nils-herrmann/clean_scirepeval huggingface dataset:
Energy-Efficient UAV Communication With Trajectory Optimization

Wireless communication with unmanned aerial vehicles (UAVs) is a promising technology for future communication systems. In this paper, assuming that the UAV flies horizontally with a fixed altitude, we study energy-efficient UAV communication with a ground terminal via optimizing the UAV’s trajectory, a new design paradigm that jointly considers both the communication throughput and the UAV’s energy consumption. To this end, we first derive a theoretical model on the propulsion energy consumption of fixed-wing UAVs as a function of the UAV’s flying speed, direction, and acceleration. Based on the derived model and by ignoring the radiation and signal processing energy consumption, the energy efficiency of UAV communication is defined as the total information bits communicated normalized by the UAV propulsion energy consumed for a finite time horizon. For the case of unconstrained trajectory optimization, we show that both the rate-maximization and energy-minimization designs lead to vanishing energy efficiency and thus are energy-inefficient in general. Next, we introduce a simple circular UAV trajectory, under which the UAV’s flight radius and speed are jointly optimized to maximize the energy efficiency. Furthermore, an efficient design is proposed for maximizing the UAV’s energy efficiency with general constraints on the trajectory, including its initial/final locations and velocities, as well as minimum/maximum speed and acceleration. Numerical results show that the proposed designs achieve significantly higher energy efficiency for UAV communication as compared with other benchmark schemes.
Saliency detection by multi-context deep learning

Low-level saliency cues or priors do not produce good enough saliency detection results especially when the salient object presents in a low-contrast background with confusing visual appearance. This issue raises a serious problem for conventional approaches. In this paper, we tackle this problem by proposing a multi-context deep learning framework for salient object detection. We employ deep Convolutional Neural Networks to model saliency of objects in images. Global context and local context are both taken into account, and are jointly modeled in a unified multi-context deep learning framework. To provide a better initialization for training the deep neural networks, we investigate different pre-training strategies, and a task-specific pre-training scheme is designed to make the multi-context modeling suited for saliency detection. Furthermore, recently proposed contemporary deep models in the ImageNet Image Classification Challenge are tested, and their effectiveness in saliency detection are investigated. Our approach is extensively evaluated on five public datasets, and experimental results show significant and consistent improvements over the state-of-the-art methods.
Putting the altruism back into altruism: the evolution of empathy.

Evolutionary theory postulates that altruistic behavior evolved for the return-benefits it bears the performer. For return-benefits to play a motivational role, however, they need to be experienced by the organism. Motivational analyses should restrict themselves, therefore, to the altruistic impulse and its knowable consequences. Empathy is an ideal candidate mechanism to underlie so-called directed altruism, i.e., altruism in response to anothers's pain, need, or distress. Evidence is accumulating that this mechanism is phylogenetically ancient, probably as old as mammals and birds. Perception of the emotional state of another automatically activates shared representations causing a matching emotional state in the observer. With increasing cognition, state-matching evolved into more complex forms, including concern for the other and perspective-taking. Empathy-induced altruism derives its strength from the emotional stake it offers the self in the other's welfare. The dynamics of the empathy mechanism agree with predictions from kin selection and reciprocal altruism theory.
VLSI testing based security metric for IC camouflaging

An Integrated Circuit (IC) can be reverse engineered by imaging its layout and reconstructing the netlist. IC camouflaging is a layout-level technique that hampers imaging-based reverse engineering by using, in one embodiment, functionally different standard cells that look alike. Reverse engineering will fail if the functionality of a camouflaged gate cannot be correctly resolved. We adapt VLSI testing principles (justification and sensitization) to quantify the ability of a reverse engineer to unambiguously resolve the functionality of look-alike camouflaged gates. We evaluate the security of look-alike standard cells based IC camouflaging by applying it on the controllers in OpenSPARC T1 processor.
A Shift-Tolerant Dissimilarity Measure for Surface Defect Detection

