nils-herrmann/clean_scirepeval huggingface dataset:
User guided entity similarity search using meta-path selection in heterogeneous information networks

With the emergence of web-based social and information applications, entity similarity search in information networks, aiming to find entities with high similarity to a given query entity, has gained wide attention. However, due to the diverse semantic meanings in heterogeneous information networks, which contain multi-typed entities and relationships, similarity measurement can be ambiguous without context. In this paper, we investigate entity similarity search and the resulting ambiguity problems in heterogeneous information networks. We propose to use a meta-path-based ranking model ensemble to represent semantic meanings for similarity queries, exploit the possibility of using using user-guidance to understand users query. Experiments on real-world datasets show that our framework significantly outperforms competitor methods.
Mobile Clinical Decision Support Systems and Applications: A Literature and Commercial Review

The latest advances in eHealth and mHealth have propitiated the rapidly creation and expansion of mobile applications for health care. One of these types of applications are the clinical decision support systems, which nowadays are being implemented in mobile apps to facilitate the access to health care professionals in their daily clinical decisions. The aim of this paper is twofold. Firstly, to make a review of the current systems available in the literature and in commercial stores. Secondly, to analyze a sample of applications in order to obtain some conclusions and recommendations. Two reviews have been done: a literature review on Scopus, IEEE Xplore, Web of Knowledge and PubMed and a commercial review on Google play and the App Store. Five applications from each review have been selected to develop an in-depth analysis and to obtain more information about the mobile clinical decision support systems. Ninety-two relevant papers and 192 commercial apps were found. Forty-four papers were focused only on mobile clinical decision support systems. One hundred seventy-one apps were available on Google play and 21 on the App Store. The apps are designed for general medicine and 37 different specialties, with some features common in all of them despite of the different medical fields objective. The number of mobile clinical decision support applications and their inclusion in clinical practices has risen in the last years. However, developers must be careful with their interface or the easiness of use, which can impoverish the experience of the users.
A Knowledge-Grounded Multimodal Search-Based Conversational Agent

Multimodal search-based dialogue is a challenging new task: It extends visually grounded question answering systems into multi-turn conversations with access to an external database. We address this new challenge by learning a neural response generation system from the recently released Multimodal Dialogue (MMD) dataset (Saha et al., 2017). We introduce a knowledge-grounded multimodal conversational model where an encoded knowledge base (KB) representation is appended to the decoder input. Our model substantially outperforms strong baselines in terms of text-based similarity measures (over 9 BLEU points, 3 of which are solely due to the use of additional information from the KB).
DISRUPTIVE INNOVATION and CATALYTIC CHANGE in Higher Education

NOTEBOOK NOTEBOOK T he downfall of many successful and seemingly invincible companies has been precipitated by a disruptive innovation—that is, an innovation that makes a complicated and expensive product simpler and cheaper and thereby attracts a new set of customers. Business Administration at the Harvard Business School, describes how disruptive companies establish a foothold in the market, expand that market dramatically, and then inexorably migrate up the quality chain. Ultimately they pin the original leaders in the highest tiers of the market, where there simply is not enough volume to sustain them all. Christensen extends his theory from the business realm to higher education. Online business courses, for example, now offer lower-end and more convenient access to courses that can improve students' credentials or help them switch careers—which is often precisely what the student customers want to accomplish by enrolling. Traditional colleges and universities don't consider themselves in competition with these new entrants, but in the process of retreating from them they risk becoming more and more out of touch with the mainstream and, therefore, increasingly irrelevant. Disruptive companies typically establish a foothold in the market, expand that market dramatically, and then inexorably migrate up the quality chain. Ultimately they pin the original leaders in the highest tiers of the market, where there simply is not enough volume to sustain them all. Harvard Business School is being disrupted at the bott om of its core market by corporations that are sett ing up their own universities for their best employees. HBS still holds the advantage in providing networking, connections, and brand. No institutional mission fo-cuses on these jobs, however. Colleges and universities are being disrupted in many ways similar to Harvard Business School. Th e nation' s top institutions—the original leaders in the higher education market—are moving up the quality chain and losing touch with the mainstream. It is time that they completely rethink their model.
Behavioral dynamics on the web: Learning, modeling, and prediction

The queries people issue to a search engine and the results clicked following a query change over time. For example, after the earthquake in Japan in March 2011, the query japan spiked in popularity and people issuing the query were more likely to click government-related results than they would prior to the earthquake. We explore the modeling and prediction of such temporal patterns in Web search behavior. We develop a temporal modeling framework adapted from physics and signal processing and harness it to predict temporal patterns in search behavior using smoothing, trends, periodicities, and surprises. Using current and past behavioral data, we develop a learning procedure that can be used to construct models of users' Web search activities. We also develop a novel methodology that learns to select the best prediction model from a family of predictive models for a given query or a class of queries. Experimental results indicate that the predictive models significantly outperform baseline models that weight historical evidence the same for all queries. We present two applications where new methods introduced for the temporal modeling of user behavior significantly improve upon the state of the art. Finally, we discuss opportunities for using models of temporal dynamics to enhance other areas of Web search and information retrieval.
Towards modeling and analysis of cyber-physical medical systems

