nils-herrmann/clean_scirepeval huggingface dataset:
Molecular mechanisms responsible for the atheroprotective effects of laminar shear stress.

The endothelium lining the inner surface of blood vessels of the cardiovascular system is constantly exposed to hemodynamic shear stress. The interaction between endothelial cells and hemodynamic shear stress has critical implications for atherosclerosis. Regions of arterial narrowing, curvatures, and bifurcations are especially susceptible to atherosclerotic lesion formation. In such areas, endothelial cells experience low, or oscillatory, shear stress. Corresponding changes in endothelial cell structure and function make them susceptible to the initiation and development of atherosclerosis. In contrast, blood flow with high laminar shear stress activates signal transductions as well as gene and protein expressions that play important roles in vascular homeostasis. In response to laminar shear stress, the release of vasoactive substances such as nitric oxide and prostacyclin decreases permeability to plasma lipoproteins as well as the adhesion of leukocytes, and inhibits smooth muscle cell proliferation and migration. In summary, different flow patterns directly determine endothelial cell morphology, metabolism, and inflammatory phenotype through signal transduction and gene and protein expression. Thus, high laminar shear stress plays a key role in the prevention of atherosclerosis through its regulation of vascular tone and long-term maintenance of the integrity and function of endothelial cells.
Document clustering using nonnegative matrix factorization

A methodology for automatically identifying and clustering semantic features or topics in a heterogeneous text collection is presented. Textual data is encoded using a low rank nonnegative matrix factorization algorithm to retain natural data nonnegativity, thereby eliminating the need to use subtractive basis vector and encoding calculations present in other techniques such as principal component analysis for semantic feature abstraction. Existing techniques for nonnegative matrix factorization are reviewed and a new hybrid technique for nonnegative matrix factorization is proposed. Performance evaluations of the proposed method are conducted on a few benchmark text collections used in standard topic detection studies.
Wireless Recording in the Peripheral Nervous System with Ultrasonic Neural Dust

The emerging field of bioelectronic medicine seeks methods for deciphering and modulating electrophysiological activity in the body to attain therapeutic effects at target organs. Current approaches to interfacing with peripheral nerves and muscles rely heavily on wires, creating problems for chronic use, while emerging wireless approaches lack the size scalability necessary to interrogate small-diameter nerves. Furthermore, conventional electrode-based technologies lack the capability to record from nerves with high spatial resolution or to record independently from many discrete sites within a nerve bundle. Here, we demonstrate neural dust, a wireless and scalable ultrasonic backscatter system for powering and communicating with implanted bioelectronics. We show that ultrasound is effective at delivering power to mm-scale devices in tissue; likewise, passive, battery-less communication using backscatter enables high-fidelity transmission of electromyogram (EMG) and electroneurogram (ENG) signals from anesthetized rats. These results highlight the potential for an ultrasound-based neural interface system for advancing future bioelectronics-based therapies.
Optimal and Low-Complexity Algorithms for Dynamic Spectrum Access in Centralized Cognitive Radio Networks with Fading Channels

In this paper, we develop a centralized spectrum sensing and Dynamic Spectrum Access (DSA) scheme for secondary users (SUs) in a Cognitive Radio (CR) network. Assuming that the primary channel occupancy follows a Markovian evolution, the channel sensing problem is modeled as a Partially Observable Markov Decision Process (POMDP). We assume that each SU can sense only one channel at a time by using energy detection, and the sensing outcomes are then reported to a central unit, called the secondary system decision center (SSDC), that determines the channel sensing/accessing policies. We derive both the optimal channel assignment policy for secondary users to sense the primary channels, and the optimal channel access rule. Our proposed optimal sensing and accessing policies alleviate many shortcomings and limitations of existing proposals: (a) ours allows fully utilizing all available primary spectrum white spaces, (b) our model, and thus the proposed solution, exploits the temporal and spatial diversity across different primary channels, and (c) is based on realistic local sensing decisions rather than complete knowledge of primary signalling structure. As an alternative to the high complexity of the optimal channel sensing policy, a suboptimal sensing policy is obtained by using the Hungarian algorithm iteratively, which reduces the complexity of the channel assignment from an exponential to a polynomial order. We also propose a heuristic algorithm that reduces the complexity of the sensing policy further to a linear order. The simulation results show that the proposed algorithms achieve a near-optimal performance with a significant reduction in computational time.
Advertising and Consumers' Communications

