Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceived risk agents in e-commerce environments

During the past three decades, the growth of e-commerce has presented marketers with many new arenas for research and application. Certainly e-commerce has become a significant portion of the world economy and in particular the consumer sector. As previous literature has consistently considered perceived risk as a major factor consumer purchase decisions, this research identifies several major components of consumer perceived risk (PR) and their normative implications in the e-commerce environment. Moreover, normative suggestions concerning those factors are provided.
The Visible Man : 3 D Interactive Musculoskeletal Anatomic Atlas of the Lower Extremity 1

To build a personal computer based interactive musculoskeletal anatomic atlas using Visible Man data set, we developed a semi-automated segmentation program by using an intelligent scissors approach and a shape-based interpolation. Manual contour extractions at 3mm slice intervals and shape-based interpolations of inter-slices of the musculoskeletal structures of the lower extremity were carried out. For interactive and realistic 3D display, we have developed an efficient binary volume rendering method that introduces the concepts of shear-warp factorization and applies a newly developed normal calculation technique. We have segmented and labeled all the muscles, tendons and bones of the lower extremity. Our volume based 3D interactive atlas supports various interactions including rotation, removal, highlighting with artificial colors, arbitrary cutting operation, transparent view, and descriptive knowledge representation. In addition, browsing through the 2D images of transverse, coronal, and sagittal views with labeling and segmentation information is possible.
Energy-aware supply and body biasing voltage scheduling algorithm

A technique of energy minimization by combining dynamic voltage scheduling (DVS) and adaptive body biasing voltage (ABB) method for distributed real-time system at system design level is proposed. Based on a simplified energy optimizing model, the proposed approach named LEVVS (low energy supply voltage and body biasing voltage scheduling algorithm) explores the minimizing of energy consumption by finding optimal trade-off between dynamic and static energy. Experiments show that, using the LEVVS approach, more than 53% average energy reduction can be obtained than by employing the DVS method alone. Furthermore the effects of switch capacitance and global slack on the energy saving efficiency of LEVVS are investigated.
Analysis and Design of Stacked-FET Millimeter-Wave Power Amplifiers

Stacked field-effect transistor (FET) CMOS millimeter-wave power amplfiers (PAs) are studied with a focus on design of appropriate complex impedances between the transistors. The stacking of multiple FETs allows increasing the supply voltage, which, in turn, allows higher output power and a broader bandwidth output matching network. Different matching techniques for the intermediate nodes are analyzed and used in two-, three-, and four-stack single-stage <formula formulatype="inline"><tex Notation="TeX">$Q$</tex></formula>-band CMOS PAs. A four-stack amplifier design achieves a saturated output power greater than 21 dBm while achieving a maximum power-added efficiency (PAE) greater than 20% from 38 to 47 GHz. The effectiveness of an inductive tuning technique is demonstrated in measurement, improving the PAE from 26% to 32% in a two-stack PA design. The input and output matching networks are designed using on-chip shielded coplanar waveguide transmission lines, as well as metal finger capacitors. The amplifiers were implemented in a 45-nm CMOS silicon-on-insulator process. Each of the amplifiers occupies an area of 600 <formula formulatype="inline"> <tex Notation="TeX">$\mu$</tex></formula>m<formula formulatype="inline"><tex Notation="TeX">$\,\times\,$</tex></formula>500 <formula formulatype="inline"> <tex Notation="TeX">$\mu$</tex></formula>m including pads.
Nurse-led implementation of the single assessment process in primary care: a descriptive feasibility study.

OBJECTIVE to determine the resources required to carry out the single assessment process in primary care. DESIGN prospective descriptive study. SETTING one urban primary care practice, Southampton. PARTICIPANTS nine hundred and forty-four people aged 70+ years, registered with the practice, not living in a residential/nursing home, or terminally ill. INTERVENTION participants were sent the six-item Sherbrooke questionnaire (case-finding tool). Non-responders were re-mailed after 4 weeks. All those scoring 4, 5 or 6 and a randomly selected half of those scoring 2 or 3 were offered overview assessment and comprehensive assessment as indicated by the Minimum Data Set for Home Care protocol. The nurse assessor identified unmet needs and agreed an action plan with participants. Another researcher conducted semi-structured interviews with a purposive sample of 26 participants to elicit their views of the process. MAIN OUTCOME MEASURES response rates/scores of Sherbrooke questionnaire; numbers/characteristics of people requiring overview and comprehensive assessments; nature of resulting recommendations/referrals and impact on other agencies; resources required; views of service users. RESULTS eight hundred and sixty-three (91%) participants replied. Five hundred and seven (54%) scored 2+, triggering an overview assessment, which was offered to 307. One hundred and twenty-four participants (40%) accepted; 64 (52%) had unmet needs (median 8 each, range 2-18), resulting in 34 referrals within the practice including four case conferences, and 21 to community/secondary health services. Few participants with a Sherbrooke score of 2 required comprehensive assessment. Users perceived the process as acceptable and useful, but not always relevant to their current needs. CONCLUSION targeting those scoring 3+ on the Sherbrooke questionnaire (28% of sample) may improve the identification of patients who would benefit from further assessment. A contact approach rather than a case-finding one may improve the relevance of this process to older people.
An Empirical Study of Hear-Through Augmented Reality: Using Bone Conduction to Deliver Spatialized Audio

