Miracle-dz/UniFAR-FTUs huggingface dataset:
An Innovative Approach for online Meta Search Engine Optimization

This paper presents an approach to identify efficient techniques used in Web Search Engine Optimization (SEO). Understanding SEO factors which can influence page’s ranking in search engine is significant for webmasters who wish to attract large number of users to their website. Different from previous relevant research, in this study we developed an intelligent Meta search engine which aggregates results from various search engines and ranks them based on several important SEO parameters. The research tries to establish that using more SEO parameters in ranking algorithms helps in retrieving better search results thus increasing user satisfaction. Initial results generated from Meta search engine outperformed existing search engines in terms of better retrieved search results with high precision.
SPA: A Secure and Private Auction Framework for Decentralized Online Social Networks

The security and privacy threats on e-commerce have attracted intensive attention recently. The explosive growth of online social networks (OSNs) has made them potential new great marketplaces for e-commerce, which, however, raise serious security and privacyconcerns. This is mainly due to the centralized system architecture where the service provider knows all users’ private data and becomes the single point of failure. To this end, we propose a secure and private auction framework, called SPA, for decentralized online social networks (DOSNs). SPA consists of three phases: identity initiation, buyer-seller matching, and private auction. It requires no trust among the participants but can provide security, privacy, authenticity, non-repudiation, and correctness for the auctions. We analyze the computation and communication complexities of the proposed private auction scheme, which are <inline-formula> <tex-math notation="LaTeX">$O(n+K)$</tex-math><alternatives><inline-graphic xlink:type="simple" xlink:href="li-ieq1-2494009.gif"/> </alternatives></inline-formula> for each node where <inline-formula><tex-math notation="LaTeX">$n$</tex-math> <alternatives><inline-graphic xlink:type="simple" xlink:href="li-ieq2-2494009.gif"/></alternatives></inline-formula> is the number of bidders and <inline-formula><tex-math notation="LaTeX">$K$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="li-ieq3-2494009.gif"/></alternatives></inline-formula> is the number of pricing points. In contrast, those of previous auction schemes are <inline-formula><tex-math notation="LaTeX">$O(nK)$</tex-math> <alternatives><inline-graphic xlink:type="simple" xlink:href="li-ieq4-2494009.gif"/></alternatives></inline-formula> at best. The storage complexity is significantly lower than before as well. Security and privacy of SPA are also analyzed. Extensive experiments are conducted to validate the efficiency of SPA.
Up-regulation of SERPINA3 correlates with high mortality of melanoma patients and increased migration and invasion of cancer cells

Serpin Peptidase Inhibitor, clade A member 3 (SERPINA3) was found to be abnormally overexpressed in a subset of melanoma tissue biopsies. High SERPINA3 expression was also associated with poor patient survival. In this study, we set out to test SERPINA3 protein's prognostic potential with a larger-sized and independent patient cohort, and to explore SERPINA3's function in melanoma cells. Tissue microarray-based immunohistochemistry analysis showed a significant increase in SERPINA3 expression in invasive and metastatic melanomas compared to normal nevi and melanoma-in-situ (P < 0.001, Chi-square test). In melanoma patients, high SERPINA3 expression was strongly associated with worse overall and disease specific survival at 5 years. Multivariate Cox regression analysis showed that SERPINA3 expression is an independent prognostic factor to predict melanoma patient clinical outcome. When SERPINA3 expression was selectively silenced using small interfering RNA molecules (siRNA) in cultured melanoma cell lines, cell migration and matrix invasion was significantly decreased, but no change in cell proliferation was observed. This study confirms the prognostic potential of SERPINA3 expression in human cutaneous melanoma and reveals the pro-migration and pro-invasion functions of this protein on melanoma cells.
Constructing Shared “Space”: Meaningfulness in Long-Distance Romantic Relationship Communication Formats

Via analysis of online survey data of 262 people aged 18 to 70 in the United States who have been involved in a long-distance romantic relationship (LDR) since 2005, we investigate how paper, audio, visual, and digital communication formats may be used and viewed as differentially meaningful. While in most intimate relationships, digital formats such as e-mail, messaging, and texts are frequently used, LDR couples are more likely to use and find most meaningful video chat and audio platforms compared with paper or digital formats. The reason for each platform’s meaningfulness varies according to survey respondents’ subjective understandings. Audio and visual formats are seen as meaningful because they offer intimacy, paper formats offer thoughtfulness, and digital formats offer ease as well as thoughtfulness. This research contributes to understanding the subjectively constructed meaning of ever-changing communication platforms, as well as definitions of shared sociomental spaces, for couples who are geographically separated.
Prototyping Incentive-Based Resource Assignment for Clouds in Community Networks