Contemporary health care systems are increasingly incorporating medical sensors and automatically controlled actuation devices to deliver smart and proactive health services. The embedded devices used in this regard continuously interact with the human body or patient either through sensing of physiological signals or through the execution of critical medical actuation such as drug delivery. Given the high risks in their deployment, patient safety during operation of these Cyber-Physical Medical Systems (CPMSes) has to be guaranteed even before use. This paper proposes CPS-MAS, a cyber-physical medical system modeling and analysis framework for safety verification. However, given the complex nature of interaction of the medical devices with the human body, characterized by non-linearity, transport delay, spatio-temporal effects, and nontrivial aggregation of interaction during networked operation of devices, such modeling and analysis is a challenging problem. The paper describes an approach to formal modeling and analysis of CPMSes with the help of two examples related to analgesic drug delivery and chemotherapy.
A biomechanical comparison of the traditional squat, powerlifting squat, and box squat.

The purpose of this study was to compare the biomechanics of the traditional squat with 2 popular exercise variations commonly referred to as the powerlifting squat and box squat. Twelve male powerlifters performed the exercises with 30, 50, and 70% of their measured 1 repetition maximum (1RM), with instruction to lift the loads as fast as possible. Inverse dynamics and spatial tracking of the external resistance were used to quantify biomechanical variables. A range of significant kinematic and kinetic differences (p < 0.05) emerged between the exercises. The traditional squat was performed with a narrow stance, whereas the powerlifting squat and box squat were performed with similar wide stances (48.3 ± 3.8, 89.6 ± 4.9, 92.1 ± 5.1 cm, respectively). During the eccentric phase of the traditional squat, the knee traveled past the toes resulting in anterior displacement of the system center of mass (COM). In contrast, during the powerlifting squat and box squat, a more vertical shin position was maintained, resulting in posterior displacements of the system COM. These differences in linear displacements had a significant effect (p < 0.05) on a number of peak joint moments, with the greatest effects measured at the spine and ankle. For both joints, the largest peak moment was produced during the traditional squat, followed by the powerlifting squat, then box squat. Significant differences (p < 0.05) were also noted at the hip joint where the largest moment in all 3 planes were produced during the powerlifting squat. Coaches and athletes should be aware of the biomechanical differences between the squatting variations and select according to the kinematic and kinetic profile that best match the training goals.
An electromagnetic fault injection sensor using Hogge phase-detector

Fault injection attack against embedded devices has attracted much attention in recent years. As a highly efficient fault injection, EM fault injection (EMFI) outperforms other injection means owing to its outstanding penetration capability in incurring local faults into security ICs. In this paper, we present an all digital countermeasure for detecting the on-the-fly EMFI attempts in silicon chips. The proposed logic consists of a watchdog ring-oscillator (RO), and a Hogge Phase Detector (PD) for sensing the frequency turbulence induced by the ongoing EMFIs. Experimental validation on Xilinx FPGA Virtex-5 FPGA reports a fault detection rate of 93.15% and a failure rate of 0.0069, with negligible overhead. A significant security margin for alerting the injection attempt is also noticeable. The technique is versatile and can be integrated in any VLSI design for its lightweight and all digital architecture, especially for the security-critical scenarios, such as the endpoints of Internet-of-Things (IoT).
Breast cancer. Knowledge, attitudes and practices of breast self examination among women in Qassim region of Saudi Arabia.

To determine the knowledge, attitudes and practices of women in Qassim region regarding breast self examination (BSE), and also to explore their level of knowledge regarding breast cancer.



We conducted a cross-sectional survey during the period from May to June 2005, among Saudi female patients attending the Primary Health Care Centers of Qassim region. A total of 300 females, 20-70 years of age, were interviewed in 10 randomly selected primary health care centers.



The mean age of the participants was 36.2 +/- 10.2 years, and 70.7% of them were literate. Regarding the knowledge of risk factors, 76% of the respondents had 3 or more correct answers out of the total 7 questions. Twenty-six percent of the respondents did not know the presenting symptom of breast cancer. Whereas, 69.7% of the participants had never heard of BSE. The participants had a positive attitude towards learning BSE. Of the total respondents, 18.7% reported that they practice BSE, majority (57%) of whom had started performing it within the previous year. However, 74% of the respondents did not have access to breast health information.