Augmented reality (AR) is the mixing of computer-generated stimuli with real-world stimuli. In this paper, we present results from a controlled, empirical study comparing three ways of delivering spatialized audio for AR applications: a speaker array, headphones, and a bone-conduction headset. Analogous to optical-see-through AR in the visual domain, hear-through AR allows users to receive computer-generated audio using the bone-conduction headset, and real-world audio using their unoccluded ears. Our results show that subjects achieved the best accuracy using a speaker array physically located around the listener when stationary sounds were played, but that there was no difference in accuracy between the speaker array and the bone-conduction device for sounds that were moving, and that both devices outperformed standard headphones for moving sounds. Subjective comments by subjects following the experiment support this performance data.
An Empirical Analysis of Valuation Algorithms for Pricing Callable Snowball Floaters

In this paper we value a callable snowball floater, a complex interest rate instrument with variable coupon payments, which depend on the prevailing interest rates in arrears and recursively on previous coupon payments. The embedded option requires solving an optimal stopping problem using the dynamic programming principle. A well-known and widely used algorithm to estimate conditional expectations is a specific form of least squares Monte Carlo simulation introduced by Longstaff and Schwartz (2001), which we refer to as the LSM approach. Contrary to the standard approach, where discounted option values of the subsequent period are regressed on the current state variables, Longstaff and Schwartz (2001) use the ex post realized payoffs of in-the-money option scenarios from continuation instead. They argue that, in doing so, they get values less than or equal to the value implied by the optimal stopping rule, which provides an objective convergence criterion. We compare the LSM approach with the standard approach and use the price estimate from an elaborate nested Monte Carlo simulation as a benchmark. We empirically find that the LSM estimate of the embedded option might be considerably downward biased, whereas the standard estimate is much closer to the benchmark price. Moreover, we find that there is no optimal type of basis function that can generally be recommended for pricing interest rate instruments. Instead, we suggest using the LSM approach to determine the optimal type of basis function that results in the largest option value and rely on the standard approach to price the instrument. These are important issues to consider when pricing complex interest rate instruments, in general, and callable snowball floaters, in particular.
Poverty, Social Exclusion and Microfinance in Britain

In recent years, some of the most innovative economic responses to poverty have been developed in countries in the South, such as Bangladesh and Mexico. In favourable circumstances, 'microfinance initiatives' can enable poor people to build up security through savings and insurance, and obtain credit to cover times of hardship or to establish small businesses. What can be learned from these international experiences in Britain, where increasing numbers of poor people are being excluded from commercially provided financial services?
Lognormal and Gamma Mixed Negative Binomial Regression : Supplementary Material

Proof: The first and second derivatives of f(z) = − ln(1 − pz) are f ′(z) = p 1−pz and f ′′(z) = p (1−pz)2 = (f ′(z))2, respectively, and f j(0) = 0 for j ≥ 1, therefore d dzm f j(z) ∣∣ z=0 = 0 for j > m and thus (2) is true for j > m. When n = j = 1, we have d n dzn f j(z) ∣∣ z=0 = f ′ k(0) = p, ∑n j′=1 F (n, j ′) j! (j−j′! ) [f ′(z)]nf j−j ′ (z) ∣∣∣ z=0 = p, and F (n, j)j! pn = p, therefore, (2) is true for n = m = 1. Assume (2) is true for n = m− 1, then
Monte Carlo Evaluation of Implied Orders As a Basis for Tailored Testing

TAILOR, the computer program which imple ments an approach to tailored testing outlined by Cliff (1975), was examined with errorless data by monte carlo methods. Three replications of each cell of a 3 × 3 table with 10, 20, and 40 items and persons were analyzed. Mean rank correlation coef ficients between the true person and item order, specified by preassigned random numbers, and the computed order produced by the program averaged . 96. The average proportion of items used was . 48. A marked tendency was observed for the program to produce better results as the number of persons and items increased.
Rule Extraction using Artificial Neural Networks