Wireless community networks are a successful example of a collective where communities operate ICT infrastructure and provide IP connectivity based on the principle of reciprocal resource sharing of network bandwidth. This sharing, however, has not extended to computing and storage resources, resulting in very few applications and services which are currently deployed within community networks. Cloud computing, as in today's Internet, has made it common to consume resources provided by public clouds providers, but such cloud infrastructures have not materialized within community networks. We analyse in this paper socio-technical characteristics of community networks in order to derive scenarios for community clouds. Based on an architecture for such a community cloud, we implement a prototype for the incentive-driven resource assignment component, deploy it in a test bed of community network nodes, and evaluate its behaviour experimentally. Our evaluation gives insight into how the deployed prototype components regulate the consumption of cloud resources taking into account the users' contributions, and how this regulation affects the system usage. Our results suggest a further integration of this regulation component into current cloud management platforms in order to open them up for the operation of an ecosystem of community cloud.
A novel geometrically invariant blind robust watermarking algorithm based on SVD and DCT

In this paper, we propose a novel blind image watermarking algorithm that is resistant to various attacks, including geometrical distortions. To embed the watermark, we first enlarge the image with a small scale to be fit for the next image blocking process. Secondly, the enlarged image is blocked into macroblocks and subblocks with adaptively determined sizes. Third, we scramble the watermark and embed it repeatedly into the coefficients that are obtained from the subblock-based SVD followed by macroblock-based DCT. Then, the watermarked image can be obtained by means of corresponding inverse transformations. The watermark extraction process is similar as the embedding one. Compared with others' works, the small-scale image enlargement, the adaptive image blocking and the watermark scrambling we present in this SVD-DCT based method play an important role in increasing the robustness to resist geometrical distortions. Experimental results demonstrate that our method can not only resist common image processing attacks, but also has good performance when the image has been scaled or cropped.
Grid Resource Allocation : Allocation Mechanisms and Utilisation Patterns

Grid systems have been put to remarkable use in recent years. Finding planets, rendering multi-million dollar movies, and helping to understand disease are just some of the examples grid systems have been used for. With business turning to towards using grid systems and looking to make them global mechanisms for service delivery, they are nicely poised to be an exciting future prospect. However the performance of a grid system is strongly related to how well grid resource allocation is performed. With many possible approaches to grid resource allocation we have to ask the question, what impact does the choice of resource allocation methodology have on the utilisation and performance of a Grid system? This paper addresses this question through the investigation of the characteristic allocation patterns for three different resource allocation mechanisms and their subsequent effect on resource utilisation within a simulated Grid system.
A Skill-Based PID Controller Using Artificial Neural Networks

The works for representing human skills are attracted, which human experts have cultivated in long experiences. Recently, there are many literatures with respect to skill-based controllers which human skills are represented by artificial neural networks. The technique for representing the human skills plays an important role in various industries, for example, manufacturing systems and process systems. In this paper, a skill-based PID control scheme is proposed, which extracts skills of human experts as PID gains. This controller is designed by using three-layered artificial neural networks. The effectiveness of the proposed skill-based PID control scheme is investigated through an application for a pressure control system. Furthermore, the characteristic of the PID controller constructed based on the human skills is interpreted in the frequency domain
Adaptive Robust Observer Design For A Class Of Uncertain Nonlinear Systems

In this paper the problem of state estimation for nonlinear uncertain systems in the presence of both parameter uncertainties and system disturbances will be discussed. An adaptive robust observer is proposed for a class of nonlinear uncertain systems, where two different categories of uncertainties are considered: the first category is the structured system uncertainty, which can be separated and expressed as the product of known functions of states and a set of unknown constants. The second category is the unstructured (interval) uncertainty, which is not separable but is bounded by a partially known function. It is further assumed that this partially known bounding function is convex to the set of unknown parameters, namely, the bounding function is no longer linear in parameters. The adaptive method is adopted to deal with the structured uncertainties, and the adaptive robust method is applied to deal with the unstructured uncertainty. Convergence analysis study shows that the state estimation errors can converge to zero asymptotically. This paper includes simulation studies to demonstrate the performance of the proposed observer.
Nanoparticle-Based and Bioengineered Probes and Sensors to Detect Physiological and Pathological Biomarkers in Neural Cells

