Miracle-dz/UniFAR-FTUs huggingface dataset:
Insider attack attribution using signal strength-based hyperbolic location estimation

A rogue insider, in a wireless network, is an authenticated member that exploits possession of a valid identity in order to launch an attack. A typical example is the transmission of a verifiable message containing false or incomplete information. An important step, in enabling the network authorities to attribute an attack message to its originator, involves locating the physical source of the transmission. We propose a probabilistic scheme to determine the location of a transmitting rogue, with a degree of confidence, using the relative signal strength received by neighboring devices, even if the effective isotropic radiated power (EIRP) employed by the rogue is unknown. The relative received signal strength (RSS) between pairs of trusted receivers are combined with a range of possible EIRP values to construct an area in Euclidian space bounded by minimum and maximum distance hyperbolas. The area contained within the intersection of multiple hyperbola pairs pinpoints the location of the rogue transmitter with a specific level of confidence. Copyright © 2008 John Wiley & Sons, Ltd.
Strain for CMOS performance improvement

Device improvement with strain engineering is considered a way to enhance the carrier mobility. Several stress-transfer techniques (such as etch-stop liner, stress transfer technique, e-SiGe) using extra integration process into an existing baseline process is demonstrated. In addition, new preparation techniques of strained-Si surface (e.g. biaxial tensile stress) and different substrate orientation to enhance mobility are introduced. The challenges and vitality of each method are discussed and compared. In addition, we highlight how the stress oriented from the layout geometry affects the device electrical behavior. The issues and improvement in the circuit level device modeling are discussed.
Chapter PBXIP 1 is overexpressed in astrocytoma and promotes tumor cell growth and migration

Background: Glial brain tumors cause considerable mortality and morbidity in children and adults. Innovative targets for therapy are needed to improve survival and reduce long-term sequelae. Aim of this study is to find a candidate tumor-promoting protein, abundantly expressed in tumor cells but not in normal brain tissues, as a potential target for therapy.
RFFE: A Buffer Cache Management Algorithm for Flash-Memory-Based SSD to Improve Write Performance

Flash-memory-based solid-state drives use multiple NAND flash memory chips as storage media and deploy a large-sized random access memory (RAM) inside it. This RAM buffer absorbs the read and write requests by file systems, and thus the resulting write requests to NAND flash memory are determined by the buffer replacement scheme. Many of the previously proposed algorithms concentrate on improving the random write performance by reordering the writes, addressing the temporal locality, or evicting the clean pages beforehand. However, the sequential write patterns in the incoming write stream are not completely utilized by the flash translation layer; this increases garbage collection overhead. To overcome this limitation, we propose a novel algorithm, called random first flash enlargement (RFFE), to improve the performance of the write operation. The algorithm identifies the interleaved sequential writes and builds various policy decisions, and the write sequence is constructed by contemplating the flash memory characteristics. In particular, the write stream is written into an appropriate log block area. Trace driven simulation is compared with the previously proposed least recently used fully-associative sector translation (FAST), block padding least recently used (BPLRU), and recently-evicted-first (REF) buffer management schemes. The result shows that the RFFE outperforms the previously proposed schemes with respect to merge, erase, and write count.
Ontologies in Enterprise Application: Dimensional Comparison

Many important categories of applications such as information integration, data analytics, and personal assistance require access to a general store of knowledge. The usefulness of such a store depends not only on its factual content, but also on its conceptual framework, or ontology. Ontologies vary markedly in their characteristics, and the value of an ontology varies according to the purpose for which it will be used. Thus, selecting one or more ontologies suitable for an application can be challenging. We have created a set of dimensions to consider when making this selection. Although these dimensions are motivated by the needs of conversational assistance applications, they can benet the development of a wide variety of enterprise applications. We applied these dimensions to four large, general-purpose ontologies|Cyc, Freebase, SUMO, and YAGO|and made qualitative comparisons between them. General-purpose ontologies are playing an increasingly central role in many important categories of applications such as information integration, distributed knowledge management, data analytics, and personal assistance. These ontologies aim to provide a framework for better organization and access of data, eective information and knowledge sharing, reliable information exchange, and improved coordination between distinct organizations or among members of the same organization. Consequently, companies face an increasing need to be able to choose the most suitable ontologies for their applications. This paper addresses that need. We aim to provide an evaluative framework that may be applied to assess the usefulness of an ontology for any particular commercial application. Specically, we set out to dene
Outlier and influence diagnostics for meta‐analysis