U recently, brand identities were built by firms via brand image advertising. However, the flourishing consumer communication weakened the firms’ grip on their brands. The interaction between advertising and consumer communications and their joint impact on brand identity is the focal point of this paper. We present a model in which consumer preference for functional attributes may correlate with the identity they desire to project of themselves. This correlation is known to the firm but not to the consumers. Both the firm and the consumers can communicate their desired brand identity, although the actual brand identity is determined endogeneously by the composition of consumers who purchase it (i.e., what types of people consume the brand). We find that sometimes the firm can strengthen the identity of its brand by refraining from advertising. This result is based on the following intermediate finding: advertising can diminish the endogeneous informativeness of consumer communications by making it one-sided. Furthermore, it turns out that refraining from brand image advertising may be optimal for the firm when the product is especially well positioned to create a strong identity—i.e., when consumer preferences for functional and self-expressive attributes are highly correlated.
A Generalized Coupling Matrix Extraction Technique for Bandpass Filters With Uneven-Qs

In this paper, a vector fitting (VF) based analytical extraction method, which is capable of accurately extracting the coupling matrix and the uneven unloaded Qs of each electric resonators of a filter, is presented. Having had the complex poles and residues determined using VF, the coupling matrix can be obtained by a sequence of complex orthogonal transformations. As a side product, the unloaded Qs for each resonator will be directly obtained from the complex diagonal elements of the coupling matrix. To demonstrate the effectiveness of the proposed method, an ideal demonstrative example along with two practical challenging filter tuning examples, namely, an eight-pole dual-mode dielectric filter and an eight-pole dual-mode predistortion filter, are demonstrated. An excellent match between the responses of the measured data and those from the extracted coupling matrix with actual unloaded Q factors can be seen.
Conversion of complex contour line definitions into polygonal element mosaics

A simple algorithm is presented for processing complex contour arrangements to produce polygonal element mosaics which are suitable for line drawing and continuous tone display. The program proceeds by mapping adjacent contours onto the same unit square and, subject to ordering limitations, connecting nodes of one contour to their nearest neighbors in the other contour. While the mapping procedure provides a basis for branching decisions, highly ambiguous situations are resolved by user interaction. The program was designed to interface a contour definition of the components of a human brain. These brain data are a most complex definition and, as such, serve to illustrate both the capabilities and limitations of the procedures.
Customer lifetime network value: customer valuation in the context of network effects

Nowadays customers are increasingly connected and extensively interact with each other using technologyenabled media like online social networks. Hence, customers are frequently exposed to social influence when making purchase decisions. However, established approaches for customer valuation mostly neglect network effects based on social influence. This leads to a misallocation of resources. Following a design-oriented approach, this paper develops a model for customer valuation referred to as the customer lifetime network value (CLNV) incorporating an integrated network perspective. By considering the customers’ net contribution to the network, the CLNV reallocates values between customers based on social influence. Inspired by common prestigeand eigenvector-related centrality measures it incorporates social influence among all degrees of separation acknowledging its viral spread. Using a real-world dataset, we demonstrate the practicable applicability of the CLNV to determine individual customers’ value.
Efficiency of Fuzzy C Means algorithm for Brain Tumor segmentation in MR Brain Images

Image processing is a technique or set of operations to get meaningful information from an image for the usefulness and effectiveness of images. Image segmentation is an efficient technique in extracting and separating some of the features in the images.  The main objective of this research work is to find the best fit of FCM algorithm over finding the axial and coronal plane of MRI brain imagesvia its accuracy and computational time.In the preprocessing, brain images of MRI have been converted from the DICOM format into standard image. Preprocessing is carried out by Gaussian filter technique to remove the noises in the images. The Fuzzy C Means (FCM) algorithm is implemented to segment the tumor affected region in the MR images.  By comparing the histogram values of the images (before and after segmentation) with the cluster center values by the FCM algorithm, the efficiency and accuracy of the algorithm is evaluated.  The best fit of FCM algorithm into the axial and coronal plane is identified based on the computational time in this work.
A peer-to-peer anonymizing network layer