Template matching has been widely used in image processing for visual inspection of complicated, patterned surfaces. The currently existing methods such as golden template matching and normalized cross correlation are very sensitive to the displacement even the object under test is carefully aligned with respect to the template. This paper proposes a dissimilarity measure based on the optical-flow technique for surface defect detection, and aims at light-emitting diode (LED) wafer die inspection. The dissimilarity measure of each pixel derived from the optical flow field does not represent the true translation distance, but is reliable enough to indicate the degree of difference between an image pair. It is well tolerated to misalignment and random product variation. The integral image technique is applied to replace the sum operations in optical flow computation, and speeds up the intensive computation. We also point out the pitfall of the Lucas-Kanade optical flow when it is applied for defect detection, and propose a swapping process to tackle the problem. The experiment on LED wafer dies has shown that the proposed method can achieve a 100% recognition rate based on a test set of 357 die images.
Mutation testing implements grammar-based testing

This paper presents an abstract view of mutation analysis. Mutation was originally thought of as making changes to program source, but similar kinds of changes have been applied to other artifacts, including program specifications, XML, and input languages. This paper argues that mutation analysis is actually a way to modify any software artifact based on its syntactic description, and is in the same family of test generation methods that create inputs from syntactic descriptions. The essential characteristic of mutation is that a syntactic description such as a grammar is used to create tests. We call this abstract view grammar-based testing, and view it as an interface, which mutation analysis implements. This shift in view allows mutation to be defined in a general way, yielding three benefits. First, it provides a simpler way to understand mutation. Second, it makes it easier to develop future applications of mutation analysis, such as finite state machines and use case collaboration diagrams. The third benefit, which due to space limitations is not explored in this paper, is ensuring that existing techniques are complete according to the criteria defined here.
High accuracy approximate multiplier with error correction

Approximate computing has gained significant attention due to the popularity of multimedia applications. In this paper, we propose a novel inaccurate 4:2 counter that can effectively reduce the partial product stages of the Wallace Multiplier. Compared to the normal Wallace multiplier, our proposed multiplier can reduce 10.74% of power consumption and 9.8% of delay on average, with an error rate from 0.2% to 13.76% The accuracy of amplitude is higher than 99% In addition, we further enhance the design with error-correction units to provide accurate results. The experimental results show that the extra power consumption of correct units is lower than 6% on average. Compared to the normal Wallace multiplier, the average latency of our proposed multiplier with EDC is 6% faster when the bit-width is 32, and the power consumption is still 10% lower than that of the Wallace multiplier.
Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier

With the rapid development of the World Wide Web, electronic word-of-mouth interaction has made consumers active participants. Nowadays, a large number of reviews posted by the consumers on the Web provide valuable information to other consumers. Such information is highly essential for decision making and hence popular among the internet users. This information is very valuable not only for prospective consumers to make decisions but also for businesses in predicting the success and sustainability. In this paper, a Gini Index based feature selection method with Support Vector Machine (SVM) classifier is proposed for sentiment classification for large movie review data set. The results show that our Gini Index method has better classification performance in terms of reduced error rate and accuracy.
Knowledge, attitudes, and breast feeding practices of postnatal mothers: A cross sectional survey.

Breast feeding has several benefits for both the infants and mothers. However, despite strong evidences in support of breast feeding its prevalence has remained low worldwide. The objective of the present study was to examine the knowledge and attitude towards breast feeding and infant feeding practices among Indian postnatal mothers.



A cross sectional descriptive study was carried out among randomly selected postnatal mothers at Pediatric outpatient department at a tertiary care center. Data was collected through face-to-face interview using a structured questionnaire.



Our findings revealed that a majority (88.5%) of the mothers were breast feeders. However, merely 27% of the mothers were exclusive breast feeders and only 36.9% initiated breast feeding within an hour. While mothers have good knowledge on breast feeding (12.05±1.74, M±SD), the average score of the Iowa Infant Feeding Scale (IIFAS) (58.77±4.74, M ±SD) indicate neutral attitudes toward breast feeding. Mothers those who were currently breast feeding (58.83 ± 4.74) had more positive attitudes than non- breastfeed mothers (45.21±5.22).



