nils-herrmann/clean_scirepeval huggingface dataset:
Towards event source unobservability with minimum network traffic in sensor networks

Sensors deployed to monitor the surrounding environment report such information as event type, location, and time when a real event of interest is detected. An adversary may identify the real event source through eavesdropping and traffic analysis. Previous work has studied the source location privacy problem under a local adversary model. In this work, we aim to provide a stronger notion: event source unobservability, which promises that a global adversary cannot know whether a real event has ever occurred even if he is capable of collecting and analyzing all the messages in the network at all the time. Clearly, event source unobservability is a desirable and critical security property for event monitoring applications, but unfortunately it is also very difficult and expensive to achieve for resource-constrained sensor network.
 Our main idea is to introduce carefully chosen dummy traffic to hide the real event sources in combination with mechanisms to drop dummy messages to prevent explosion of network traffic. To achieve the latter, we select some sensors as proxies that proactively filter dummy messages on their way to the base station. Since the problem of optimal proxy placement is NP-hard, we employ local search heuristics. We propose two schemes (i) Proxy-based Filtering Scheme (PFS) and (ii) Tree-based Filtering Scheme (TFS) to accurately locate proxies. Simulation results show that our schemes not only quickly find nearly optimal proxy placement, but also significantly reduce message overhead and improve message delivery ratio. A prototype of our scheme was implemented for TinyOS-based Mica2 motes.
Risk assessment of supply chain finance with intuitionistic fuzzy information

Supply chain finance is a new financing model that makes the industry chain as an organic whole chain to develop financing services. Its purpose is to combine with financial institutions, companies and third-party logistics companies to achieve win-win situation. The supply chain is designed to maximize the financial value. The supply chain finance business in our country is still in its early stages. Conducting the research on risk assessment and control of the supply chain finance business has an important significance for the promotion of the development of our country supply chain finance business. The paper investigates the dynamic multiple attribute decision making problems, in which the decision information, provided by decision makers at different periods, is expressed in intuitionistic fuzzy numbers. We first develop one new aggregation operators called dynamic intuitionistic fuzzy Hamacher weighted averaging (DIFHWA) operator. Moreover, a procedure based on the DIFHWA and IFHWA operators is developed to solve the dynamic multiple attribute decision making problems where all the decision information about attribute values takes the form of intuitionistic fuzzy numbers collected at different periods. Finally, an illustrative example for risk assessment of supply chain finance is given to verify the developed approach and to demonstrate its practicality and effectiveness.
Homological Algebra and Data

These lectures are a quick primer on the basics of applied algebraic topology with emphasis on applications to data. In particular, the perspectives of (elementary) homological algebra, in the form of complexes and co/homological invariants are sketched. Beginning with simplicial and cell complexes as a means of enriching graphs to higher-order structures, we define simple algebraic topological invariants, such as Euler characteristic. By lifting from complexes of simplices to algebraic complexes of vector spaces, we pass to homology as a topological compression scheme. Iterating this process of expanding to sequences and compressing via homological algebra, we define persistent homology and related theories, ending with a simple approach to cellular sheaves and their cohomology. Throughout, an emphasis is placed on expressing homological-algebraic tools as the natural evolution of linear algebra. Category-theoretic language (though more natural and expressive) is deemphasized, for the sake of access. Along the way, sample applications of these techniques are sketched, in domains ranging from neuroscience to sensing, image analysis, robotics, and computation.
Ground coplanar waveguide to rectangular waveguide transition

The ever-increasing interest towards microwave and millimeter-wave monolithic integrated circuits has led to employing coplanar waveguide (CPW) due to its many advantages such as compact size and capability to incorporate shunt and series elements without the need to process backside and integrate via holes. On the other hand, rectangular waveguides are widely used at higher frequencies for their low loss characteristics in applications such as high Q filters, resonators and antenna feed networks. Therefore at millimeter- and submillimeter-wave applications in which active and passive components are integrated, often waveguide structures are to be combined with CPW lines. For those applications, low loss transitions are required.
[HP-FSH (Fostimon)--a matter of choice in women with low ovarian response].

Assisted reproductive technologies (ART) are one of the most dynamic fields of the medical science. ART beginning on 25th of July 1978 with the pioneering scientific work of P. Steptoe and B. Edwards. Although the ART have improved dramatically since 1978, the success of that treatment remains unsatisfactory. More than 25 years experience in IVF accrued in believe that a break-through in success rates can soon be reach. The guarantee of IVF success remains the good ovarian response, which very often is a challenge for reproductive specialist. The manuscript is a review of the literature about the most successful therapy for controlled ovarian stimulation for IVF in aged infertile women and/or low ovarian responders.



