Miracle-dz/UniFAR-FTUs huggingface dataset:
A Similarity Network Based Behavior Anomaly Detection Model for Computer Systems

As modern computer systems become increasingly complex in infrastructure and usage, the demand for capabilities of detecting anomalous behavior has grown urgent. Although techniques for point anomaly detection have been proposed and adopted in practice, behavior anomaly detection still lacks effective approaches due to its inherent complexities. We present a new anomalous behavior detection model based on similarity network. Instead of learning behaviors according to frequencies of occurrences as current approaches do, our model exploits the similarity relationship between emerging behavior patterns. Specifically, a Markov model rank algorithm is performed on the similarity network to discover behavior anomalies. Our model is able to distinguish normal behavior patterns and anomalous ones in changing environments without training phase. We implemented this model and conducted extensive experiments on a range of data sets. Results show that our model can detect behavior anomalies in computer systems with high accuracy.
Reliability Bounds for Fault-Tolerant Systems with Deferred Repair using Bounding Split Regenerative Randomization

A numerically stable method is developed which computes seemingly tight bounds at a small computational cost relative to the model size, when that model size is large, for the unreliability and bounds for the unreliability using, respectively, exact and bounding failure/repair continuous-time Markov chain models of fault-tolerant systems with exponential failure and repair time distributions, in which repair is deferred until some condition on the collection of failed components is satisfied, and, then, proceeds until reaching the state without failed components, with failure rates much smaller than repair rates and not too different output rates from states with deferred repair.
On the use of asymmetric-shaped tapers for speaker verification using i-vectors

This paper presents asymmetric-shaped tapers (or windows) for speaker recognition. Symmetric tapers (e.g., hamming), having the linear phase property and longer time delay, are widely used for short-time analysis of speech signals. Since human speech perception is relatively insensitive to short-time phase distortion, the linearity constraint on phase can be removed without any adverse effects. Use of asymmetric tapers, having better magnitude response and shorter time delay, in speaker recognition can lead to a better recognition performance. Speaker verification results on the telephone and microphone speech of the latest NIST 2010 SRE corpus show that the asymmetric-shaped tapers perform better than the symmetric hamming window.
A context analysis method for constructing reliable embedded systems

This paper proposes a context-dependent requirements analysis method called CAMEmb (Context Analysis Method for Embedded systems) consisting of UML-based context modeling and formal verification support. In CAMEmb, a context model is constructed from initial system requirements by using a UML Profile for Context Analysis. This context model clarifies the relations among hardware components and contexts. After that, the context model is verified in order to check whether a target system behaves correctly under the expected contexts.
An accessible hydrogeological tool to monitor critical groundwater resources in hard-rock aquifers

Groundwater resources are increasingly being relied on in rural areas for income generation and food security. However, there is currently a lack of simple, yet accessible hydrogeological tools to monitor critical groundwater resources, both for quantity and quality. This is particularly true in developing countries underlain by fractured hard rock aquifers, with low productivities. Electrical conductivity (EC) meters are presented here as an easy-to-use tool that can provide real-time data collection to enhance routine groundwater monitoring in rural areas. A program was established within a fractured hard rock watershed for over a year in Rajasthan, India to determine the effectiveness and controls over EC as a monitoring tool. The initial groundwater quality in this region was largely influenced by rainfall, modified by evapotranspiration with recognizable input from water-rock interaction in the later months following the monsoon season. Chloride concentrations were linearly correlated with EC in all of the sampled groundwater, but the strength of the correlation attenuated in the months following the monsoon. Recharge rates were estimated using the chloride mass balance (CMB) approach, and then compared to the recharge rates derived from using EC as a surrogate for in what is referred to here as the CMB-EC approach. Recharge rates estimated from the CMB and CMB-EC methods were statistically similar (p=0.44).
A simple and effective approach for border noise removal from document images

When digitizing bound material like books or magazines, marginal noise appears along the page border. This noise consists of undesired text parts from the neighboring page and/or speckles that result from the binarization process. When a keyword based search is performed in a digitized collection, textual noise in particular poses problems since the returned search results might correspond to textual noise instead of actual contents of the page. Manually removing marginal noise for each page is not feasible in large scale digitization projects. In this paper, we present a simple and effective approach for removing both textual and non-textual noise by finding borders of noise regions using projection profile analysis. We demonstrate the effectiveness of our approach by evaluating it quantitatively on the widely used University of Washington (UW3) dataset. The results show that our approach reduces the noise ratio from 70% to 20% while retaining more than 99% of actual page contents. Comparison with state-of-the-art approaches shows that our algorithm performs comparable to them, while being simple to understand and easy to implement. We also provide an open source implementation of our method as part of the OCRopus OCR system.
Observed Properties of Exoplanets : Masses, Orbits, and Metallicities(Origins : From Early Universe to Extrasolar Planets)