Our findings also show that the level of exclusive breast-feeding was low. Thus, it is important to provide prenatal education to mothers and fathers on breast-feeding. We also recommend strengthening the public health education campaigns to promote breast-feeding.
On Compressing Deep Models by Low Rank and Sparse Decomposition

Deep compression refers to removing the redundancy of parameters and feature maps for deep learning models. Low-rank approximation and pruning for sparse structures play a vital role in many compression works. However, weight filters tend to be both low-rank and sparse. Neglecting either part of these structure information in previous methods results in iteratively retraining, compromising accuracy, and low compression rates. Here we propose a unified framework integrating the low-rank and sparse decomposition of weight matrices with the feature map reconstructions. Our model includes methods like pruning connections as special cases, and is optimized by a fast SVD-free algorithm. It has been theoretically proven that, with a small sample, due to its generalizability, our model can well reconstruct the feature maps on both training and test data, which results in less compromising accuracy prior to the subsequent retraining. With such a warm start to retrain, the compression method always possesses several merits: (a) higher compression rates, (b) little loss of accuracy, and (c) fewer rounds to compress deep models. The experimental results on several popular models such as AlexNet, VGG-16, and GoogLeNet show that our model can significantly reduce the parameters for both convolutional and fully-connected layers. As a result, our model reduces the size of VGG-16 by 15&#xd7;, better than other recent compression methods that use a single strategy.
Semantic feature modelling

Almost all current feature modelling systems are parametric, history-based modelling systems. These systems suffer from a number of shortcomings with regard to the modelling process. In particular, they lack a complete specification of feature semantics, and therefore fail to maintain the meaning of the features during modelling. Also, modelling operations sometimes are hampered by the model history, and occasionally even have ill-defined semantics. In the semantic feature modelling approach presented here, the semantics of features is well defined and maintained during all modelling operations. The result of these operations is independent of the order of feature creation, and is well defined. The specification of feature classes, the structure and functionality of the feature model, in particular the Cellular Model, and the model validity maintenance scheme are described. The advantages and disadvantages of the approach, compared to current feature modelling approaches, are pointed out. q 2000 Elsevier Science Ltd. All rights reserved.
A Novel Quantitative Approach For Measuring Network Security

Evaluation of network security is an essential step in securing any network. This evaluation can help security professionals in making optimal decisions about how to design security countermeasures, to choose between alternative security architectures, and to systematically modify security configurations in order to improve security. However, the security of a network depends on a number of dynamically changing factors such as emergence of new vulnerabilities and threats, policy structure and network traffic. Identifying, quantifying and validating these factors using security metrics is a major challenge in this area. In this paper, we propose a novel security metric framework that identifies and quantifies objectively the most significant security risk factors, which include existing vulnerabilities, historical trend of vulnerability of the remotely accessible services, prediction of potential vulnerabilities for any general network service and their estimated severity and finally policy resistance to attack propagation within the network. We then describe our rigorous validation experiments using real- life vulnerability data of the past 6 years from National Vulnerability Database (NVD)  to show the high accuracy and confidence of the proposed metrics. Some previous works have considered vulnerabilities using code analysis. However, as far as we know, this is the first work to study and analyze these metrics for network security evaluation using publicly available vulnerability information and security policy configuration.
Aggressive 3-D collision avoidance for high-speed navigation

Autonomous robot navigation through unknown, cluttered environments at high-speeds is still an open problem. Quadrotor platforms with this capability have only begun to emerge with the advancements in light-weight, small form factor sensing and computing. Many of the existing platforms, however, require excessive computation time to perform collision avoidance, which ultimately limits the vehicle's top speed. This work presents an efficient perception and planning approach that significantly reduces the computation time by using instantaneous perception data for collision avoidance. Minimum-time, state and input constrained motion primitives are generated by sampling terminal states until a collision-free path is found. The worst case performance of the Triple Integrator Planner (TIP) is nearly an order of magnitude faster than the state-of-the-art. Experimental results demonstrate the algorithm's ability to plan and execute aggressive collision avoidance maneuvers in highly cluttered environments.
Impedance Control of an Intrinsically Compliant Parallel Ankle Rehabilitation Robot

Robot-aided physical therapy should encourage subject's voluntary participation to achieve rapid motor function recovery. In order to enhance subject's cooperation during training sessions, the robot should allow deviation in the prescribed path depending on the subject's modified limb motions subsequent to the disability. In the present work, an interactive training paradigm based on the impedance control was developed for a lightweight intrinsically compliant parallel ankle rehabilitation robot. The parallel ankle robot is powered by pneumatic muscle actuators (PMAs). The proposed training paradigm allows the patients to modify the robot imposed motions according to their own level of disability. The parallel robot was operated in four training modes namely position control, zero-impedance control, nonzero-impedance control with high compliance, and nonzero-impedance control with low compliance to evaluate the performance of proposed control scheme. The impedance control scheme was evaluated on 10 neurologically intact subjects. The experimental results show that an increase in robotic compliance encouraged subjects to participate more actively in the training process. This work advances the current state of the art in the compliant actuation of parallel ankle rehabilitation robots in the context of interactive training.
An efficient B-tree layer implementation for flash-memory storage systems

