nils-herrmann/clean_scirepeval huggingface dataset:
Forwarding-Loop Attacks in Content Delivery Networks

We describe how malicious customers can attack the availability of Content Delivery Networks (CDNs) by creating forwarding loops inside one CDN or across multiple CDNs. Such forwarding loops cause one request to be processed repeatedly or even indefinitely, resulting in undesired resource consumption and potential Denial-of-Service attacks. To evaluate the practicality of such forwarding-loop attacks, we examined 16 popular CDN providers and found all of them are vulnerable to some form of such attacks. While some CDNs appear to be aware of this threat and have adopted specific forwarding-loop detection mechanisms, we discovered that they can all be bypassed with new attack techniques. Although conceptually simple, a comprehensive defense requires collaboration among all CDNs. Given that hurdle, we also discuss other mitigations that individual CDN can implement immediately. At a higher level, our work underscores the hazards that can arise when a networked system provides users with control over forwarding, particularly in a context that lacks a single point of administrative control.
Dynamics of contraceptive use in India: apprehension versus future intention among non-users and traditional method users.

This study examines the reasons for not using any method of contraception as well as reasons for not using modern methods of contraception, and factors associated with the future intention to use different types of contraceptives in India and its selected states, namely Uttar Pradesh, Assam and West Bengal.



Data from the third wave of District Level Household and Facility Survey, 2007-08 were used. Bivariate as well as logistic regression analyses were performed to fulfill the study objective.



Postpartum amenorrhea and breastfeeding practices were reported as the foremost causes for not using any method of contraception. Opposition to use, health concerns and fear of side effects were reported to be major hurdles in the way of using modern methods of contraception. Results from logistic regression suggest considerable variation in explaining the factors associated with future intention to use contraceptives.



Promotion of health education addressing the advantages of contraceptive methods and eliminating apprehension about the use of these methods through effective communication by community level workers is the need of the hour.
RFF : A Robust , FF-Based MDP Planning Algorithm for Generating Policies with Low Probability of Failure

Over the years, researchers have developed many efficient techniques, such as the planners FF (Hoffmann and Nebel 2001), LPG (Gerevini, Saetti, and Serina 2003), SATPLAN (Kautz, Selman, and Hoffmann 2006), SGPLAN (Hsu et al. 2006), and others, for planning in classical (i.e., deterministic) domains. Some of these planning techniques have been adapted for planning under uncertainty and provide some impressive performance results. For example, the FF-REPLAN (Yoon, Fern, and Givan 2007) is a reactive online planning algorithm that has been demonstrated to be very effective for many MDP planning problems. As another example, planning-graph techniques inspired by FF has also been generalized to planning under nondeterminism and partial observability (Hoffmann and Brafman 2005; Bryce, Kambhampati, and Smith 2006). Finally, the conformantplanning approach of (Palacios and Geffner 2006; 2007) describes how to translate any conformant planning problem into a classical problem for a classical planner. Their approach generates a single translation, and can only find conformant solutions. In this paper, we describe our work that investigates a somewhat middle-ground between the previous approaches described above. In particular, we present an MDP planning algorithm, called RFF, for generating offline robust policies in probabilistic domains. Like both of the above approaches, RFF first creates a relaxation of an MDP planning problem by translating it into a deterministic planning problem in which each action corresponds to an effect of a probabilistic action in the original MDP and for every such effect in the original MDP there is a deterministic action, and there are no probabilities, costs, and rewards. In this relaxed planning problem, RFF computes a policy by generating successive execution paths leading to the goal from the initial states by using FF. The policy returned by RFF has a low probability of failing. In our approach, we interpret this not as the probability of reaching a goal but as the probability of causing any replanning during execution. In this work, we use a Monte-Carlo simulation in order to compute the probability that a partial policy would fail during execution (i.e., the probability that the execution of
The influence of age on stroke outcome. The Copenhagen Stroke Study.

This study was undertaken to elucidate whether and how age influences stroke outcome.