Nanotechnology, a rapidly evolving field, provides simple and practical tools to investigate the nervous system in health and disease. Among these tools are nanoparticle-based probes and sensors that detect biochemical and physiological properties of neurons and glia, and generate signals proportionate to physical, chemical, and/or electrical changes in these cells. In this context, quantum dots (QDs), carbon-based structures (C-dots, grapheme, and nanodiamonds) and gold nanoparticles are the most commonly used nanostructures. They can detect and measure enzymatic activities of proteases (metalloproteinases, caspases), ions, metabolites, and other biomolecules under physiological or pathological conditions in neural cells. Here, we provide some examples of nanoparticle-based and genetically engineered probes and sensors that are used to reveal changes in protease activities and calcium ion concentrations. Although significant progress in developing these tools has been made for probing neural cells, several challenges remain. We review many common hurdles in sensor development, while highlighting certain advances. In the end, we propose some future directions and ideas for developing practical tools for neural cell investigations, based on the maxim “Measure what is measurable, and make measurable what is not so” (Galileo Galilei).
Analysis of steady-state excess mean-square-error of the least mean kurtosis adaptive algorithm

In this paper, the average of the steady state excess mean square error (ASEMSE) of the least mean kurtosis (LMK) adaptive algorithm is theoretically derived. It is done by applying the energy conservation behavior of adaptive filters and it is based on the n-th order correlations and cumulants theory. By doing so, the behavior of the recently proposed LMK can be predicted, so that it can be widely used. The behavior is compared with the various adaptive algorithms. Our study shows that it is possible to adjust the performance of the LMK. When the step size μ is carefully selected, the performance of the LMK can outperform the LMS.
Analysis of a Self-Organizing Algorithm for Energy Saving in Data Centers

The Cloud computing paradigm allows users to satisfy their increasing need for on-demand and remote computational services. These services are provided by data centers that often consume a huge amount of electrical power. Recently the ecoCloud algorithm has been proposed as a solution for saving energy by consolidating Virtual Machines on as few servers as possible, so as to hibernate the remaining servers and save energy. The ecoCloud approach founds on probabilistic processes: mapping and migration of VMs are driven by Bernoulli trials whose success probability depends on the utilization of single servers. These processes are self-organizing and decentralized, which makes them particularly efficient in large data centers. While in previous work the performance evaluation of ecoCloud was based on artificial traces, in this paper, a mathematical analysis is presented along with simulations fed with logs of real VMs. Results show that efficiency is very close to the theoretical minimum and comparable to that of one of the best centralized algorithms devised so far; in addition, ecoCloud notably reduces the frequency of events, such as VM migrations and server switches, that can deteriorate the quality of service.
A framework architecture supporting QoS-power trade-offs for heterogeneous network systems

Power awareness and quality of service (QoS) integration in wireless systems are two of the main research activities in wireless systems today. As frameworks are developed to handle dynamic reconfiguration, the need for a power optimization methodology to investigate alternative cross-layer configurations is imposed as critical. However, as networks become more complex and energy savings become critical, this leads to the consideration of constructs for treating QoS-power trade-offs and adjust to the heterogeneous nature of network systems. In this paper, we propose an interoperable architecture for a hybrid wired-wireless network, where communication is treated transparently and enhancements are proposed to improve QoS by the definition of a framework also supporting dynamic power optimization
Local Responses to Federal Grants: Evidence from the Introduction of Title I in the South

We analyze the effects of the introduction of Title I of the 1965 Elementary and Secondary Education Act, a large federal grants program designed to increase poor students' educational services and achievement. We focus on the South, the poorest region of the country. Title I increased school spending by $0.50 on the dollar in the average southern school district and by more in districts with less ability to offset grants through local tax reductions. Title I-induced increases in school budgets appear to have reduced high school dropout rates of whites, but not blacks.
Development of Measurable Security for a Distributed Messaging System

Systematically developed security metrics make it possible to gather sufficient and credible security evidence for runtime adaptive security management and off-line security engineering and management. This study introduces and analyzes security metrics and parameter dependencies for one particular distributed messaging system. The focus is on the effectiveness and correctness of security-enforcing mechanisms. The security metrics development approach that the study utilizes is risk-driven, requirement-centric, and integrated with the development of Quality-of-Service metrics. In this approach, the security requirements are expressed in terms of lower-level measurable components by applying a decomposition approach. Security metrics are then developed based on the leaf components of the decomposition. The paper also analyzes the benefits and shortcomings of the metrics development approach and introduces a trust, confidence and trustworthiness calculation model for basic measurable components of the decomposition. Keywords-security metrics; security indicators; security strength; security requirements; messaging systems
Expression and Characterization of the RKOD DNA Polymerase in Pichia pastoris