Abstract Artificial neural networks have been successfully applied to a variety of business application problems involving classification and regression. Although backpropagation neural networks generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions are not as interpretable as those of decision trees. In many applications, it is desirable to extract knowledge from trained neural networks so that the users can gain a better understanding of the solution. This paper presents an efficient algorithm to extract rules from artificial neural networks. We use two-phase training algorithm for backpropagation learning. In the first phase, the number of hidden nodes of the network is determined automatically in a constructive fashion by adding nodes one after another based on the performance of the network on training data. In the second phase, the number of relevant input units of the network is determined using pruning algorithm. The pruning process attempts to eliminate as many connections as possible from the network. Relevant and irrelevant attributes of the data are distinguished during the training process. Those that are relevant will be kept and others will be automatically discarded. From the simplified networks having small number of connections and nodes we may easily able to extract symbolic rules using the proposed algorithm. Extensive experimental results on several benchmarks problems in neural networks demonstrate the effectiveness of the proposed approach with good generalization ability.
Integration of RFID into Wireless Sensor Networks: Architectures, Opportunities and Challenging Problems

The vision of pervasive computing is based on the idea that computers merge with their environment. Radio frequency identification (RFID) and wireless sensor network (WSN) are two important components of pervasive computing, since both technologies can be used for coupling the physical and the virtual world. However, RFID and WSN almost are under development in parallel method, few integration schemes and related opportunities are investigated in detail. Through deep analysis of RFID and WSN, three forms of new system architecture that combines the two technologies are proposed and its feasibility, technical challenges are discussed thoroughly
Toward understanding the cliques of opinion spammers with social network analysis

Consumer generated product reviews are considered as more persuasive than commercial advertising, and are now an important message source to make purchase decision. Nevertheless, firms may purposely hire spammers to create fake reviews to promote their products and to demote products of their competitors. To create the opinion majority, firms may hire a group of spammers rather than just one or few individual spammers to write fake reviewers. These spammers may act as a group to support other spammers to create a social consensus or majority of opinions. In the study, we attempt to adopt a real case to analyze the social network of spammers by K-core and Clique analysis. Our research results show that the social connection among spammers is stronger than that among non-spammers. Moreover, K-cores and cliques can be used as cues to identify spammers.
A Literacy of Armed Love: Confrontation and Desire in Aesthetic and Critical Projects

The article argues that creative confrontations with damaging discourses as part of a critical literacy curriculum can be viewed as acts of love, for self and community. Using data from a multi-sited critical ethnography, the study considers the literacy productions of two focal students in diverse schools, a charter middle school and a large urban high school. Mediated discourse analysis of their work explores their aesthetic and critical literacy productions as refusals of oppressive discourses pressing against marginalized identities, and as expressions of desire for imagined, better realities. This research views such performances of multimodal creative resistance as an audacious literacy of desire, valuable as standards-meeting persuasive compositions, but also immeasurably valuable because of the emotional experience of the student producers, who were powerfully affected through the twin pleasures of resisting and imagining. This study illustrates how literacy projects might both inhabit and move forward Freire’s concept of armed love.
A PLANETARY SYSTEM AROUND THE NEARBY M DWARF GJ 667C WITH AT LEAST ONE SUPER-EARTH IN ITS HABITABLE ZONE

We re-analyze 4 years of HARPS spectra of the nearby M1.5 dwarf GJ 667C available through the European Southern Observatory public archive. The new radial velocity (RV) measurements were obtained using a new data analysis technique that derives the Doppler measurement and other instrumental effects using a least-squares approach. Combining these new 143 measurements with 41 additional RVs from the Magellan/Planet Finder Spectrograph and Keck/High Resolution Echelle Spectrometer spectrometers reveals three additional signals beyond the previously reported 7.2 day candidate, with periods of 28 days, 75 days, and a secular trend consistent with the presence of a gas giant (period ∼10 years). The 28 day signal implies a planet candidate with a minimum mass of 4.5 M⊕ orbiting well within the canonical definition of the star's liquid water habitable zone (HZ), that is, the region around the star at which an Earth-like planet could sustain liquid water on its surface. Still, the ultimate water supporting capability of this candidate depends on properties that are unknown such as its albedo, atmospheric composition, and interior dynamics. The 75 day signal is less certain, being significantly affected by aliasing interactions among a potential 91 day signal, and the likely rotation period of the star at 105 days detected in two activity indices. GJ 667C is the common proper motion companion to the GJ 667AB binary, which is metal-poor compared to the Sun. The presence of a super-Earth in the HZ of a metal-poor M dwarf in a triple star system supports the evidence that such worlds should be ubiquitous in the Galaxy.
Analysis of the A-U Rich Hairpin from the Intergenic Region of Tospovirus S RNA as Target and Inducer of RNA Silencing