Existing Internet systems implement anonymity at the application layer or through centralized components. A robust, decentralized infrastructure that anonymizes any Internet traffic could benefit a wide array of existing protocols and systems. This anonymous network layer could seamlessly replace the current communications channel, and it could continue to offer anonymity and availability even while components fail maliciously. This thesis proposes Tarzan, a peer-to-peer anonymous IP network overlay. Because it provides IP service, Tarzan is general-purpose and transparent to applications. Organized as a decentralized peer-to-peer overlay, Tarzan is fault-tolerant, highly scalable, and easy to manage. Tarzan achieves its anonymity with layered encryption and multi-hop routing, much like a Chaumian mix. A message initiator chooses a path of peers pseudo-randomly in a way that adversaries cannot easily influence. Cover traffic prevents a global observer from drawing conclusions based on traffic analysis as to an initiator’s identity. Tarzan provides anonymity to either clients or servers, without requiring that both participate, presenting the abstraction of a one-way anonymous tunnel. In both cases, Tarzan uses a network address translator (NAT) to bridge between Tarzan hosts and oblivious Internet hosts. We quantify Tarzan’s anonymity properties and show that Tarzan imposes minimal performance overhead over a corresponding non-anonymous overlay route. Thesis Supervisor: Robert T. Morris Title: Assistant Professor of Computer Science and Engineering
The Thing and I: Understanding the Relationship Between User and Product

We currently witness a growing interest of the Human-Computer Interaction (HCI) community in user experience. It has become a catchphrase, calling for a holistic perspective and an enrichment of traditional quality models with non-utilitarian concepts, such as fun (Monk & Frohlich, 1999;  1999), joy (Glass, 1997), pleasure (Jordan, 2000), hedonic value (Hassenzahl, 2002a) or ludic value (Gaver & Martin, 2000). In the same vein, literature on experiential marketing stresses that a product should not longer be seen as simply delivering a bundle of functional features and benefits it provides experiences. Customers want products "that dazzle their senses, touch their hearts and stimulate their minds" (Schmitt, 1999, p. 22). Experiential marketing assumes that customers take functional features, benefits, and product quality as a given. Even though the HCI community seems to embrace the notion that functionality and usability is just not enough, we are far from having a coherent understanding of what user experience actually is. The few existing models (e.g., Logan, 1984; Jordan, 2000) of user experience in HCI that incorporate aspects such as pleasure are rare and often overly simplistic. In the present chapter, I will propose a more complex model that defines key elements of user experience and their functional relations. Specifically, it aims at addressing aspects, such as (a) the subjective nature of experience per se; (b) perception of a product; (c) emotional responses to products in (d) varying situations. It is a more detailed and further developed version of a research model, I previously presented in Hassenzahl (2002a). I view it as a first step towards a better understanding of how people experience products and a valuable starting point for further in-depth theoretical discussions.
Effective Defense Schemes for Phishing Attacks on Mobile Computing Platforms

Recent years have witnessed the increasing threat of phishing attacks on mobile computing platforms. In fact, mobile phishing is particularly dangerous due to the hardware limitations of mobile devices and the habits of mobile users. In this paper, we did a comprehensive study on the security vulnerabilities caused by mobile phishing attacks, including web page phishing attacks, application phishing attacks, and account registry phishing attacks. Existing schemes designed for web phishing attacks on personal computers (PCs) cannot effectively address the various phishing attacks on mobile devices. Hence, we propose MobiFish, which is a novel automated lightweight antiphishing scheme for mobile platforms. MobiFish verifies the validity of web pages, applications, and persistent accounts by comparing the actual identity to the claimed identity. MobiFish has been implemented on a Nexus 4 smartphone running the Android 4.2 operating system. We experimentally evaluate the performance of MobiFish with 100 phishing URLs and corresponding legitimate URLs, as well as phishing apps. The results show that MobiFish is very effective in detecting phishing attacks on mobile phones.
Menstrual hygiene management and school absenteeism among female adolescent students in Northeast Ethiopia

Adolescence in girls has been recognized as a special period marked with the onset of menarche. Even though menstruation is a natural process, it is associated with misconceptions, malpractices and challenges among girls in developing countries. However, much is not documented; school-absenteeism and dropout are a common problem among girls in rural Ethiopia. Focusing among school girls, this study has examined knowledge about menstruation, determinants of menstrual management and its influence on school-attendance in Northeast Ethiopia.



We conducted a mixed-method research combining quantitative and qualitative methods in Northeast Ethiopia. The quantitative study was conducted among 595 randomly selected adolescent school girls. Nine in-depth interviews; five school-dropout girls and four female teachers, and four focus group discussions among school girls were conducted in 2013.