This prospective and community-based study comprised 515 consecutive acute stroke patients. Computed tomographic scan was performed in 79% of patients. Activities of daily living (ADL) and neurological status were assessed weekly during hospital stay using the Barthel Index (BI) and the Scandinavian Stroke Scale (SSS), respectively. Information regarding social condition and comorbidity before stroke was also registered. A multiple regression model was used to analyze the independent influence of age on stroke outcome.



Age was not related to the type of stroke lesion or infarct size. However, age independently influenced initial BI (-4 points per 10 years, P < .01), initial SSS (-2 points per 10 years, P = .01), and discharge BI (-3 points per 10 years, P < .01). No independent influence of age was found regarding mortality within 3 months, discharge SSS, length of hospital stay, and discharge placement. ADL improvement was influenced independently by age (-3 points per 10 years, P < .01), whereas age had no influence on neurological improvement or on speed of recovery.



Age independently influences stroke outcome selectively in ADL-related aspects (BI) but not in neurological aspects (SSS), suggesting a poorer compensatory ability in elderly stroke patients. Therefore, rehabilitation of elderly stroke patients should be focused more on ADL and compensation rather than on the recovery of neurological status, and age itself should not be a selection criterion for rehabilitation.
Case Study : MavHome Smart Home

We live in an increasingly connected and automated society. Smart environments embody this trend by linking computers to everyday tasks and settings. Important features of such environments are that they possess a degree of autonomy, adapt themselves to changing conditions, and communicate with humans in a natural way. These systems can be found in offices, airports, hospitals, classrooms, or any other environment. This article discusses automation of our most personal environment: the home. There are several characteristics that are commonly found in smart homes. This type of environment assumes controls and coordinates a network of sensors and devices, relieving the inhabitants of this burden. Interaction with smart homes is in a form that is comfortable to people: speech, gestures, and actions take the place of windows, icons, menus, and pointers. We define a smart home as one that is able to acquire and apply knowledge about its inhabitants and their surroundings in order to adapt to the inhabitants and meet the goals of comfort and efficiency. Designing and implementing smart homes requires a unique breadth of knowledge not limited to a single discipline, but integrates aspects of machine learning, decision making, human-machine interfaces, wireless networking, mobile communications, databases, sensor networks, and pervasive computing. With these capabilities, the home can control many aspects of the environment such as climate, lighting, maintenance, and entertainment. Intelligent automation of these activities can reduce the amount of interaction required by inhabitants and reduce energy consumption and other potential operating costs. The same capabilities can be
Human milk: a source of more life than we imagine.

The presence of bacteria in human milk has been acknowledged since the seventies. For a long time, microbiological analysis of human milk was only performed in case of infections and therefore the presence of non-pathogenic bacteria was yet unknown. During the last decades, the use of more sophisticated culture-dependent and -independent techniques, and the steady development of the -omic approaches are opening up the new concept of the 'milk microbiome', a complex ecosystem with a greater diversity than previously anticipated. In this review, possible mechanisms by which bacteria can reach the mammary gland (contamination versus active migration) are discussed. In addition, the potential roles of human milk for both infant and maternal health are summarised. A better understanding of the link between the milk microbiome and health benefit, the potential factors influencing this relationship and whether or not it can be influenced by nutrition is required to open new avenues in the field of pregnancy and lactation.
Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video

We present two real-time hidden Markov model-based systems for recognizing sentence-level continuous American Sign Language (ASL) using a single camera to track the user’s unadorned hands. The first system observes the user from a desk mounted camera and achieves 92 percent word accuracy. The second system mounts the camera in a cap worn by the user and achieves 98 percent accuracy (97 percent with an unrestricted grammar). Both experiments use a 40-word lexicon.
Zigbee : “ Wireless Control That Simply Works ”

There are many wireless monitoring and control applications for industrial and home markets which require longer battery life, lower data rates and less complexity than available from existing wireless standards. These standards provide higher data rates at the expense of power consumption, application complexity and cost. What these markets need, in many cases, is a standardsbased wireless technology having the performance characteristics that closely meet the requirements for reliability, security, low power and low cost. This standards-based, interoperable wireless technology will address the unique needs of low data rate wireless control and sensor-based networks.
Learning to Transfer