The ovarian stimulation with highly purified gonadotrophins (HP-FSH) in outlined group of patient is associated with fewer oocytes retrieval, but higher proportion of top-quality embryos compared with rFSH, an improved capacity to implant, ongoing pregnancy and live birth rate among the top-quality embryos, derived from stimulation with HP-hMG compared with top-quality embryos in the rFSH group. High purified hMG better performed than rFSH in older women and women with poor ovarian response, probably of exogenous LH activity and/or relatively higher acidic isoforms of the FSH protein (produced when lower estrogen levels are present) which may be of relevance for clinical outcome.
Exploring collaborative work among graduate students through the C5 model of collaboration: A diary study

Collaborative work among students, while an important topic of inquiry, needs further treatment as we still lack the knowledge regarding obstacles that students face, the strategies they apply, and the relations among personal and group aspects. This article presents a diary study of 54 master’s students conducting group projects across four semesters. A total of 332 diary entries were analysed using the C5 model of collaboration that incorporates elements of communication, contribution, coordination, cooperation and collaboration. Quantitative and qualitative analyses show how these elements relate to one another for students working on collaborative projects. It was found that face-to-face communication related positively with satisfaction and group dynamics, whereas online chat correlated positively with feedback and closing the gap. Managing scope was perceived to be the most common challenge. The findings suggest the varying affordances and drawbacks of different methods of communication, collaborative work styles and the strategies of group members.
The Effect of Labor ' s Emotional Intelligence on Their Job Satisfaction , Job Performance and Commitment

Emotional Intelligence is one of the most important concepts introduced to psychology and management in the last decade. The purposes of this study were to determine the effect of labor's emotional intelligence on job satisfaction, job performance and commitment in Fars Province industries in Iran. The population from which we drew our sample consisted of employees employed in Fars Province industries in Iran. A total number of three hundred and fifty participants who were selected randomly answered the questionnaires. Two hundred eighty nine questionnaires were used for analysis. The instrument to measure employees‟ emotional intelligence was the MSEIS (Schutte, Malouff, & Hall, 1998), and the Grikson (1983) was used to gather information about employees‟ job satisfaction. Also, Alen and Mayer's questionnaire (1990) was considered in order to collect employees‟ commitment data. The results showed that employees‟ emotional intelligence was positively correlated with job satisfaction. Then, there was a significant relationship between the labor's emotional intelligence and their job performance. Also, there was not any relationship between labor's emotional intelligence and their commitment. The results underline the important role of emotional intelligence and its effect on work situations.
Viewpoint-Aware Attentive Multi-view Inference for Vehicle Re-identification

Vehicle re-identification (re-ID) has the huge potential to contribute to the intelligent video surveillance. However, it suffers from challenges that different vehicle identities with a similar appearance have little inter-instance discrepancy while one vehicle usually has large intra-instance differences under viewpoint and illumination variations. Previous methods address vehicle re-ID by simply using visual features from originally captured views and usually exploit the spatial-temporal information of the vehicles to refine the results. In this paper, we propose a Viewpoint-aware Attentive Multi-view Inference (VAMI) model that only requires visual information to solve the multi-view vehicle reID problem. Given vehicle images of arbitrary viewpoints, the VAMI extracts the single-view feature for each input image and aims to transform the features into a global multiview feature representation so that pairwise distance metric learning can be better optimized in such a viewpointinvariant feature space. The VAMI adopts a viewpoint-aware attention model to select core regions at different viewpoints and implement effective multi-view feature inference by an adversarial training architecture. Extensive experiments validate the effectiveness of each proposed component and illustrate that our approach achieves consistent improvements over state-of-the-art vehicle re-ID methods on two public datasets: VeRi and VehicleID.
Poisson Cluster Process Based Analysis of HetNets With Correlated User and Base Station Locations

This paper develops a new approach to the modeling and analysis of heterogeneous cellular networks (HetNets) that accurately incorporates coupling across the locations of users and base stations, which exists due to the deployment of small cell base stations (SBSs) at the places of high user density (termed user hotspots in this paper). Modeling the locations of the geographical centers of user hotspots as a homogeneous Poisson point process (PPP), we assume that the users and SBSs are clustered around each user hotspot center independently with two different distributions. The macrocell BS locations are modeled by an independent PPP. This model is consistent with the user and SBS configurations considered by 3GPP. Using this model, we study the performance of a typical user in terms of coverage probability and throughput for two association policies: 1) Policy 1, under which a typical user is served by the open-access BS that provides maximum averaged received power and 2) Policy 2, under which the typical user is served by the small cell tier if the maximum averaged received power from the open-access SBSs is greater than a certain power threshold; and macro tier otherwise. A key intermediate step in our analysis is the derivation of distance distributions from a typical user to the open-access and closed-access interfering SBSs. Our analysis demonstrates that as the number of SBSs reusing the same resource block increases, coverage probability decreases, whereas throughput increases. Therefore, contrary to the usual assumption of orthogonal channelization, it is reasonable to assign the same resource block to multiple SBSs in a given cluster as long as the coverage probability remains acceptable. This approach to HetNet modeling and analysis significantly generalizes the state-of-the-art approaches that are based on modeling the locations of BSs and users by independent PPPs.
Social responses to Facebook status updates: The role of extraversion and social anxiety

