nils-herrmann/clean_scirepeval huggingface dataset:
NCR: A Scalable Network-Based Approach to Co-Ranking in Question-and-Answer Sites

Question-and-answer (Q&A) websites, such as Yahoo! Answers, Stack Overflow and Quora, have become a popular and powerful platform for Web users to share knowledge on a wide range of subjects. This has led to a rapidly growing volume of information and the consequent challenge of readily identifying high quality objects (questions, answers and users) in Q&A sites. Exploring the interdependent relationships among different types of objects can help find high quality objects in Q&A sites more accurately. In this paper, we specifically focus on the ranking problem of co-ranking questions, answers and users in a Q&A website. By studying the tightly connected relationships between Q&A objects, we can gain useful insights toward solving the co-ranking problem. However, co-ranking multiple objects in Q&A sites is a challenging task: a) With the large volumes of data in Q&A sites, it is important to design a model that can scale well; b) The large-scale Q&A data makes extracting supervised information very expensive. In order to address these issues, we propose an unsupervised Network-based Co-Ranking framework (NCR) to rank multiple objects in Q&A sites. Empirical studies on real-world Yahoo! Answers datasets demonstrate the effectiveness and the efficiency of the proposed NCR method.
Language control in bilinguals: The adaptive control hypothesis

Speech comprehension and production are governed by control processes. We explore their nature and dynamics in bilingual speakers with a focus on speech production. Prior research indicates that individuals increase cognitive control in order to achieve a desired goal. In the adaptive control hypothesis we propose a stronger hypothesis: Language control processes themselves adapt to the recurrent demands placed on them by the interactional context. Adapting a control process means changing a parameter or parameters about the way it works (its neural capacity or efficiency) or the way it works in concert, or in cascade, with other control processes (e.g., its connectedness). We distinguish eight control processes (goal maintenance, conflict monitoring, interference suppression, salient cue detection, selective response inhibition, task disengagement, task engagement, opportunistic planning). We consider the demands on these processes imposed by three interactional contexts (single language, dual language, and dense code-switching). We predict adaptive changes in the neural regions and circuits associated with specific control processes. A dual-language context, for example, is predicted to lead to the adaptation of a circuit mediating a cascade of control processes that circumvents a control dilemma. Effective test of the adaptive control hypothesis requires behavioural and neuroimaging work that assesses language control in a range of tasks within the same individual.
Blood pressure estimation from photoplethysmogram using latent parameters

Non-invasive cuff-less Blood Pressure (BP) estimation from Photoplethysmogram (PPG) is a well known challenge in the field of affordable healthcare. This paper presents a set of improvements over an existing method that estimates BP using 2-element Windkessel model from PPG signal. A noisy PPG corpus is collected using fingertip pulse oximeter, from two different locations in India. Exhaustive pre-processing techniques, such as filtering, baseline and topline correction are performed on the noisy PPG signals, followed by the selection of consistent cycles. Subsequently, the most relevant PPG features and demographic features are selected through Maximal Information Coefficient (MIC) score for learning the latent parameters controlling BP. Experimental results reveal that overall error in estimating BP lies within 10% of a commercially available digital BP monitoring device. Also, use of alternative latent parameters that incorporate the variation in cardiac output, shows a better trend following for abnormally low and high BP.
Infants selectively encode the goal object of an actor's reach

Research with young children has shown that, like adults, they focus selectively on the aspects of an actor's behavior that are relevant to his or her underlying intentions. The current studies used the visual habituation paradigm to ask whether infants would similarly attend to those aspects of an action that are related to the actor's goals. Infants saw an actor reach for and grasp one of two toys sitting side by side on a curtained stage. After habituation, the positions of the toys were switched and babies saw test events in which there was a change in either the path of motion taken by the actor's arm or the object that was grasped by the actor. In the first study, 9-month-old infants looked longer when the actor grasped a new toy than when she moved through a new path. Nine-month-olds who saw an inanimate object of approximately the same dimensions as the actor's arm touch the toy did not show this pattern in test. In the second study, 5-month-old infants showed similar, though weaker, patterns. A third study provided evidence that the findings for the events involving a person were not due to perceptual changes in the objects caused by occlusion by the hand. A fourth study replicated the 9 month results for a human grasp at 6 months, and revealed that these effects did not emerge when infants saw an inanimate object with digits that moved to grasp the toy. Taken together, these findings indicate that young infants distinguish in their reasoning about human action and object motion, and that by 6 months infants encode the actions of other people in ways that are consistent with more mature understandings of goal-directed action.
SnaPEA: Predictive Early Activation for Reducing Computation in Deep Convolutional Neural Networks

