Miracle-dz/UniFAR-FTUs huggingface dataset:
Hiding Text Information in a Digital Image Based on Entropy Function

The paper is concerned with hiding information into a digital image, specifically, an English text is used to be hidden into a digital grey$scale image. The purpose of this research is to embed a maximum text data size into the most suitable image selected among several images based on the binary entropy function measurements of both the text and image. The embedding process is constrained by minimising the bit error rate. Our results show that the binary entropy function can be considered as a powerful tool to select a proper image for a predetermined text under the BER constrain.
The X‐ray variability of high‐redshift QSOs

We present an analysis of X-ray variability in a sample of 156 radio-quiet quasars taken from the ROSAT archive, covering a redshift range 0.1 2) in the sense that QSOs of the same X-ray luminosity are more variable at z > 2. We discuss possible explanations for this effect. The simplest explanation may be that high-redshift QSOs are accreting at a larger fraction of the Eddington limit than local AGNs.
Utilizing Color Information for the Gaze Control of an Active Vision System

Color plays an important role in human visual attention. Therefore, high color contrasts are often used for warning or information purposes (e.g. traffic signs, warning signs in industrial areas). In this work the color contrasts are evaluated in a perceptually uniform color space to achieve a human-like color classification. The results of the color analysis are organized in several feature maps and a conspicuity map which are parts of a larger attractivity representation. This attractivity representation is the bottom-up component of an attention model directing the gaze of an exploratory vision system.
Using Entity Information from a Knowledge Base to Improve Relation Extraction

Relation extraction is the task of extracting predicate-argument relationships between entities from natural language text. This paper investigates whether background information about entities available in knowledge bases such as FreeBase can be used to improve the accuracy of a state-of-the-art relation extraction system. We describe a simple and effective way of incorporating FreeBase’s notable types into a state-of-the-art relation extraction system (Riedel et al., 2013). Experimental results show that our notable typebased system achieves an average 7.5% weighted MAP score improvement. To understand where the notable type information contributes the most, we perform a series of ablation experiments. Results show that the notable type information improves relation extraction more than NER labels alone across a wide range of entity types and relations.
Identification of Estrogen Receptor-Related Receptor Gamma as a Direct Transcriptional Target of Angiogenin

Nuclear translocation of angiogenin (ANG) is essential for the proliferation of its target cells. ANG promotes rRNA synthesis, while whether it regulates mRNA transcription remains unknown. Using the chromatin immunoprecipitation method, we have identified 12 ANG-binding sequences. One of these sequences lies in the estrogen receptor-related receptor gamma (ERRγ) gene which we designated as ANG-Binding Sequence within ERRγ (ABSE). ABSE exhibited ANG-dependent repressor activity in the luciferase reporter system. Down-regulation of ANG increased ERRγ expression, and active gene marker level at the ABSE region. The expression levels of ERRγ targets genes, p21WAF/CIP and p27KIP1, and the occupation of ERRγ on their promoter regions were increased in ANG-deficient cells accordingly. Furthermore, knockdown of ERRγ promoted the proliferation rate in ANG-deficient breast cancer cells. Finally, immunohistochemistry staining showed negative correlation between ANG and ERRγ in breast cancer tissue. Altogether, our study provides evidence that nuclear ANG directly binds to the ABSE of ERRγ gene and inhibits ERRγ transcription to promote breast cancer cell proliferation.
Vision-based human-computer interface using hand gestures

The proliferation of interactive digital TV applications will depend on their complexity and ease of use. The investigation of simple, intuitive human-machine interfaces is therefore crucial for their success. While the operation of most home devices is today based on using remote control, in this paper we investigate an alternative solution using a visual input of the users's gestures. The proposed system recognises point and click gestures of the user's hand, which is enough to control simple iconic user interfaces of the interactive TV applications. Position of the user's hand is tracked using Kalman filtering, and shape analysis is performed to detect the pose of the forefinger. A finger bending gesture is recognized and transformed into a click action on the screen. Simulations show that the system performs well in uncontrolled home environment under different lighting conditions and cluttered backgrounds and thus could be used in future smart home devices.
Why eve and mallory (also) love webmasters: a study on the root causes of SSL misconfigurations