Posting, reading, and responding to status updates is an integral part of many peoples' daily lives. However, the role of personality in predicting social responses to status updates remains largely unexplored. Based on the social enhancement and the social compensation hypothesis, we assessed the role of extraversion and social anxiety in predicting social responses to status updates. Moreover, we explored the influence of valence in this context. Capitalizing on the assets of a multimethod approach, personality was assessed with self-reports, and valence was evaluated by independent raters. Social responses to status updates were captured (1) by observing direct social feedback (i.e. likes and commenters) and (2) by informant reports on the interpersonal appraisal of participants’ status updates by their friends. In a German and a US sample, for direct social feedback neither extraversion nor social anxiety emerged as significant predictors. However, analyses of the informant reports showed that status updates of more socially anxious individuals were appreciated more by their friends. Furthermore, results pointed to the importance of valence in this context; revealing associations between valence and direct social feedback, valence and extraversion, and a moderation effect of personality on the association between valence and likes in the US sample.
Unsupervised Domain Adaptation with Similarity Learning

The objective of unsupervised domain adaptation is to leverage features from a labeled source domain and learn a classifier for an unlabeled target domain, with a similar but different data distribution. Most deep learning approaches to domain adaptation consist of two steps: (i) learn features that preserve a low risk on labeled samples (source domain) and (ii) make the features from both domains to be as indistinguishable as possible, so that a classifier trained on the source can also be applied on the target domain. In general, the classifiers in step (i) consist of fully-connected layers applied directly on the indistinguishable features learned in (ii). In this paper, we propose a different way to do the classification, using similarity learning. The proposed method learns a pairwise similarity function in which classification can be performed by computing similarity between prototype representations of each category. The domain-invariant features and the categorical prototype representations are learned jointly and in an end-to-end fashion. At inference time, images from the target domain are compared to the prototypes and the label associated with the one that best matches the image is outputed. The approach is simple, scalable and effective. We show that our model achieves state-of-the-art performance in different unsupervised domain adaptation scenarios.
Comprehensive management of an orthognathic surgery patient with aggressive central giant cell granuloma of the mandible.

This article presents a case involving a 16-year-old boy who came to the Tripler Army Medical Center Oral and Maxillofacial Surgery with a central giant cell granuloma (CGCG) on the anterior mandible. Initial management consisted of surgical curettage and intralesional injection of corticosteroids. Upon completion of steroid therapy, there was clinical and radiographic evidence of remission; however, radiographic evidence of lesion recurrence was seen at a six-month follow-up visit. The CGCG was retreated with curettage and five months of systemic injections of calcitonin, both of which failed. The lesion was most likely an aggressive form of CGCG that progressed despite conservative therapy, with destruction of hard and soft tissues, root resorption, tooth displacement, and paraesthesia in the anterior mandible. The authors present a treatment algorithm with comprehensive management involving surgical resection, reconstruction, orthodontics, and orthognathic surgery with prosthodontic considerations.
Anomaly Based Intrusion Detection Systems Using SNMP Data

This paper discusses a statistical algorithm to detect DOS attacks on computer networks. DOS attacks hamper the network by making resources unavailable to genuine users. The algorithm presented here use SNMP data in order to detect incoming flooding attack on a computer or network. The data to be monitored depends on the class of flooding attacks that is intended to be detected. In this paper we focus on detecting ICMP, UDP, TCP and IP type of flooding attacks on various network interfaces.
Establishing Moore's Law

The seemingly unshakeable accuracy of Moore's law - which states that the speed of computers; as measured by the number of transistors that can be placed on a single chip, will double every year or two - has been credited with being the engine of the electronics revolution, and is regarded as the premier example of a self-fulfilling prophecy and technological trajectory in both the academic and popular press. Although many factors have kept Moore's law as an industry benchmark, it is the entry of foreign competition that seems to have played a critical role in maintaining the pace of Moore's law in the early VLSI transition. Many different kinds of chips used many competing logic families. DRAMs and microprocessors became critical to the semiconductor industry, yet were unknown during the original formulation of Moore's law
Longitudinal study of fingerprint recognition.