The mean age at menarche was 13.98 (±1.17) years. About 51% of girls had knowledge about menstruation and its management. Only a third of the girls used sanitary napkins as menstrual absorbent during their last menstruation. Girls from urban areas, had mothers of secondary and above education and, families of higher monthly expenditure had more chance of using sanitary napkins than their counterparts. More than half of the girls reported to have been absent from school during their menstruation period. Those who did not use sanitary napkins were more likely to be absent from school [AOR-95% C.I: 5.37 (3.02 - 9.55)]. Fifty eight percent of girls reported that their school-performance had declined after they had menarche. In addition, the qualitative study indicated that school-dropout was common among girls who experienced teasing and humiliation by classmates when their clothes were stained with blood as they do not use sanitary napkins.



Though there is an effort to increase girls' school enrollment, lack of basic needs, like sanitary napkins that facilitate routine activates of girls at early adolescence are observed to deter girls' school-attendance in rural Ethiopia. Special support for girl students, especially when they have their first menstruation and separate functioning sanitary facilities are necessities that should be in school at all times if gender equality and girls empowerment is to be achieved.
Nobel Lecture: The Economic Way of Looking at Behavior

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Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results

Yuan Yuan∗, Yew-Soon Ong∗, Liang Feng†, A.K. Qin‡, Abhishek Gupta∗, Bingshui Da∗, Qingfu Zhang§, Kay Chen Tan¶, Yaochu Jin‖, and Hisao Ishibuchi∗∗ ∗ School of Computer Science and Engineering, Nanyang Technological University, Singapore † College of Computer Science, Chongqing University, China ‡ School of Science, RMIT University, Australia § Department of Computer Science, City University of Hong Kong, Hong Kong ¶ Department of Electrical and Computer Engineering, National University of Singapore, Singapore ‖ Department of Computer Science, University of Surrey, United Kingdom ∗∗ Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Japan Technical Report
Computer crime and abuse: A survey of public attitudes and awareness

The survey results show that individual awareness of computer crime and abuse is high and that the majority of respondents perceive it to be a problem. However, the views expressed regarding the seriousness of the different types of abuse (and the potential motivations for them) were more variable. In addition, awareness of abuse is more widespread than knowledge of the associated legislation that may be used to prevent and punish it. The results also revealed the significant potential for media reports to influence opinions in this area, highlighting the importance of a responsible attitude in order to foster the information society
Exploiting latent semantic information in statistical language modeling

Statistical language models used in large-vocabulary speech recognition must properly encapsulate the various constraints, both local and global, present in the language. While local constraints are readily captured through n-gram modeling, global constraints, such as long-term semantic dependencies, have been more difficult to handle within a data-driven formalism. This paper focuses on the use of latent semantic analysis, a paradigm that automatically uncovers the salient semantic relationships between words and documents in a given corpus. In this approach, (discrete) words and documents are mapped onto a (continuous) semantic vector space, in which familiar clustering techniques can be applied. This leads to the specification of a powerful framework for automatic semantic classification, as well as the derivation of several language model families with various smoothing properties. Because of their large-span nature, these language models are well suited to complement conventional n-grams. An integrative formulation is proposed for harnessing this synergy, in which the latent semantic information is used to adjust the standard n-gram probability. Such hybrid language modeling compares favorably with the corresponding n-gram baseline: experiments conducted on the Wall Street Journal domain show a reduction in average word error rate of over 20%. This paper concludes with a discussion of intrinsic tradeoffs, such as the influence of training data selection on the resulting performance.
Epigenetic Risk Factors in PTSD and Depression

Epidemiological and clinical studies have shown that children exposed to adverse experiences are at increased risk for the development of depression, anxiety disorders, and posttraumatic stress disorder (PTSD). A history of child abuse and maltreatment increases the likelihood of being subsequently exposed to traumatic events or of developing PTSD as an adult. The brain is highly plastic during early life and encodes acquired information into lasting memories that normally subserve adaptation. Translational studies in rodents showed that enduring sensitization of neuronal and neuroendocrine circuits in response to early life adversity are likely risk factors of life time vulnerability to stress. Hereby, the hypothalamic-pituitary-adrenal (HPA) axis integrates cognitive, behavioral, and emotional responses to early-life stress and can be epigenetically programed during sensitive windows of development. Epigenetic mechanisms, comprising reciprocal regulation of chromatin structure and DNA methylation, are important to establish and maintain sustained, yet potentially reversible, changes in gene transcription. The relevance of these findings for the development of PTSD requires further studies in humans where experience-dependent epigenetic programing can additionally depend on genetic variation in the underlying substrates which may protect from or advance disease development. Overall, identification of early-life stress-associated epigenetic risk markers informing on previous stress history can help to advance early diagnosis, personalized prevention, and timely therapeutic interventions, thus reducing long-term social and health costs.