The presence of outliers and influential cases may affect the validity and robustness of the conclusions from a meta‐analysis. While researchers generally agree that it is necessary to examine outlier and influential case diagnostics when conducting a meta‐analysis, limited studies have addressed how to obtain such diagnostic measures in the context of a meta‐analysis. The present paper extends standard diagnostic procedures developed for linear regression analyses to the meta‐analytic fixed‐ and random/mixed‐effects models. Three examples are used to illustrate the usefulness of these procedures in various research settings. Issues related to these diagnostic procedures in meta‐analysis are also discussed. Copyright © 2010 John Wiley & Sons, Ltd.
From disability to not self-sufficiency: an application of latent variables models

Riassunto: In questo lavoro si propone un’applicazione dei modelli a classi latenti per la quantificazione della non autosufficienza negli anziani, con particolare riferimento ad un’area territoriale dell’Italia centrale. A tal fine sono stati utilizzati i dati dell’indagine ISTAT Multiscopo sulle Condizioni di salute e ricorso ai servizi sanitari 1999-2000, relativi alle variabili che rilevano le difficolta nelle attivita quotidiane. La selezione del modello ha portato all’individuazione di quattro classi latenti che dividono gli anziani in autosufficienti, con disabilita lieve, con non autosufficienza iniziale e non autosufficienti. Successivamente, mediante un modello di Rasch per variabili ordinali e stato attribuito a ciascun anziano un punteggio sulla scala del tratto latente della non autosufficienza allo scopo di verificare la bonta della classificazione ottenuta.
Joint user selection and multicast beamforming in the downlink cochannel

The problem of joint user selection and multicast beamforming design in the downlink cochannel is studied in our paper. The primary goal is to maximize the number of served users, and minimizing the transmitting power is the second one. We reformulate the problem into a form suitable for analyzing by introducing two-value auxiliary variables, then relax it into a general problem. Different approximations are discussed to turn the general problem into easy-handling problems, which covering several works in both unicast and multicast scenes. An algorithm is proposed based on analyzing two characteristics in the multicast problem, inactive signal-to-interface-noise (SINR) constraints and non-rank-one beamforming covariance matrices. In simulations, three approximate functions are chosen, and their influence on the algorithm in performance and complexity is compared. Then our algorithm and MDR algorithm in [1] is contrasted and the relation between performance gap of two algorithms and characteristics in the multicast problem is discussed at last.
Diagnostically Lossless Compression-2 of Medical Images

A method is proposed for compression of medical images. The approach is called DLC2 (Diagnostically Lossless Compression-2). To get lossless effect, we use lossy coding followed by error image coding. An NNVQ (Neural Network Vector Quantizer) is used for lossy compression and Huffman coding is used to code the difference image losslessly. It is an spatial domain technique. No frequency domain transformations are required, this makes the proposed scheme simple and computationally economical. Proposed method can achieve higher compression ratios than existing standard lossless compression techniques and also meets the legal requirement of medical image archiving
Hierarchical key management scheme using Hyper Elliptic Curve Cryptography in Wireless Sensor Networks

Wireless Sensor Network (WSN) be a large scale network with thousands of tiny sensors moreover is of utmost importance as it is used in real time applications. Currently WSN is required for up-to-the-minute applications which include Internet of Things (IOT), Smart Card, Smart Grid, Smart Phone and Smart City. However the greatest issue in sensor network is secure communication for which key management is the primary objective. Existing key management techniques have many limitations such as prior deployment knowledge, transmission range, insecure communication and node captured by the adversary. The proposed novel Track-Sector Clustering (TSC) and Hyper Elliptic Curve Cryptography (HECC) provides better transmission range and secure communication. In TSC, the overall network is separated into circular tracks and triangular sectors. Power Aware Routing Protocol (PARP) was used for routing of data in TSC, which reduces the delay with increased packet delivery ratio. Further for secure routing HECC was implemented with 80 bits key size, which reduces the memory space and computational overhead than the existing Elliptic Curve Cryptography (ECC) key management scheme.
Effects of a skin-massaging device on the ex-vivo expression of human dermis proteins and in-vivo facial wrinkles