Previous research showed that the SSL infrastructure is a fragile system: X.509 certificate validation fails for a non-trivial number of HTTPS-enabled websites resulting in SSL warning messages presented to users. Studies revealed that warning messages do not provide easy-to-understand information or are ignored by webbrowser users. SSL warning messages are a critical component in the HTTPS infrastructure and many attempts have been made to improve these warning messages. However, an important question has not received sufficient attention yet: Why do webmasters (deliberately) deploy non-validating, security-critical X.509 certificates on publicly available websites? In this paper, we conduct the first study with webmasters operating non-validating X.509 certificates to understand their motives behind deploying those certificates. We extracted the non-validating certificates from Google's webcrawler body of X.509 certificates, informed webmasters about the problem with the X.509 certificate configuration on their website and invited a random sample of the respective webmasters to participate in our study. 755 webmasters participated, allowing us insight into their motives. While one third of them admitted to having misconfigured their webserver accidentally, two thirds of them gave reasons for deliberately using a non-validating X.509 certificate.
Note Value Recognition for Rhythm Transcription Using a Markov Random Field Model for Musical Scores and Performances of Piano Music

This paper presents a statistical method for music transcription that can estimate score times of note onsets and offsets from polyphonic MIDI performance signals. Because performed note durations can deviate largely from score-indicated values, previous methods had the problem of not being able to accurately estimate offset score times (or note values) and thus could only output incomplete musical scores. Based on observations that the pitch context and onset score times are influential on the configuration of note values, we construct a context-tree model that provides prior distributions of note values using these features and combine it with a performance model in the framework of Markov random fields. Evaluation results showed that our method reduces the average error rate by around 40 percent compared to existing/simple methods. We also confirmed that, in our model, the score model plays a more important role than the performance model, and it automatically captures the voice structure by unsupervised learning.
Secure Kernel Machines against Evasion Attacks

Machine learning is widely used in security-sensitive settings like spam and malware detection, although it has been shown that malicious data can be carefully modified at test time to evade detection. To overcome this limitation, adversary-aware learning algorithms have been developed, exploiting robust optimization and game-theoretical models to incorporate knowledge of potential adversarial data manipulations into the learning algorithm. Despite these techniques have been shown to be effective in some adversarial learning tasks, their adoption in practice is hindered by different factors, including the difficulty of meeting specific theoretical requirements, the complexity of implementation, and scalability issues, in terms of computational time and space required during training. In this work, we aim to develop secure kernel machines against evasion attacks that are not computationally more demanding than their non-secure counterparts. In particular, leveraging recent work on robustness and regularization, we show that the security of a linear classifier can be drastically improved by selecting a proper regularizer, depending on the kind of evasion attack, as well as unbalancing the cost of classification errors. We then discuss the security of nonlinear kernel machines, and show that a proper choice of the kernel function is crucial. We also show that unbalancing the cost of classification errors and varying some kernel parameters can further improve classifier security, yielding decision functions that better enclose the legitimate data. Our results on spam and PDF malware detection corroborate our analysis.
GENOME DIVERGENCE AND THE GENETIC ARCHITECTURE OF BARRIERS TO GENE FLOW BETWEEN LYCAEIDES IDAS AND L. MELISSA