precision (3 m s −1 ) and long duration. The 104 planets detected in this survey have minimum masses (M sin i )a s low as 6MEarth, orbiting between 0.02 and 6 AU. The core-accretion model of planet formation is supported by four observations: 1) The mass distribution rises toward the lowest detectable masses, dN /dM ∝ M −1.0 . 2) Stellar metallicity correlates strongly with the presence of planets. 3) One planet (1.3 MSat) has a massive rocky core, MCore ≈ 70 MEarth. 4) A super-Earth of ∼ 7 MEarth has been discovered. The distribution of semi-major axes rises from 0.3 – 3.0 AU (dN /d log a) and extrapolation suggests that ∼12% of the FGK stars harbor gas-giant exoplanets within 20 AU. The median orbital eccentricity .
Analysis of Replica Placement under Expiration-Based Consistency Management

Expiration-based consistency management is widely used to keep replicated contents up-to-date in the Internet. The effectiveness of replication can be characterized by the communication costs of client accesses and consistency management. Both costs depend on the locations of the replicas. This paper investigates the problem of placing replicas in a network where replica consistency is managed by the expiration-based scheme. Our objective is to minimize the total cost of client accesses and consistency management. By analyzing the communication cost of recursive validations for cascaded replicas, we prove that in the optimal placement scheme, the nodes not assigned replicas induce a connected subgraph that includes the origin server. Our results are generic in that they apply to any request arrival patterns. Based on the analysis, an O(D)-time algorithm is proposed to compute the optimal placement of the replicas, where D is the sum of the number of descendants over all nodes in the routing tree
Evil Acts and Malicious Gossip

Although person perception is central to virtually all human social behavior, it is ordinarily studied in isolated individual perceivers. Conceptualizing it as a socially distributed process opens up a variety of novel issues, which have been addressed in scattered literatures mostly outside of social psychology. This article examines some of these issues using a series of multiagent models. Perceivers can use gossip (information from others about social targets) to improve their ability to detect targets who perform rare negative behaviors. The model suggests that they can simultaneously protect themselves against being influenced by malicious gossip intended to defame specific targets. They can balance these potentially conflicting goals by using specific strategies including disregarding gossip that differs from a personally obtained impression. Multiagent modeling demonstrates the outcomes produced by different combinations of assumptions about gossip, and suggests directions for further research and theoretical development.
The Growth of Private Universities in Kenya: Implications for Gender Equity in Higher Education.

The establishment of private universities in Kenya and Africa is relatively new. At independence (1960s) there were about seven universities on the continent. However, by 2005 there were 85 private and 316 public universities in Africa (Kihara 2005). Kenya is leading in this expansion of private higher education in East Africa with 16 in 2006 compared to three in 1980. This article examines the implications of the growth of private universities on gender equity in higher education in Kenya. The article is based on two studies conducted in Kenya in 2002 and 2004 on the participation of women in university management. These two studies and a survey of literature on student enrolment in private and public universities reveal that private universities are providing increased opportunities for women to access higher education both as students and staff. Increased opportunities are provided through flexible admission and recruitment criteria, a conducive working environment and the appointment of more women into senior management positions. Resume La creation d’universites privees au Kenya et en Afrique est relativement quelque chose de nouveau. Au moment de l’independance (1960), il y’avait environ sept universites sur le continent. Cependant, en 2005 il y’avait 85 universites privees et 316 universites publiques en Afrique (Kihara 2005). Le Kenya est a la tete de cette expansion de l’enseignement superieur prive en Afrique de l’Est avec 16 universites privees en 2006 contre trois en 1980. Cet article examine les implica
False Negatives Are a Significant Feature of Next Generation Sequencing Callsets

Short-read, next-generation sequencing (NGS) is now broadly used to identify rare or de novo mutations in population samples and disease cohorts. However, NGS data is known to be error-prone and post-processing pipelines have primarily focused on the removal of spurious mutations or “false positives” for downstream genome datasets. Less attention has been paid to characterizing the fraction of missing mutations or “false negatives” (FN). Here we interrogate several publically available human NGS autosomal variant datasets using corresponding Sanger sequencing as a truth-set. We examine both low-coverage Illumina and high-coverage Complete Genomics genomes. We show that the FN rate varies between 3%-18% and that false-positive rates are considerably lower (<3%) for publically available human genome callsets like 1000 Genomes. The FN rate is strongly dependent on calling pipeline parameters, as well as read coverage. Our results demonstrate that missing mutations are a significant feature of genomic datasets and imply additional fine-tuning of bioinformatics pipelines is needed. To address this, we design a phylogeny-aware tool [PhyloFaN] which can be used to quantify the FN rate for haploid genomic experiments, without additional generation of validation data. Using PhyloFaN on ultra-high coverage NGS data from both Illumina HiSeq and Complete Genomics platforms derived from the 1000 Genomes Project, we characterize the false negative rate in human mtDNA genomes. The false negative rate for the publically available mtDNA callsets is 17-20%, even for extremely high coverage haploid data.
Neural Networks for Emotion Classification