Mechanical and geometrical cues influence cell behaviour. At the tissue level, almost all organs exhibit immediate mechanical responsiveness, in particular by increasing their stiffness in direct proportion to an applied mechanical stress. It was recently shown in cultured-cell models, in particular with fibroblasts, that the frequency of the applied stress is a fundamental stimulating parameter. However, the influence of the stimulus frequency at the tissue level has remained elusive. Using a device to deliver an oscillating torque that generates cyclic strain at different frequencies, we studied the effect(s) of mild skin massage in an ex vivo model and in vivo. Skin explants were maintained ex vivo for 10 days and massaged twice daily for one minute at various frequencies within the range of 65–85 Hz. Biopsies were analysed at D0, D5 and D10 and processed for immuno-histological staining specific to various dermal proteins. As compared to untreated skin explants, the massaging procedure clearly led to higher rates of expression, in particular for decorin, fibrillin, tropoelastin, and procollagen-1. The mechanical stimulus thus evoked an anti-aging response. Strikingly, the expression was found to depend on the stimulus frequency with maximum expression at 75Hz. We then tested whether this mechanical stimulus had an anti-aging effect in vivo. Twenty Caucasian women (aged 65-75y) applied a commercial anti-aging cream to the face and neck, followed by daily treatments using the anti-aging massage device for 8 weeks. A control group of twenty-two women, with similar ages to the first group, applied the cream alone. At W0, W4 and W8, a blinded evaluator assessed the global facial wrinkles, skin texture, lip area, cheek wrinkles, neck sagging and neck texture using a clinical grading scale. We found that combining the massaging device with a skin anti-aging formulation amplified the beneficial effects of the cream.
Physiological stress and cell death in marine phytoplankton: Induction of proteases in response to nitrogen or light limitation

The physiological processes of natural phytoplankton mortality due to environmental stress (vs. that caused by sedimentation and predation) are poorly understood. Cell survival was examined in batch cultures of the diatom Thalassiosira weissflogii and the chlorophyte Dunaliella tertiolecta during deprivation of fixed nitrogen or light. Despite severe impairment of photosynthetic efficiencies, both species remained viable during 2 weeks of N starvation. Under N stress, a specific protease was induced in the diatom, overall activity of proteases doubled, and there was gradual, selective loss of certain proteins, especially ribulose 1,5‐bisphosphate carboxylase/oxygenase. Light‐deprived diatoms were virtually unaffected, but the chlorophyte underwent catastrophic cell death after about 6 d of darkness. Cell death coincided with a large increase in protease activity and the induction of a specific protease. Although we cannot completely rule out roles for viruses or bacteria in the losses of cells, the consistent timing, the unique response to stress, and the coincident expression of a specific protease strongly suggest that the process is a form of autocatalyzed cell death, such as apoptosis. While of uncertain adaptive significance, phytoplankton cell death may have implications for species succession and cycling of organic matter in aquatic ecosysterns.
A Framework for Plagiarism Detection in Arabic Documents

We are developing a web-based plagiarism detection system to detect plagiarism in written Arabic documents. This paper describes the proposed framework of our plagiarism detection system. The proposed plagiarism detection framework comprises of two main components, one global and the other local. The global component is heuristics-based, in which a potentially plagiarized given document is used to construct a set of representative queries by using different best performing heuristics. These queries are then submitted to Google via Google's search API to retrieve candidate source documents from the Web. The local component carries out detailed similarity computations by combining different similarity computation techniques to check which parts of the given document are plagiarised and from which source documents retrieved from the Web. Since this is an ongoing research project, the quality of overall system is not
Ontology Based Scope Visualisation

This paper presents an original solution to visualize semantic information contained into an ontology. It is important to be able to record a large amount of data. On another hand, it is useful to be able to find a specific data among all the data recorded. The more data we have the higher is the difficulty to visualize them. The solution that is presented here below is based on a particular visualization of content based on filter. A Knowledge Management System provides the storage part. The storage is based on an ontology specifically designed for travels. The ontology provides a data structure with the creation of concepts. This structure allows at the same time the sorting of data and a clear visualization. The visualization is dynamic, it depends on the ontology relationship established.
Human Umbilical Cord Matrix Mesenchymal Stem Cells Suppress the Growth of Breast Cancer by Expression of Tumor Suppressor Genes

Human and rat umbilical cord matrix mesenchymal stem cells (UCMSC) possess the ability to control the growth of breast carcinoma cells. Comparative analyses of two types of UCMSC suggest that rat UCMSC-dependent growth regulation is significantly stronger than that of human UCMSC. Their different tumoricidal abilities were clarified by analyzing gene expression profiles in the two types of UCMSC. Microarray analysis revealed differential gene expression between untreated naïve UCMSC and those co-cultured with species-matched breast carcinoma cells. The analyses screened 17 differentially expressed genes that are commonly detected in both human and rat UCMSC. The comparison between the two sets of gene expression profiles identified two tumor suppressor genes, adipose-differentiation related protein (ADRP) and follistatin (FST), that were specifically up-regulated in rat UCMSC, but down-regulated in human UCMSC when they were co-cultured with the corresponding species’ breast carcinoma cells. Over-expression of FST, but not ADRP, in human UCMSC enhanced their ability to suppress the growth of MDA-231 cells. The growth of MDA-231 cells was also significantly lower when they were cultured in medium conditioned with FST, but not ADRP over-expressing human UCMSC. In the breast carcinoma lung metastasis model generated with MDA-231 cells, systemic treatment with FST-over-expressing human UCMSC significantly attenuated the tumor burden. These results suggest that FST may play an important role in exhibiting stronger tumoricidal ability in rat UCMSC than human UCMSC and also implies that human UCMSC can be transformed into stronger tumoricidal cells by enhancing tumor suppressor gene expression.
AIS Electronic Library (AISeL)