Genome divergence during speciation is a dynamic process that is affected by various factors, including the genetic architecture of barriers to gene flow. Herein we quantitatively describe aspects of the genetic architecture of two sets of traits, male genitalic morphology and oviposition preference, that putatively function as barriers to gene flow between the butterfly species Lycaeides idas and L. melissa. Our analyses are based on unmapped DNA sequence data and a recently developed Bayesian regression approach that includes variable selection and explicit parameters for the genetic architecture of traits. A modest number of nucleotide polymorphisms explained a small to large proportion of the variation in each trait, and average genetic variant effects were nonnegligible. Several genetic regions were associated with variation in multiple traits or with trait variation within‐ and among‐populations. In some instances, genetic regions associated with trait variation also exhibited exceptional genetic differentiation between species or exceptional introgression in hybrids. These results are consistent with the hypothesis that divergent selection on male genitalia has contributed to heterogeneous genetic differentiation, and that both sets of traits affect fitness in hybrids. Although these results are encouraging, we highlight several difficulties related to understanding the genetics of speciation.
Correlated Frailty Models in the Analysis of Dependent Failure Times

The multivariate survival distributions are used for description of dependent failure times (life spans) (Clayton and Cuzik 1985; Hougaard 1995, Yashin et al. 1995). The convenient representation of such distributions involves semi-parametric (copula) structure of multivariate survival function. Such structure is important in applications where the cause for dependence between life spans is the subject of the study (Yashin and Iachine 1994, Yashin and Iachine 1995a). It turns out that the wide class of frailty models allows for such a representation (Yashin et al. 1999). In genetic-epidemiological applications these models can be used to evaluate the presence and the magnitude of genetic influence on susceptibility to death as well as its variability in the population (Yashin and Iachine 1995b; 1997). In particular the correlated gamma frailty model applied to survival data on monozygotic (MZ) and dizygotic (DZ) twins can be used for this purpose. This model is characterized by the bivariate survival function ( ) ( ) 2 2 1 1 2 1 , , x T x T P x x S > > = [ ] 2 1 2 2 2 1 12 21 1 ) ( ) ( ) ( ) ( ) , ( 2 1 1
Profiling Android Vulnerabilities

In widely used mobile operating systems a single vulnerability can threaten the security and privacy of billions of users. Therefore, identifying vulnerabilities and fortifying software systems requires constant attention and effort. However, this is costly and it is almost impossible to analyse an entire code base. Thus, it is necessary to prioritize efforts towards the most likely vulnerable areas. A first step in identifying these areas is to profile vulnerabilities based on previously reported ones. To investigate this, we performed a manual analysis of Android vulnerabilities, as reported in the National Vulnerability Database for the period 2008 to 2014. In our analysis, we identified a comprehensive list of issues leading to Android vulnerabilities. We also point out characteristics of the locations where vulnerabilities reside, the complexity of these locations and the complexity to fix the vulnerabilities. To enable future research, we make available all of our data.
Smooth Mixed-Resolution GPU Volume Rendering

We propose a mixed-resolution volume ray-casting approach that enables more flexibility in the choice of downsampling positions and filter kernels, allows freely mixing volume bricks of different resolutions during rendering, and does not require modifying the original sample values. A C0-continuous function is obtained everywhere with hardware-native filtering at full speed by simply warping texture coordinates of samples in transition regions. Additionally, we propose a simple but powerful, flat texture packing scheme that supports mixing different resolution levels in a single 3D volume cache texture with a very simple and fast address translation scheme. Although this packing constrains full scalability, it enables mixing different resolution levels in GPU-based ray-casting with only a single rendering pass. We demonstrate our approach on large real-world data, obtaining a continuous scalar function and shading at brick boundaries, using single-pass ray-casting at real-time frame rates.
MGR : An Architecture for Problem Solving in Unstructured Environments

Model Generative Reasoning (MGR) is a problem solving architecture designed to support AI applications inunstructuredtask environments, i.e. in task environments in which the statistical quality, completeness, and relevance of the data that will be available to the problem solver at any particular time cannot be predicted reliably (Hartley et al., 1987; Coombs and Hartley, in pressa; Coombs et al., submitted). MGR was originally developed to solve problems in decision support for process control and fault diagnosis (Coombs and Hartley, in pressb). Current application areas include scenario-based meteorological data integration (Coombs et al., 1988) and automated nucleic-acid and protein sequence data analysis.
Development of a Full 3D NEGF Nano-CMOS Simulator