It is argued that for the computer to be able to interact with humans, it needs to have the communication skills of humans. One of these skills is the ability to understand the emotional state of the person. This thesis describes a neural network-based approach for emotion classification. We learn a classifier that can recognize six basic emotions with an average accuracy of 77% over the Cohn-Kanade database. The novelty of this work is that instead of empirically selecting the parameters of the neural network, i.e. the learning rate, activation function parameter, momentum number, the number of nodes in one layer, etc. we developed a strategy that can automatically select comparatively better combination of these parameters. We also introduce another way to perform back propagation. Instead of using the partial differential of the error function, we use optimal algorithm; namely Powell's direction set to minimize the error function. We were also interested in construction an authentic emotion databases. This is a very important task because nowadays there is no such database available. Finally, we perform several experiments and show that our neural network approach can be successfully used for emotion recognition.
An access strategy selection in Cognitive Radio networks and QoS provisioning

The basic notion behind Cognitive Radio (CR) is that secondary users (SU) access the under-utilized licensed spectrum of primary users (PU) in an opportunistic manner. It assuages the problem of spectrum scarcity, which is a major concern as many applications and devices nowadays are using wireless medium. Before carrying on any transmission, the SU senses the licensed spectrum and predicts whether the spectrum is preoccupied by PU. Access strategy selection is a very crucial issue for CR networks, because the SU needs to satisfy the interference constraint caused to the PU as well as the quality-of-service (QoS) requirements. In this paper the access strategies for the spectrum access, i.e., the underlay strategy and the overlay strategy have been discussed for low and high transmit power of SU transmitter. The relationship between effective capacity and the quality of service exponent has also been presented in this paper.
Application of Quantitative Analysis Method in Traffic Volume Prediction

Predictive analysis about China railway parcel is an urgent task for railway intelligent transportation system (RITS). This paper proposes a novel parcel traffic volume predictive method named FCP (fuzzy cell prediction), which provides an effective means to build strategic models and enhances the predictive ability of interactive and hierarchical analysis. The key components of FCP are traffic data process mode which map variable to infinite spaces. The variable multiple correlation problem are solved. Applications to maximize transport capacity suggest that FCP is a contributive and powerful tool for the RITS.
Evaluation of clustering algorithms for Polish Word Sense Disambiguation

Word Sense Disambiguation in text is still a difficult problem as the best supervised methods require laborious and costly manual preparation of training data. Thus, this work focuses on evaluation of a few selected clustering algorithms in task of Word Sense Disambiguation for Polish. We tested 6 clustering algorithms (K-Means, K-Medoids, hierarchical agglomerative clustering, hierarchical divisive clustering, Growing Hierarchical Self Organising Maps, graph-partitioning based clustering) and five weighting schemes. For agglomerative and divisive algorithm 13 criterion function were tested. The achieved results are interesting, because best clustering algorithms are close in terms of cluster purity to precision of supervised clustering algorithm on the same dataset, using the same features.
A density-based algorithm for detecting anomalous trajectories

Fast growth of mobile sensing technologies, like GPS in smart phones, made capturing position data in the form of trajectories easy. But detecting anomalous trajectories, which are grossly different from remaining trajectories, is a major challenge in the surveillance domain and a big data problem. In the present work, a novel density-based method has been proposed and implemented for detection of anomalous trajectories. We introduced the concept of Hausdorff-neighborhood-trajectory, which makes our approach capable of handling border trajectory situations.
Architecture on demand design for high-capacity optical SDM/TDM/FDM switching

Reconfigurable optical add/drop multiplexers (ROADMs) are key elements in operators' backbone networks. The breakthrough node concept of architecture on demand (AoD) permits us to design optical nodes with higher flexibility with respect to ROADMs. In this work, we present a five-step algorithm for designing AoD instances according to some given traffic requests, which are able to support subwavelength time switching up to wavelength/superchannel/fiber switching. We evaluate AoD performancein terms of power consumption and number of backplane optical cross-connections. Furthermore, we discuss trade-offs involved in the migration from a fixed to a flexible grid with regard to the optical node size, capacity, and power consumption. We compare several ROADM architectures proposed in the literature with AoD in terms of power consumption and cost. We also study different technologies for enhancing the scalability of AoD. Results show that AoD can bring significant power savings compared to other architectures while offering a throughput of hundreds of terabits per second.
Sex differences and representative values for 6 lower extremity alignment measures.