Earlier work indicated that Tomato spotted wilt virus (TSWV) messenger transcripts, and not the (anti)genomic RNAs, are targeted by the RNA silencing machinery. Here, the predicted AU-rich hairpin (HP) structure encoded by the intergenic region (IGR) of the TSWV S RNA, and present at the 3′ end of viral mRNAs, was analyzed as a target and inducer for RNA silencing. Virus-derived siRNAs (vsiRNAs) purified from virus infected plants were found to derive from all three genomic RNA segments but predominantly the ambisense M and S RNAs. Further profiling on the S RNA sequence revealed that vsiRNAs were found from almost the entire S RNA sequence, except the IGR from where hardly any vsiRNAs were found. Similar profiles were observed with the distantly related Tomato yellow ring tospovirus (TYRV). Dicer cleavage assays using Drosophila melanogaster (Dm) embryo extracts showed that synthetic transcripts of the IGR-HP region were recognized as substrate for Dicer. Transient agroinfiltration assays of a GFP-sensor construct containing the IGR-HP sequence at its 3′ UTR (GFP-HP) did not show more rapid/strong silencing and profiling of the corresponding siRNAs, generated outside the context of a viral infection, still revealed relatively low levels of IGR-HP-derived siRNAs. These data support the idea that the IGR-HP is a weak inducer of RNA silencing and only plays a minor role in the amplification of a strong antiviral RNAi response.
Selecting the Universal Features for Breast Lumps with Different Acquisition Parameter Settings

Ultrasound imaging has been widely used to visualize internal body structure. In clinical practices, physicians employ ultrasound imaging to assist the diagnosis of various diseases. However, different parameter settings in sonography system may cause discrepancies in the ultrasound images. Hence, this paper proposed a GA-based method for selecting universal features under different parameter settings in sonography system. The F-score, T-test, and Correlation coefficient are used to evaluate the discriminant capability of features. Experimental results showed that the selected universal feature sets achieve high classification accuracy in various parameter settings.
A Network Genetic Algorithm for Concept Learning

This paper presents a highly parallel genetic algorithm, designed for concept induction in propositional and first order logics. The system exploits niches and species for learning multimodal concepts; it deeply differs from other systems because of the distributed architecture, which totally eliminates the concept of common memory. A first implementation of the system, designed for checking the possibility of exploiting parallel processing in network computer, is evaluated on standard benchmarks. The experimental results show that the system reaches good performances both with respect to the quality of the learned knowledge and with respect to the speed up on a workstation cluster.
HI-risk: A method to analyse health information risk intelligence

Information security threat intelligence is a prevalent topic amongst researchers, long-established IT-vendors and start-ups. The possibilities of Big Data analytics to security threat and vulnerability scanning offer a significant development in the protection of infrastructures. At the same time, industry research reports continue to state that the main contributing factor in the events leading to a data breach is human error. The common response of information security professionals is to resort to technological solutions to prevent these human errors. However, some very important information security intelligence is not hidden within the network traffic: it's available from the people that work with sensitive information. This article describes the Health Information risk (HI-risk) method to identify non-technical information security risks in healthcare. The method includes risks related to skills, behaviour, processes, organisational culture, physical security, and external influences. HI-risk offers a solution to collect intelligence about nontechnical information security incidents from across the healthcare sector to demonstrate past trends and to be ahead of future incidents. A test of a HI-risk forecast proved the feasibility of this approach in healthcare and beyond. It is suggested that HI-risk could become a valuable addition to existing technical threat and vulnerability monitoring tools.
Does Dual Sourcing Lower Procurement Costs?

U.S. defense policy encourages the use of dual sourcing to reduce government procurement costs, but recent theoretical work raises doubts about the benefits of this policy. I study the determinants of dual sourcing and its effects on government procurement costs using a panel dataset of tactical missiles. I find dual sourcing is not driven by failures to reduce costs; instead, it is used more often after incumbent suppliers demonstrate quality control problems, and in settings where tacit collusion is likely to be difficult. After correcting for selection bias, dual sourcing is found to reduce government procurement costs significantly.
The new signature generation method based on an unpacking algorithm and procedure for a packer detection

Recently, a malware is growing rapidly and the number of malware applies various techniques to protect itself from the anti-virus solution detection. The reason of this phenomenon is that a longer resident on an infected host guarantees the more profit. As a result, these many protection techniques are applied to a malware, a representative of those is a Packing. It is not an exaggeration that most of the malware currently is distributed. In other words, a packer is widely used for a malware protection. Therefore analysts must determine whether the malware was packed or not and if the malware is packed, what packer is used, before an analysis of the malware. For these procedures, some packer detection tools were released and used. But, the detection performance is not good and there is some false positive and false negative. Therefore we propose a signature generation method that is based on an unpacking process and algorithm in this paper. And we offer the packer detection experiment result using the proposed packer detection signature generation method.