We describe the development of a new fully 3D NEGF simulator. At this stage we illustrate the use of the corresponding 3D NEGF solver to study, non-self consistently, the effect of the spatial inhomogeneities caused by stray charges and rough Si/SiO2 interface in nano-CMOS devices. These cannot be accurately described using perturbative techniques. The use of decoupled simulations is justifiable in the subthreshold region of device operation. In the presence of the above imperfections the current and charge density have a distinct 3D character
The Employment Creation Impact of the Addis Ababa Integrated Housing Program Draft

Severe housing shortages and high unemployment prevail in Addis Ababa. The Addis Ababa Integrated Housing Programme (AAIHDP) is an active labour market programme that attempts to tackle these problems simultaneously by creating and supporting Small and Medium Enterprises to construct housing using low-cost technologies novel for Ethiopia. This paper analyses the employment creation impact of the program and shows it to be negligible. Program participants do bene t from higher earnings and the program premium is highest for those at the bottom of the income distribution. Paradoxically, the program premium is due to the fact that the AAIHDP has created rms which are larger than pre-existing construction rms, though selection on unobservables cannot be ruled out. More generally, this paper, which is (one of the) rst to analyse the impact of SME support programs on employment and earnings in a developing country context, challenges the case for promoting SMEs to generate employment. This is a very rst draft: Please do not quote without permission!
Anti-proliferative activity of epigallocatechin-3-gallate and silibinin on soft tissue sarcoma cells

Disseminated soft tissue sarcomas (STS) present a therapeutic dilemma. The first-line cytostatic doxorubicin demonstrates a response rate of 30% and is not suitable for elderly patients with underlying cardiac disease, due to its cardiotoxicity. Well-tolerated alternative treatment options, particularly in palliative situations, are rare. Therefore, the present study assessed the anti-proliferative effects of the natural compounds epigallocatechin-3-gallate (EGCG), silibinin and noscapine on STS cells. A total of eight different human STS cell lines were used in the study: Fibrosarcoma (HT1080), liposarcoma (SW872, T778 and MLS-402), synovial sarcoma (SW982, SYO1 and 1273) and pleomorphic sarcoma (U2197). Cell proliferation and viability were analysed by 5-bromo-2′-deoxyuridine and MTT assays and real-time cell analysis (RTCA). RTCA indicated that noscapine did not exhibit any inhibitory effects. By contrast, EGCG decreased proliferation and viability of all cell lines except for the 1273 synovial sarcoma cell line. Silibinin exhibited anti-proliferative effects on all synovial sarcoma, liposarcoma and fibrosarcoma cell lines. Liposarcoma cell lines responded particularly well to EGCG while synovial sarcoma cell lines were more sensitive to silibinin. In conclusion, the green tea polyphenol EGCG and the natural flavonoid silibinin from milk thistle suppressed the proliferation and viability of liposarcoma, synovial sarcoma and fibrosarcoma cells. These compounds are therefore potential candidates as mild therapeutic options for patients that are not suitable for doxorubicin-based chemotherapy and require palliative treatment. The findings from the present study provide evidence to support in vivo trials assessing the effect of these natural compounds on solid sarcomas.
Modeling and deterministic robust control of a three-phase four-switch PWM voltage-source rectifier based on direct power control

This paper proposes a mathematical model of the three-phase four-switch pulse width modulated (PWM) voltage-source rectifier (VSR) based on direct power control (DPC). The derived model has the advantages of specific physical meaning, more versatile and precise, and most nonlinear control strategies of six-switch VSR could be utilized for four-switch VSR. By analyzing the proposed model, the dynamic of voltage difference between the two capacitors is considered a second-order dynamic. A compensation algorithm is applied to arrange its transition process to balance the voltage of the two capacitors. The filter inductors may be uncertain and the grid angular frequency changes slowly. That would lead to undesired high-order dynamic which generates more harmonics and affects the stability of the system. Deterministic robust control (DRC) is introduced to provide affordable performance under the dynamic of uncertain nonlinearities or above-mentioned parameters uncertainty. Test result shows the deterministic robust control based on proposed model is of excellent performance and robustness.
A Total Bandwidth Expanded Dual-Band GaN Doherty PA toward the LTE-A Carrier Aggregation Application