This study concludes that the level of awareness of the females of Qassim region regarding breast cancer and BSE is not adequate and a health education program for this subject should be introduced in the region.
Targeting the PI3K/Akt/mTOR pathway: effective combinations and clinical considerations.

The PI3K/Akt/mTOR pathway is a prototypic survival pathway that is constitutively activated in many types of cancer. Mechanisms for pathway activation include loss of tumor suppressor PTEN function, amplification or mutation of PI3K, amplification or mutation of Akt, activation of growth factor receptors, and exposure to carcinogens. Once activated, signaling through Akt can be propagated to a diverse array of substrates, including mTOR, a key regulator of protein translation. This pathway is an attractive therapeutic target in cancer because it serves as a convergence point for many growth stimuli, and through its downstream substrates, controls cellular processes that contribute to the initiation and maintenance of cancer. Moreover, activation of the Akt/mTOR pathway confers resistance to many types of cancer therapy, and is a poor prognostic factor for many types of cancers. This review will provide an update on the clinical progress of various agents that target the pathway, such as the Akt inhibitors perifosine and PX-866 and mTOR inhibitors (rapamycin, CCI-779, RAD-001) and discuss strategies to combine these pathway inhibitors with conventional chemotherapy, radiotherapy, as well as newer targeted agents. We will also discuss how the complex regulation of the PI3K/Akt/mTOR pathway poses practical issues concerning the design of clinical trials, potential toxicities and criteria for patient selection.
Application with a XY-plotter controlled by PLC used in student laboratory works

In many specific laboratories the students use only a PLC simulator software, because the hardware equipment is expensive. This paper presents a solution that allows students to study both the hardware and software parts, in the laboratory works. The hardware part of solution consists in an old plotter, an adapter board, a PLC and a HMI. The software part of this solution is represented by the projects of the students, in which they developed applications for programming the PLC and the HMI. This equipment can be made very easy and can be used in university labs by students, so that they design and test their applications, from low to high complexity , .
Augmentable Gamma Belief Networks

To infer multilayer deep representations of high-dimensional discrete and nonnegative real vectors, we propose an augmentable gamma belief network (GBN) that factorizes each of its hidden layers into the product of a sparse connection weight matrix and the nonnegative real hidden units of the next layer. The GBN’s hidden layers are jointly trained with an upward-downward Gibbs sampler that solves each layer with the same subroutine. The gamma-negative binomial process combined with a layer-wise training strategy allows inferring the width of each layer given a fixed budget on the width of the first layer. Example results illustrate interesting relationships between the width of the first layer and the inferred network structure, and demonstrate that the GBN can add more layers to improve its performance in both unsupervisedly extracting features and predicting heldout data. For exploratory data analysis, we extract trees and subnetworks from the learned deep network to visualize how the very specific factors discovered at the first hidden layer and the increasingly more general factors discovered at deeper hidden layers are related to each other, and we generate synthetic data by propagating random variables through the deep network from the top hidden layer back to the bottom data layer.
Effect of the World Health Organization checklist on patient outcomes: a stepped wedge cluster randomized controlled trial.

We hypothesized reduction of 30 days' in-hospital morbidity, mortality, and length of stay postimplementation of the World Health Organization's Surgical Safety Checklist (SSC).



Reductions of morbidity and mortality have been reported after SSC implementation in pre-/postdesigned studies without controls. Here, we report a randomized controlled trial of the SSC.



A stepped wedge cluster randomized controlled trial was conducted in 2 hospitals. We examined effects on in-hospital complications registered by International Classification of Diseases, Tenth Revision codes, length of stay, and mortality. The SSC intervention was sequentially rolled out in a random order until all 5 clusters-cardiothoracic, neurosurgery, orthopedic, general, and urologic surgery had received the Checklist. Data were prospectively recorded in control and intervention stages during a 10-month period in 2009-2010.



A total of 2212 control procedures were compared with 2263 SCC procedures. The complication rates decreased from 19.9% to 11.5% (P < 0.001), with absolute risk reduction 8.4 (95% confidence interval, 6.3-10.5) from the control to the SSC stages. Adjusted for possible confounding factors, the SSC effect on complications remained significant with odds ratio 1.95 (95% confidence interval, 1.59-2.40). Mean length of stay decreased by 0.8 days with SCC utilization (95% confidence interval, 0.11-1.43). In-hospital mortality decreased significantly from 1.9% to 0.2% in 1 of the 2 hospitals post-SSC implementation, but the overall reduction (1.6%-1.0%) across hospitals was not significant.