Deep Convolutional Neural Networks (CNNs) perform billions of operations for classifying a single input. To reduce these computations, this paper offers a solution that leverages a combination of runtime information and the algorithmic structure of CNNs. Specifically, in numerous modern CNNs, the outputs of compute-heavy convolution operations are fed to activation units that output zero if their input is negative. By exploiting this unique algorithmic property, we propose a predictive early activation technique, dubbed SnaPEA. This technique cuts the computation of convolution operations short if it determines that the output will be negative. SnaPEA can operate in two distinct modes, exact and predictive. In the exact mode, with no loss in classification accuracy, SnaPEA statically re-orders the weights based on their signs and periodically performs a single-bit sign check on the partial sum. Once the partial sum drops below zero, the rest of computations can simply be ignored, since the output value will be zero in any case. In the predictive mode, which trades the classification accuracy for larger savings, SnaPEA speculatively cuts the computation short even earlier than the exact mode. To control the accuracy, we develop a multi-variable optimization algorithm that thresholds the degree of speculation. As such, the proposed algorithm exposes a knob to gracefully navigate the trade-offs between the classification accuracy and computation reduction. Compared to a state-of-the-art CNN accelerator, SnaPEA in the exact mode, yields, on average, 28% speedup and 16% energy reduction in various modern CNNs without affecting their classification accuracy. With 3% loss in classification accuracy, on average, 67.8% of the convolutional layers can operate in the predictive mode. The average speedup and energy saving of these layers are 2.02x and 1.89x, respectively. The benefits grow to a maximum of 3.59x speedup and 3.14x energy reduction. Compared to static pruning approaches, which are complimentary to the dynamic approach of SnaPEA, our proposed technique offers up to 63% speedup and 49% energy reduction across the convolution layers with no loss in classification accuracy.
A general EM-based design procedure for single-layer substrate integrated waveguide interconnects with microstrip transitions

We propose in this work a general procedure to efficient EM-based design of single-layer SIW interconnects, including their transitions to microstrip lines. Our starting point is developed by exploiting available empirical knowledge for SIW. We propose an efficient SIW surrogate model for direct EM design optimization in two stages: first optimizing the SIW width to achieve the specified low cutoff frequency, followed by the transition optimization to reduce reflections and extend the dominant mode bandwidth. Our procedure is illustrated by designing a SIW interconnect on a standard FR4-based substrate.
Crime Hot Spot Forecasting: A Recurrent Model with Spatial and Temporal Information

Crime is a major social problem in the United States, threatening public safety and disrupting the economy. Understanding patterns in criminal activity allows for the prediction of future high-risk crime “hot spots” and enables police precincts to more effectively allocate officers to prevent or respond to incidents. With the ever-increasing ability of states and organizations to collect and store detailed data tracking crime occurrence, a significant amount of data with spatial and temporal information has been collected. How to use the benefit of massive spatial-temporal information to precisely predict the regional crime rates becomes necessary. The recurrent neural network model has been widely proven effective for detecting the temporal patterns in a time series. In this study, we propose the Spatio-Temporal neural network (STNN) to precisely forecast crime hot spots with embedding spatial information. We evaluate the model using call-for-service data provided by the Portland, Oregon Police Bureau (PPB) for a 5-year period from March 2012 through the end of December 2016. We show that our STNN model outperforms a number of classical machine learning approaches and some alternative neural network architectures.
Orchestration of RAN and Transport Networks for 5G: An SDN Approach

The fifth generation of mobile networks is planned to be commercially available in a few years. The scope of 5G goes beyond introducing new radio interfaces, and will include new services like low-latency industrial applications, as well as new deployment models such as cooperative cells and densification through small cells. An efficient realization of these new features greatly benefit from tight coordination among radio and transport network resources, something that is missing in current networks. In this article, we first present an overview of the benefits and technical requirements of resource coordination across radio and transport networks in the context of 5G. Then, we discuss how SDN principles can bring programmability to both the transport and radio domains, which in turn enables the design of a hierarchical, modular, and programmable control and orchestration plane across the domains. Finally, we introduce two use cases of SDN-based transport and RAN orchestration, and present an experimental implementation of them in a testbed in our lab, which confirms the feasibility and benefits of the proposed orchestration.
Mobile Banking Information Security and Protection Methods