Human identification by fingerprints is based on the fundamental premise that ridge patterns from distinct fingers are different (uniqueness) and a fingerprint pattern does not change over time (persistence). Although the uniqueness of fingerprints has been investigated by developing statistical models to estimate the probability of error in comparing two random samples of fingerprints, the persistence of fingerprints has remained a general belief based on only a few case studies. In this study, fingerprint match (similarity) scores are analyzed by multilevel statistical models with covariates such as time interval between two fingerprints in comparison, subject's age, and fingerprint image quality. Longitudinal fingerprint records of 15,597 subjects are sampled from an operational fingerprint database such that each individual has at least five 10-print records over a minimum time span of 5 y. In regard to the persistence of fingerprints, the longitudinal analysis on a single (right index) finger demonstrates that (i) genuine match scores tend to significantly decrease when time interval between two fingerprints in comparison increases, whereas the change in impostor match scores is negligible; and (ii) fingerprint recognition accuracy at operational settings, nevertheless, tends to be stable as the time interval increases up to 12 y, the maximum time span in the dataset. However, the uncertainty of temporal stability of fingerprint recognition accuracy becomes substantially large if either of the two fingerprints being compared is of poor quality. The conclusions drawn from 10-finger fusion analysis coincide with the conclusions from single-finger analysis.
Energy-Efficient Resource Allocation Optimization for Multimedia Heterogeneous Cloud Radio Access Networks

The heterogeneous cloud radio access network (H-CRAN) is a promising paradigm that incorporates cloud computing into heterogeneous networks (HetNets), thereby taking full advantage of cloud radio access networks (C-RANs) and HetNets. Characterizing cooperative beamforming with fronthaul capacity and queue stability constraints is critical for multimedia applications to improve the energy efficiency (EE) in H-CRANs. An energy-efficient optimization objective function with individual fronthaul capacity and intertier interference constraints is presented in this paper for queue-aware multimedia H-CRANs. To solve this nonconvex objective function, a stochastic optimization problem is reformulated by introducing the general Lyapunov optimization framework. Under the Lyapunov framework, this optimization problem is equivalent to an optimal network-wide cooperative beamformer design algorithm with instantaneous power, average power, and intertier interference constraints, which can be regarded as a weighted sum EE maximization problem and solved by a generalized weighted minimum mean-square error approach. The mathematical analysis and simulation results demonstrate that a tradeoff between EE and queuing delay can be achieved, and this tradeoff strictly depends on the fronthaul constraint.
Parent , Peer and Media Effect on the Perception of Body Image in Preadolescent Girls and Boys

The goal of this study was to begin to determine how parent, peer and media influences on the perception of body image of preadolescent girls and boys in Rize. The research was carried total 70 students of Mehmet Akif Ersoy middle school . Surveys were collected and analyzed in the statistics department using SPSS 14.0 for Windows. Results indicated peers had the largest negative influence (X 1.60/3.0) and media had the largest positive influence (X 2.12/3.0) for scale-response. Results also revealed the higher the positive influence, the higher positive self-image. As one increases positively the other will increase positively. The correlation between parents, peers, and media and body image was found to be significant, P < .000 < α = .05. A correlation coefficient of .606/1.0 revealed a moderate to strong correlation. Results also indicated the preadolescent surveyed had a lower reported body image score than the influence from parents, peers, and media together. Results should be used by health educators to develop educational classes, programs that focus on the impact from parent, peer, and media influences on the perception of body image. The present study provides more insight and new trends in data, to the understanding of the onset of body image development in preadolescent.
Minimizing the Communication Cost of Aggregation in Publish/Subscribe Systems

Modern applications for distributed publish/subscribe systems often require stream aggregation capabilities along with rich data filtering. When compared to other distributed systems, aggregation in pub/sub differentiates itself as a complex problem which involves dynamic dissemination paths that are difficult to predict and optimize for a priori, temporal fluctuations in publication rates, and the mixed presence of aggregated and non-aggregated workloads. In this paper, we propose a formalization for the problem of minimizing communication traffic in the context of aggregation in pub/sub. We present a solution to this minimization problem by using a reduction to the well-known problem of minimum vertex cover in a bipartite graph. This solution is optimal under the strong assumption of complete knowledge of future publications. We call the resulting algorithm "Aggregation Decision, Optimal with Complete Knowledge" (ADOCK). We also show that under a dynamic setting without full knowledge, ADOCK can still be applied to produce a low, yet not necessarily optimal, communication cost. We also devise a computationally cheaper dynamic approach called "Aggregation Decision with Weighted Publication" (WAD). We compare our solutions experimentally using two real datasets and explore the trade-offs with respect to communication and computation costs.