This paper presents a new theoretical concept, design implementation and experimental results of a compact dual-band GaN-HEMT Doherty power amplifier (DPA) with broad bandwidths. Measured results well agree with simulation. Small signal 3 dB bandwidths at 700 MHz band and 2.1 GHz band are 300MHz and 450 MHz, respectively. Total bandwidth of 750 MHz which covers the IM3 bandwidth of LTE-A signal is achieved. Drain efficiency at 6 dB back off exceeds 43% over 100 MHz bandwidths at both bands. The fabricated dual-band DPA is 135 × 65 mm2.
svlab - A Kernel Methods Package

Abstractsvlab is an extensible, object oriented, package for kernel based learningin R. Its main objective is to provide a tool kit consisting of basic kernelfunctionality, optimizers and high level algorithms such as Support VectorMachines and Kernel Principal Component Analysis which can be extendedby the user in a very modular way. Based on this infrastructure kernel-basedmethods can be easily be constructed and developed. 1 Introduction It is often diﬃcult to solve problems like classiﬁcation, regression and clustering—ormore generally: supervised and unsupervised learning—in the space in which theunderlying observations have been made. One way out is to project the observationsinto a higher-dimensional feature space where these problems are easier to solve,e.g., by using simple linear methods. If the methods applied in the feature spaceare only based on dot or inner products the projection does not have to be carriedout explicitely but only implicitely using kernel functions. This is often referred toas the “kernel trick”. More precisely, if a projection Φ : X → H is used the dotproduct hΦ(x),Φ(y)i can be represented by a kernel function kk(x,y) = hΦ(x),Φ(y)i, (1)
Mobility Management Technique for Real Time Traffic in 802.11 Networks

A practical mobility management technique aimed at reducing handoff latency to less than 10ms has been proposed and implemented in this paper. Handoff latency has been reduced by decoupling scanning from handoff execution process. This scheme has been implemented in the form of client side application with modifications to open source Madwifi new generation driver. Performance of this mobility management scheme has been evaluated on experimental Testbed for finding its suitability for real time applications. Effect of the proposed scheme on commercial VoIP client, Skype has been evaluated. Effect of the proposed handoff and optimized background scan on audio and video streaming has also been investigated. The results indicate that this mobility management technique can be used for real time traffic over 802.11 wireless networks.
Effects of SCA1, MJD, and DPRLA triplet repeat polymorphisms on cognitive phenotypes in a normal population of adolescent twins

The expansion of unstable trinucleotide CAG repeat polymorphisms of a number of genes causes several neurodegenerative disorders with decreased cognitive function, the severity of the disorder being related to allele length at the triplet repeat locus. While the effects of repeat length have been well studied in clinical samples, there has been little investigation of the effects of triplet repeat variation in the normal range for these genes. We have, therefore, examined linkage and association for three CAG triplet repeat markers (Spinocerebellar Ataxia Type 1, SCA1; Machado‐Joseph Disease, MJD; Dentatorubro‐pallidoluysian Atrophy, DRPLA) to assess their contribution to variation in cognitive ability (IQ, reading ability, processing speed) in a normal, unselected sample of adolescent twins (248 dizygotic (DZ) sibling pairs, aged 16 years). Association tests, performed in Mx and QTDT, showed a consistent positive association of SCA1 with Arithmetic (P = 0.04). While association was supported between SCA1 and Cambridge reading scores and between DRPLA and inspection time, results were inconsistent across software packages. Given the number of statistical tests performed, it is unlikely that trinucleotide repeat variation in the normal range for these genes influences variation in normal cognition. © 2006 Wiley‐Liss, Inc.