With the help of modern information communication technology, mobile banking as a new type of financial services carrier can provide efficient and effective financial services for clients. Compare with Internet banking, mobile banking is more secure and user friendly. The implementation of wireless communication technologies may result in more complicated information security problems. Based on the principles of information security, this paper presented issues of information security of mobile banking and discussed the security protection measures such as: encryption technology, identity authentication, digital signature, WPKI technology.
Development of Optimal Photosensors Based Heart Pulse Detector

Based Heart Pulse Detector N. M. Z. Hashim*, N. A. Ali*, A. Salleh*3, A. S. Ja’afar*4, N. A. Z. Abidin* * Faculty of Electronics & Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia *nikzarifie@utem.edu.my, *alisa@utem.edu.my, *azahari@utem.edu.my, *shukur@utem.edu.my, *naza_alin@yahoo.com Abstract— The development of heart pulse instruments rapidly fast in market since 21 century. However, the heart pulse detector is expensive due to the complicated system and it is used widely only in hospitals and clinics. The project is targeting to develop a significant photosensor to the medical fields that is easy to use and monitor their health by the user everywhere. The other target is to develop a comfortable instrument, reliable, accurate result to develop of heart pulse using low cost photosensors. This project involved both hardware and software with related to signal processing, mathematical, computational, formalisms, modeling techniques for transforming, transmitting and also for analog or digital signal. This project also used Peripheral Interface Controller (PIC) 16F877A microcontroller as the main function to control other elements. Result showed this project functioned smoothly and successfully with overall objectives were achieved. Apart from that, this project give good services for people to monitor their heart condition form time to time. In the future, wireless connection e.g. Global System for Mobile Communications (GSM) and Zigbee would be developed to make the system more reliable to the current world. Furthermore, the system should be compatible to various environments such as Android based OS so that it can be controlled away from the original location. KeywordColour Wavelength, Heart Rate, Photosensor, PIC 16F877A Microcontroller, Sensor
8.2 Batteryless Sub-nW Cortex-M0+ processor with dynamic leakage-suppression logic

Recent low-voltage design techniques have enabled dramatic improvements in miniaturization and lifetime of wireless sensor nodes [1-3]. These systems typically use a secondary battery to provide energy when the sensor is awake and operating; the battery is then recharged from a harvesting source when the sensor is asleep. In these systems, the key requirement is to minimize energy per operation of the sensor. This extends the number of operations on one battery charge and/or reduces the time to recharge the battery between awake cycles. This requirement has driven significant advances in energy efficiency [1-2] and standby power consumption .
Parallel neural networks for learning sequential procedures

Recent studies have shown that multiple brain areas contribute to different stages and aspects of procedural learning. On the basis of a series of studies using a sequence-learning task with trial-and-error, we propose a hypothetical scheme in which a sequential procedure is acquired independently by two cortical systems, one using spatial coordinates and the other using motor coordinates. They are active preferentially in the early and late stages of learning, respectively. Both of the two systems are supported by loop circuits formed with the basal ganglia and the cerebellum, the former for reward-based evaluation and the latter for processing of timing. The proposed neural architecture would operate in a flexible manner to acquire and execute multiple sequential procedures.
Temporal Data Mining: an overview

One of the main unresolved problems that arise during the data mining process is treating data that contains temporal information. In this case, a complete understanding of the entire phenomenon requires that the data should be viewed as a sequence of events. Temporal sequences appear in a vast range of domains, from engineering, to medicine and finance, and the ability to model and extract information from them is crucial for the advance of the information society. This paper provides a survey on the most significant techniques developed in the past ten years to deal with temporal sequences.
Perceptions and Practices regarding Breastfeeding among Postnatal Women at a District Tertiary Referral Government Hospital in Southern India

Breastfeeding is the optimal method for achieving a normal growth and development of the baby. This study aimed to study mothers' perceptions and practices regarding breastfeeding in Mangalore, India.  A cross-sectional study of 188 mothers was conducted using a structured proforma.  Importance of breast feeding was known to most mothers. While initiation of breast feeding within one hour of birth was done by majority of mothers, few had discarded colostrum and adopted prelacteal feeding. Mothers opined that breast feeding is healthy for their babies (96.3%) and easier than infant feeding (79.8%), does not affect marital relationship (51%), and decreases family expenditure (61.1%). However, there were poor perceptions regarding the advantages of breast milk with respect to nutritive value, immune effect, and disease protection. Few respondents reported discontinuation of breastfeeding in previous child if the baby had fever/cold (6%) or diarrhea (18%) and vomiting (26%). There was a statistically significant association between mother's educational level and perceived importance of breastfeeding and also between the mode of delivery and initiation of breast feeding (p < 0.05).  Importance of breast feeding was known to most mothers. Few perceptions related to breast milk and feeding along with myths and disbeliefs should be rectified by health education.
Effectiveness of Opcode ngrams for Detection of Multi Family Android Malware