Customers’ post-adoption switching behavior among competing service providers, particularly among different online Social Network Sites (SNSs), is gaining increasing attention from both information system researchers and practitioners, as the size of user base is both a selling point and a source of revenue of SNS service providers. In this study, we draw on the uses and gratification theory to identify factors motivating an individual to switch SNSs. In addition, informed by social identity theory, we explore how individuals’ social identities on their current SNSs moderate the effects of the motivating factors on their intention to switch to a more attractive SNS. The results of our survey study reveal that the perceived relative values of a competing SNS (when compared with the user’s current SNS) positively influence his/her intention to switch to the competing SNS. However, the positive impact of perceived relative values on switching intention is mitigated by the user’s perception of his/her salient social identity on his/her current SNS. This study has significant implications for both academics and practitioners.
High-power photodiodes for analog applications

This talk reviews modified uni-traveling carrier (MUTC) photodiodes that have achieved high RF output power levels of up to 1.8 Watt. Discrete wideband photodiodes, narrowband photodiodes, and photodiode arrays and their applications in analog links and photonic microwave generation are discussed.
A Local Texture-Based Superpixel Feature Coding for Saliency Detection Combined with Global Saliency

Because saliency can be used as the prior knowledge of image content, saliency detection has been an active research area in image segmentation, object detection, image semantic understanding and other relevant image-based applications. In the case of saliency detection from cluster scenes, the salient object/region detected needs to not only be distinguished clearly from the background, but, preferably, to also be informative in terms of complete contour and local texture details to facilitate the successive processing. In this paper, a Local Texture-based Region Sparse Histogram (LTRSH) model is proposed for saliency detection from cluster scenes. This model uses a combination of local texture patterns and color distribution as well as contour information to encode the superpixels to characterize the local feature of image for region contrast computing. Combining the region contrast as computed with the global saliency probability, a full-resolution salient map, in which the salient object/region detected adheres more closely to its inherent feature, is obtained on the bases of the corresponding high-level saliency spatial distribution as well as on the pixel-level saliency enhancement. Quantitative comparisons with five state-of-the-art saliency detection methods on benchmark datasets are carried out, and the comparative results show that the method we propose improves the detection performance in terms of corresponding measurements.
A Multiuser Mmse-detector for Ds-cdma Robust to Timing Errors

The problem of estimating the sent bits in a DS-CDMA system using BPSK when the time-delays are not exactly known is considered. A minimum mean squared error detector is obtained for the system. It is shown that the performance is improved when the assumption of using an exact time-delays is dropped.
Technology in Healthy Aging Sensorimotor-Cognitive Couplings in the Context of Assistive Spatial Navigation for Older Adults

With advancing adult age, sensorimotor functioning, spatial processing, and the motivation to explore new environments decline, leading to impaired spatial navigation skills. Using a controlled virtual-world laboratory equipped with a treadmill interface, we examined how assistive navigation technologies differing in cognitive demand affect walking regularity and navigation performance in younger andolderadults.Relativetoanassistivedevice withlowcognitivedemands,older,butnotyounger adults'navigationperformance decreased with a cognitively more demanding device. Furthermore, older adults showed higher gait irregularity than younger adults, especially with the cognitively demanding device. We conclude that assistive navigation devices show promise in supporting older adults' pedestrian mobility if aging-induced increments in cognitive demands of spatial navigation and postural control are considered.
The Simple Soccer Machine Learning Environment

The RoboCup simulated soccer league is a dynamic, complex and uncertain environment which presents many challenges to machine learning techniques. The asynchronous design of the RoboCup simulation environment can create long and unpredictable delays in the effects of actions, often causing onerous training times. A new environment known as Simple Soccer is proposed which, while retaining much of the dynamics and complexity of RoboCup, provides more certainty and less delay, thus increasing the viability of machine learning techniques. The goal of this work is to create an environment complex and dynamic enough that while low-level tactics may differ due to the removal of systematic uncertainty, high-level strategies directly applicable to the RoboCup environment can be developed. This paper specifies the Simple Soccer environment, and presents some initial learning results.