in Response to Heat Stress by cDNA-AFLP Analysis

High temperature impedes the growth and productivity of various crop species. To date, rice (Oryza sativa L.) has not been exploited to understand the molecular basis of its abnormally high level of temperature tolerance. To identify transcripts induced by heat stress, twenty-day-old rice seedlings of different rice cultivars suffering from heat stress were treated at different times, and differential gene expression analyses in leaves were performed by cDNA-AFLP and further verified by real-time RT-PCR. In aggregate, more than three thousand different fragments were indentified, and 49 fragments were selected for the sequence and differential expressed genes were classified functionally into different groups. 6 of 49 fragments were measured by real-time RT-PCR. In addition, the variations of three different polyamine contents in response to heat stress through high-performance liquid chromatography (HPLC) analysis were also performed. The results and their direct and indirect relationships to heat stress tolerance mechanism were discussed.
Classification of Large microarray Datasets Using Fast Random Forest Construction

Random forest is an ensemble classification algorithm. It performs well when most predictive variables are noisy and can be used when the number of variables is much larger than the number of observations. The use of bootstrap samples and restricted subsets of attributes makes it more powerful than simple ensembles of trees. The main advantage of a random forest classifier is its explanatory power: it measures variable importance or impact of each factor on a predicted class label. These characteristics make the algorithm ideal for microarray data. It was shown to build models with high accuracy when tested on high-dimensional microarray datasets. Current implementations of random forest in the machine learning and statistics community, however, limit its usability for mining over large datasets, as they require that the entire dataset remains permanently in memory. We propose a new framework, an optimized implementation of a random forest classifier, which addresses specific properties of microarray data, takes computational complexity of a decision tree algorithm into consideration, and shows excellent computing performance while preserving predictive accuracy. The implementation is based on reducing overlapping computations and eliminating dependency on the size of main memory. The implementation's excellent computational performance makes the algorithm useful for interactive data analyses and data mining.
Cross-Layer Routing with Link Quality and Stability-Aware in ITS Hybrid Wireless Mesh Networks

ITS hybrid Wireless Mesh Network (WMN) is special case of WMN containing relatively static mesh points and mobile mesh clients, which may suffer from random variations in channel and network condition and handoff problems due to mobility of mesh clients. This leads to poor performance in multi-hop WMN, especially with conventional non-flexible routing algorithms. To cope with instability of wireless links and fast handoff between mesh points for mobile mesh clients, a cross-layer routing that supports link quality optimization and stable routing is an effective way. The key technology is focusing on the choice of the route metrics. In this paper we propose a novel Link Quality and Stability-aware metric from MAC layer supporting Cross-layer Routing (LQSCR) for hybrid WMN. LQSCR selects the path which has best link quality and highest neighbor stability ratio to forward data. Analysis and simulation results show that LQSCR may achieves better performance in hybrid WMN in terms of end-to-end delay, jitter delay and packet delivery ratio in comparison to shortest path metric.
Early Software Reliability Prediction with Extended ANN Model

Generally, software reliability models can provide accurate reliability measurement in the later phase of testing. However, predictions in the early phase of software testing are useful as cost-effective and timely feedback. Early prediction is also feasible in practice with information from previous releases or similar projects. Such information has been utilized well for early reliability prediction with NHPP models by assuming the same failure rate between two similar projects. Alternatively, in this paper, we propose to "reuse" failure data from past projects/releases with ANN models to improve early reliability for current project/release. To illustrate the proposed approach, two numerical examples are developed. Better prediction performance is observed in early phase of testing compared with original ANN model without failure data reuse. Furthermore, the optimal switching point from proposed approach to original ANN model in the whole testing phase is studied, with specific analysis on the two examples