The present study assessed high-level expression of the KOD DNA polymerase in Pichia pastoris. Thermococcus kodakaraensis KOD1 is a DNA polymerase that is widely used in PCR. The DNA coding sequence of KOD was optimized based on the codon usage bias of P. pastoris and synthesized by overlapping PCR, and the nonspecific DNA-binding protein Sso7d from the crenarchaeon Sulfolobus solfataricus was fused to the C-terminus of KOD. The resulting novel gene was cloned into a pHBM905A vector and introduced into P. pastoris GS115 for secretory expression. The yield of the target protein reached approximately 250 mg/l after a 6-d induction with 1% (v/v) methanol in shake flasks. This yield is much higher than those of other DNA polymerases expressed heterologously in Escherichia coli. The recombinant enzyme was purified, and its enzymatic features were studied. Its specific activity was 19,384 U/mg. The recombinant KOD expressed in P. pastoris exhibited excellent thermostability, extension rate and fidelity. Thus, this report provides a simple, efficient and economic approach to realize the production of a high-performance thermostable DNA polymerase on a large scale. This is the first report of the expression in yeast of a DNA polymerase for use in PCR.
Entire Sound Representations Are Time-Compressed in Sensory Memory: Evidence from MMN

In order to examine the encoding of partial silence included in a sound stimulus in neural representation, time flow of the sound representations was investigated using mismatch negativity (MMN), an ERP component that reflects neural representation in auditory sensory memory. Previous work suggested that time flow of auditory stimuli is compressed in neural representations. The stimuli used were a full-stimulus of 170 ms duration, an early-gap stimulus with silence for a 20–50 ms segment (i.e., an omitted segment), and a late-gap stimulus with an omitted segment of 110–140 ms. Peak MMNm latencies from oddball sequences of these stimuli, with a 500 ms SOA, did not reflect time point of the physical gap, suggesting that temporal information can be compressed in sensory memory. However, it was not clear whether the whole stimulus duration or only the omitted segment duration is compressed. Thus, stimuli were used in which the gap was replaced by a tone segment with a 1/4 sound pressure level (filled), as well as the gap stimuli. Combinations of full-stimuli and one of four gapped or filled stimuli (i.e., early gap, late gap, early filled, and late filled) were presented in an oddball sequence (85 vs. 15%). If compression occurs only for the gap duration, MMN latency for filled stimuli should show a different pattern from those for gap stimuli. MMN latencies for the filled conditions showed the same pattern as those for the gap conditions, indicating that the whole stimulus duration rather than only gap duration is compressed in sensory memory neural representation. These results suggest that temporal aspects of silence are encoded in the same manner as physical sound.
Formative and Reflective Measurement and Validation Mismatch in Survey Research: An Archival Analysis of Information Systems Success Constructs 1985-2007

The generic IS-success constructs first identified by DeLone and McLean (1992) continue to be widely employed in research. Yet, recent work by Petter et al (2007) has cast doubt on the validity of many mainstream constructs employed in IS research over the past 3 decades; critiquing the almost universal conceptualization and validation of these constructs as reflective when in many studies the measures appear to have been implicitly operationalized as formative. Cited examples of proper specification of the Delone and McLean constructs are few, particularly in light of their extensive employment in IS research. This paper introduces a four-stage formative construct development framework: Conceive > Operationalize > Respond > Validate (CORV). Employing the CORV framework in an archival analysis of research published in top outlets 1985-2007, the paper explores the extent of possible problems with past IS research due to potential misspecification of the four application-related success dimensions: Individual-Impact, Organizational-Impact, System-Quality and Information-Quality. Results suggest major concerns where there is a mismatch of the Respond and Validate stages. A general dearth of attention to the Operationalize and Respond stages in methodological writings is also observed.
Task-Specific Modulation of Human Auditory Evoked Response in a Delayed-Match-To-Sample Task

In this study, we focus our investigation on task-specific cognitive modulation of early cortical auditory processing in human cerebral cortex. During the experiments, we acquired whole-head magnetoencephalography data while participants were performing an auditory delayed-match-to-sample (DMS) task and associated control tasks. Using a spatial filtering beamformer technique to simultaneously estimate multiple source activities inside the human brain, we observed a significant DMS-specific suppression of the auditory evoked response to the second stimulus in a sound pair, with the center of the effect being located in the vicinity of the left auditory cortex. For the right auditory cortex, a non-invariant suppression effect was observed in both DMS and control tasks. Furthermore, analysis of coherence revealed a beta band (12∼20 Hz) DMS-specific enhanced functional interaction between the sources in left auditory cortex and those in left inferior frontal gyrus, which has been shown to be involved in short-term memory processing during the delay period of DMS task. Our findings support the view that early evoked cortical responses to incoming acoustic stimuli can be modulated by task-specific cognitive functions by means of frontal–temporal functional interactions.