With the significant growth of the markets for consumer electronics and various embedded systems, flash memory is now an economic solution for storage systems design. Because index structures require intensively fine-grained updates/modifications, block-oriented access over flash memory could introduce a significant number of redundant writes. This might not only severely degrade the overall performance, but also damage the reliability of flash memory. In this paper, we propose a very different approach, which can efficiently handle fine-grained updates/modifications caused by B-tree index access over flash memory. The implementation is done directly over the flash translation layer (FTL); hence, no modifications to existing application systems are needed. We demonstrate that when index structures are adopted over flash memory, the proposed methodology can significantly improve the system performance and, at the same time, reduce both the overhead of flash-memory management and the energy dissipation. The average response time of record insertions and deletions was also significantly reduced.
The cognitive neuroscience of human memory since H.M.

Work with patient H.M., beginning in the 1950s, established key principles about the organization of memory that inspired decades of experimental work. Since H.M., the study of human memory and its disorders has continued to yield new insights and to improve understanding of the structure and organization of memory. Here we review this work with emphasis on the neuroanatomy of medial temporal lobe and diencephalic structures important for memory, multiple memory systems, visual perception, immediate memory, memory consolidation, the locus of long-term memory storage, the concepts of recollection and familiarity, and the question of how different medial temporal lobe structures may contribute differently to memory functions.
The theoretical cognitive process of visualization for science education

The use of visual models such as pictures, diagrams and animations in science education is increasing. This is because of the complex nature associated with the concepts in the field. Students, especially entrant students, often report misconceptions and learning difficulties associated with various concepts especially those that exist at a microscopic level, such as DNA, the gene and meiosis as well as those that exist in relatively large time scales such as evolution. However the role of visual literacy in the construction of knowledge in science education has not been investigated much. This article explores the theoretical process of visualization answering the question "how can visual literacy be understood based on the theoretical cognitive process of visualization in order to inform the understanding, teaching and studying of visual literacy in science education?" Based on various theories on cognitive processes during learning for science and general education the author argues that the theoretical process of visualization consists of three stages, namely, Internalization of Visual Models, Conceptualization of Visual Models and Externalization of Visual Models. The application of this theoretical cognitive process of visualization and the stages of visualization in science education are discussed.
New Multibase Non-Adjacent Form Scalar Multiplication and its Application to Elliptic Curve Cryptosystems (extended version)

In this paper we present a new method for scalar multiplication that uses a generic multibase representation to reduce the number of required operations. Further, a multibase NAF-like algorithm that efficiently converts numbers to such representation without impacting memory or speed performance is developed and showed to be sublinear in terms of the number of nonzero terms. Additional representation reductions are discussed with the introduction of window-based variants that use an extended set of precomputations. To realize the proposed multibase scalar multiplication with or without precomputations in the setting of Elliptic Curve Cryptosystems (ECC) over prime fields, we also present a methodology to derive fast composite operations such as tripling or quintupling of a point that require less memory than previous point formulae. Point operations are then protected against simple side-channel attacks using a highly efficient atomic structure. Extensive testing is carried out to show that our multibase scalar multiplication is the fastest method to date in the setting of ECC and exhibits a small footprint, which makes it ideal for implementation on constrained devices.
The cost of email interruption

The use of email by employees at the Danwood Group was studied and it was found that the interrupt effect from emails is more than generally believed. Employees allowed themselves to be interrupted almost as frequently as telephone calls and the common reaction to the arrival of an email is to react almost as quickly as they would respond to telephone calls. This means the interrupt effect is comparable with that of a telephone call. The recovery time from an email interruption was found to be significantly less than the published recovery time for telephone calls. It is to be concluded, therefore, that while Email is still less disruptive than the telephone, the way the majority of users handle their incoming email has been shown to give far more interruption than expected. By analysing the data captured the authors have been able to create recommendations for a set of guidelines for email usage within the workplace that will increase employee efficiency by reducing the prominence of interruptions, restricting the use of email-to-all messages, setting-up the email application to display three lines of the email and to check for email less frequently. It is recommended that training should be given to staff on how to use email more effectively to increase employee productivity.