CONTEXT A discrepancy in anterior cruciate ligament (ACL) injury rates exists between men and women. Structural differences between the sexes often are implicated as a factor in this discrepancy. Researchers anecdotally assume that men and women tend to display different normative values for certain lower extremity alignments, but published information about these values is limited. OBJECTIVE To evaluate the effect of sex on 6 measures of lower extremity alignment and to report representative values of these measures from a sample of active adults and elite athletes. DESIGN Descriptive cohort design. SETTING University research laboratory. PATIENTS OR OTHER PARTICIPANTS A total of 118 healthy adults (57 men: age = 21.1 +/- 3.0 years, height = 179.1 +/- 7.3 cm, mass = 79.8 +/- 13.0 kg; 61 women: age = 20.0 +/- 1.6 years, height = 167.7 +/- 6.7 cm, mass = 62.7 +/- 5.5 kg) volunteered. MAIN OUTCOME MEASURE(S) Six common measures of lower extremity posture (navicular drop, tibial varum, quadriceps angle, genu recurvatum, anterior pelvic tilt, femoral anteversion) were collected using established methods. One measurement was taken for each participant for each lower extremity alignment. We measured the right lower extremity only. RESULTS Compared with men, women demonstrated larger quadriceps angles, more genu recurvatum, greater anterior pelvic tilt, and more femoral anteversion. CONCLUSIONS We observed differences between men and women for 4 of the 6 lower extremity alignments that we measured. Future researchers should focus on identifying how sex and skeletal alignment affect biomechanical performance of functional tasks and what these differences specifically mean regarding the discrepancy in anterior cruciate ligament injury rates between the sexes.
Dystroglycan and Mitochondrial Ribosomal Protein L34 Regulate Differentiation in the Drosophila Eye

Mutations that diminish the function of the extracellular matrix receptor Dystroglycan (DG) result in muscular dystrophies, with associated neuronal migration defects in the brain and mental retardation e.g. Muscle Eye Brain Disease. To gain insight into the function of DG in the nervous system we initiated a study to examine its contribution to development of the eye of Drosophila melanogaster. Immuno-histochemistry showed that DG is concentrated on the apical surface of photoreceptors (R) cells during specification of cell-fate in the third instar larva and is maintained at this location through early pupal stages. In point mutations that are null for DG we see abortive R cell elongation during differentiation that first appears in the pupa and results in stunted R cells in the adult. Overexpression of DG in R cells results in a small but significant increase in their size. R cell differentiation defects appear at the same stage in a deficiency line Df(2R)Dg248 that affects Dg and the neighboring mitochondrial ribosomal gene, mRpL34. In the adult, these flies have severely disrupted R cells as well as defects in the lens and ommatidia. Expression of an mRpL34 transgene rescues much of this phenotype. We conclude that DG does not affect neuronal commitment but functions R cell autonomously to regulate neuronal elongation during differentiation in the pupa. We discuss these findings in view of recent work implicating DG as a regulator of cell metabolism and its genetic interaction with mRpL34, a member of a class of mitochondrial genes essential for normal metabolic function.
MEK and TGF-beta Inhibition Promotes Reprogramming without the Use of Transcription Factor

The possibility of replacing the originally discovered and widely used DNA reprogramming transcription factors is stimulating enormous effort to identify more effective compounds that would not alter the genetic information. Here, we describe the generation of induced pluripotent stem cells (iPSc) from head-derived primary culture of mouse embryonic cells using small chemical inhibitors of the MEK and TGF-beta pathways without delivery of exogenous transcription factors. These iPSc express standard pluripotency markers and retain their potential to differentiate into cells of all germ layers. Our data indicate that head-derived embryonic neural cells might have the reprogramming potential while neither the same primary cells cultivated over five passages in vitro nor a cell population derived from adult brain possesses this capacity. Our results reveal the potential for small molecules to functionally replace routinely used transcription factors and lift the veil on molecular regulation controlling pluripotency. The conditions described here could provide a platform upon which other genome non integrative and safer reprogramming processes could be developed. This work also shows novel potential for developing embryonic neural cells.
Coexistence in Metacommunities : A discrete time , finite patch approach

Metapopulation theory has emerged as a popular paradigm for studying the abundance and distribution of species in a fragmented landscape. A recent extension to metapopulation theory has been the development of metacommunity theory, which broadens the framework to incorporate the interacting dynamics of multiple species. Much of the work using a metacommunity framework has focused on determining the necessary conditions for species coexistence. Another fundamental but neglected question is the distribution of communities in a landscape and what factors drive occupancy patterns. We introduce a stochastic spatially implicit, discrete time, finite patch metacommunity model to examine expected distributions of multiple species given competition between species. We use the notion of quasi-stationarity to understand the probability distribution of different patch occupancy patterns prior to extinction. We find competition between species reduces the abundance of the lesser competitor. In addition, our novel application of quasi-stationary probability distributions elucidates the relative stability of the multiple species states and single species states. We contrast our model