With the wide diffusion of smartphones and their usage in a plethora of processes and activities, these devices have been handling an increasing variety of sensitive resources. Attackers are hence producing a large number of malware applications for Android (the most spread mobile platform), often by slightly modifying existing applications, which results in malware being organized in families. Some works in the literature showed that opcodes are informative for detecting malware, not only in the Android platform. In this paper, we investigate if frequencies of ngrams of opcodes are effective in detecting Android malware and if there is some significant malware family for which they are more or less effective. To this end, we designed a method based on state-of-the-art classifiers applied to frequencies of opcodes ngrams. Then, we experimentally evaluated it on a recent dataset composed of 11120 applications, 5560 of which are malware belonging to several different families. Results show that an accuracy of 97% can be obtained on the average, whereas perfect detection rate is achieved for more than one malware family.
XOGAN: One-to-Many Unsupervised Image-to-Image Translation

Unsupervised image-to-image translation aims at learning the relationship between samples from two image domains without supervised pair information. The relationship between two domain images can be one-to-one, one-to-many or many-to-many. In this paper, we study the one-to-many unsupervised image translation problem in which an input sample from one domain can correspond to multiple samples in the other domain. To learn the complex relationship between the two domains, we introduce an additional variable to control the variations in our one-to-many mapping. A generative model with an XO-structure, called the XOGAN, is proposed to learn the cross domain relationship among the two domains and the additional variables. Not only can we learn to translate between the two image domains, we can also handle the translated images with additional variations. Experiments are performed on unpaired image generation tasks, including edges-to-objects translation and facial image translation. We show that the proposed XOGAN model can generate plausible images and control variations, such as color and texture, of the generated images. Moreover, while state-of-the-art unpaired image generation algorithms tend to generate images with monotonous colors, XOGAN can generate more diverse results.
A study of relationship between managers' leadership style and employees' job satisfaction.

The purpose of this descriptive and cross-sectional study is to explore the relationships between managers' leadership styles and employees' job satisfaction in Isfahan University Hospitals, Isfahan, Iran, 2004.



The data were collected through the distribution of two questionnaires among the 814 employees, first line, middle and senior managers of these hospitals through a stratified random sampling.



The dominant leadership style of managers was participative. The mean score of employee-oriented dimension of leadership style in first line, middle and senior managers were 52, 54, and 54 (from 75 credit) respectively. The mean score of task-oriented dimension of leadership style in first line, middle and senior managers were 68, 69, and 70 (from 100 credit) respectively. The mean score of employee's job satisfaction was 3.26 +/- 0.56 on a 6 scale (moderate satisfaction), 1.9, 26.1, 64.7, and 7.3 percent of hospital employees had respectively very low, low, moderate, and high satisfaction with their job. Employees demonstrated less satisfaction with salaries, benefits, work conditions, promotion and communication as satisfier factors and more satisfaction with factors such as the nature of the job, co-workers and supervision type factors. There was significant correlation (p < 0.001) between the use of leadership behaviors and employees and job satisfaction.



Employee job satisfaction depends upon the leadership style of managers. Nevertheless, participative management is not always a good management style. Managers should select the best leadership style according to the organizational culture and employees' organizational maturity.



Although this study conducted in Iran, it is anticipated that the findings may have relevance on a broader scale. By replicating this study in different countries and contexts the results of could be very helpful for developing a new model of leadership with new implementation techniques that can be implemented easily and successfully in a cross cultural context.
Image Watermarking for Tamper Detection

We propose an oblivious watermarking technique for tamper detection in digital images. By comparing correlation values from different portions of the image, the technique enables us to distinguish malicious changes, such as replacing / adding features from nonmalicious changes resulting from common image processing operations. The technique can be implemented with small memory and computational requirements, which makes it potentially useful for hardware implementation in digital cameras. The technique works by dividing an image into blocks and watermarking each block with a transparent, robust watermark that sensitively depends on a secret key (camera’s ID) and continuously on the image. The watermarking method is a frequency based spread spectrum technique. To achieve a continuous dependency on the image, we propose a special bit extraction procedure that extracts bits from each block by thresholding projections onto key-dependent random smooth patterns. Those bits are then used for initializing a PRNG and synthesizing the spread spectrum signal.