Implementation of the WHO SSC was associated with robust reduction in morbidity and length of in-hospital stay and some reduction in mortality.
Genetic algorithm-based heuristic for feature selection in credit risk assessment

In this paper, an advanced novel heuristic algorithm is presented, the hybrid genetic algorithm with neural networks (HGA-NN), which is used to identify an optimum feature subset and to increase the classification accuracy and scalability in credit risk assessment. This algorithm is based on the following basic hypothesis: the high-dimensional input feature space can be preliminarily restricted to only the important features. In this preliminary restriction, fast algorithms for feature ranking and earlier experience are used. Additionally, enhancements are made in the creation of the initial population, as well as by introducing an incremental stage in the genetic algorithm. The performances of the proposed HGA-NN classifier are evaluated using a real-world credit dataset that is collected at a Croatian bank, and the findings are further validated on another real-world credit dataset that is selected in a UCI database. The classification accuracy is compared with that presented in the literature. Experimental results that were achieved using the proposed novel HGA-NN classifier are promising for feature selection and classification in retail credit risk assessment and indicate that the HGA-NN classifier is a promising addition to existing data mining techniques.
Castanea spp. biodiversity conservation: collection and characterization of the genetic diversity of an endangered species

Centuries of co-evolution between Castanea spp. biodiversity and human populations has resulted in the spread of rich and varied chestnut genetic diversity throughout most of the world, especially in mountainous and forested regions. Its plasticity and adaptability to different pedoclimates and the wide genetic variability of the species determined the spread of many different ecotypes and varieties in the wild. Throughout the centuries, man has used, selected and preserved these different genotypes, vegetatively propagating them by grafting, for many applications: fresh consumption, production of flour, animal nutrition, timber production, thereby actively contributing to the maintenance of the natural biodiversity of the species, and providing an excellent example of conservation horticulture. Nonetheless, currently the genetic variability of the species is critically endangered and hundreds of ecotypes and varieties are at risk of being lost due to a number of phytosanitary problems (canker blight, Chryphonectria parasitica; ink disease, Phytophthora spp.; gall wasp, Dryocosmus kuriphilus), and because of the many years of decline and abandonment of chestnut cultivation, which resulted in the loss of the binomial male chestnut. Recently, several research and experimentation programmes have attempted to develop strategies for the conservation of chestnut biodiversity. The purpose of this paper is to give an overview of the status of biodiversity conservation of the species and to present the results of a 7 year project aimed at the individuation and study of genetic diversity and conservation of Castanea spp. germplasm.
Algebraic Cryptanalysis of GOST Encryption Algorithm

This paper observes approaches to algebraic analysis of GOST 28147-89 encryption algorithm (also known as simply GOST), which is the basis of most secure information systems in Russia. The general idea of algebraic analysis is based on the representation of initial encryption algorithm as a system of multivariate quadratic equations, which define relations between a secret key and a cipher text. Extended linearization method is evaluated as a method for solving the nonlinear system of equations.
Over 450-GHz ft and fmax InP/InGaAs DHBTs With a Passivation Ledge Fabricated by Utilizing SiN/SiO2 Sidewall Spacers

This paper describes InP/InGaAs double heterojunction bipolar transistor (HBT) technology that uses SiN/SiO2 sidewall spacers. This technology enables the formation of ledge passivation and narrow base metals by i-line lithography. With this process, HBTs with various emitter sizes and emitter-base (EB) spacings can be fabricated on the same wafer. The impact of the emitter size and EB spacing on the current gain and high-frequency characteristics is investigated. The reduction of the current gain is <;5% even though the emitter width decreases from 0.5 to 0.25 μm. A high current gain of over 40 is maintained even for a 0.25-μm emitter HBT. The HBTs with emitter widths ranging from 0.25 to 0.5 μm also provide peak ft of over 430 GHz. On the other hand, peak fmax greatly increases from 330 to 464 GHz with decreasing emitter width from 0.5 to 0.25 μm. These results indicate that the 0.25-μm emitter HBT with the ledge passivaiton exhibits balanced high-frequency performance (ft = 452 GHz and fmax = 464 GHz), while maintaining a current gain of over 40.
Intelligent stock trading system based on improved technical analysis and Echo State Network

Stock trading system to assist decision-making is an emerging research area and has great commercial potentials. Successful trading operations should occur near the reversal points of price trends. Traditional technical analysis, which usually appears as various trading rules, does aim to look for peaks and bottoms of trends and is widely used in stock market. Unfortunately, it is not convenient to directly apply technical analysis since it depends on person’s experience to select appropriate rules for individual share. In this paper, we enhance conventional technical analysis with Genetic Algorithms by learning trading rules from history for individual stock and then combine different rules together with Echo State Network to provide trading suggestions. Numerous experiments on S&P 500 components demonstrate that whether in bull or bear market, our system significantly outperforms buy-and-hold strategy. Especially in bear market where S&P 500 index declines a lot, our system still profits.