Transfer learning borrows knowledge from a source domain to facilitate learning in a target domain. Two primary issues to be addressed in transfer learning are what and how to transfer. For a pair of domains, adopting different transfer learning algorithms results in different knowledge transferred between them. To discover the optimal transfer learning algorithm that maximally improves the learning performance in the target domain, researchers have to exhaustively explore all existing transfer learning algorithms, which is computationally intractable. As a trade-off, a sub-optimal algorithm is selected, which requires considerable expertise in an adhoc way. Meanwhile, it is widely accepted in educational psychology that human beings improve transfer learning skills of deciding what to transfer through metacognitive reflection on inductive transfer learning practices. Motivated by this, we propose a novel transfer learning framework known as Learning to Transfer (L2T) to automatically determine what and how to transfer are the best by leveraging previous transfer learning experiences. We establish the L2T framework in two stages: 1) we first learn a reflection function encrypting transfer learning skills from experiences; and 2) we infer what and how to transfer for a newly arrived pair of domains by optimizing the reflection function. Extensive experiments demonstrate the L2T’s superiority over several state-of-the-art transfer learning algorithms and its effectiveness on discovering more transferable knowledge.
Swearing as a response to pain.

Although a common pain response, whether swearing alters individuals' experience of pain has not been investigated. This study investigated whether swearing affects cold-pressor pain tolerance (the ability to withstand immersing the hand in icy water), pain perception and heart rate. In a repeated measures design, pain outcomes were assessed in participants asked to repeat a swear word versus a neutral word. In addition, sex differences and the roles of pain catastrophising, fear of pain and trait anxiety were explored. Swearing increased pain tolerance, increased heart rate and decreased perceived pain compared with not swearing. However, swearing did not increase pain tolerance in males with a tendency to catastrophise. The observed pain-lessening (hypoalgesic) effect may occur because swearing induces a fight-or-flight response and nullifies the link between fear of pain and pain perception.
Towards Smart Robots: Rock-Paper-Scissors Gaming versus Human Players

In this project a human robot interaction system was developed in order to let people naturally play rock-paper-scissors games against a smart robotic opponent. The robot does not perform random choices, the system is able to analyze the previous rounds trying to forecast the next move. A Machine Learning algorithm based on Gaussian Mixture Model (GMM) allows us to increase the percentage of robot victories. This is a very important aspect in the natural interaction between human and robot, in fact, people do not like playing against “stupid” machines, while they are stimulated in confronting with a skilled opponent.
Fast Feature Extraction with CNNs with Pooling Layers

In recent years, many publications showed that convolutional neural network based features can have a superior performance to engineered features. However, not much effort was taken so far to extract local features efficiently for a whole image. In this paper, we present an approach to compute patch-based local feature descriptors efficiently in presence of pooling and striding layers for whole images at once. Our approach is generic and can be applied to nearly all existing network architectures. This includes networks for all local feature extraction tasks like camera calibration, Patchmatching, optical flow estimation and stereo matching. In addition, our approach can be applied to other patchbased approaches like sliding window object detection and recognition. We complete our paper with a speed benchmark of popular CNN based feature extraction approaches applied on a whole image, with and without our speedup, and example code (for Torch) that shows how an arbitrary CNN architecture can be easily converted by our approach.
A feature exploration methodology for learning based cuffless blood pressure measurement using photoplethysmography

In this work, we propose a feature exploration method for learning-based cuffless blood pressure measurement. More specifically, to efficiently explore a large feature space from the photoplethysmography signal, we have applied several analytical techniques, including random error elimination, adaptive outlier removal, maximum information coefficient and Pearson's correlation coefficient based feature assessment methods. We evaluate fifty-seven possible feature candidates and propose three separate feature sets with each containing eleven features to predict the systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean blood pressure (MBP), respectively. From our experimental results on a realistic dataset, this work achieves 4.77±7.68, 3.67±5.69 and 3.85±5.87 mmHg prediction accuracy for SBP, DBP and MBP. In summary, using the proposed light-weight features, the proposed predictors can successfully achieve a Grade A in two standards proposed by the American National Standards of the Association for the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS).
Cognitive conflict adaptation in generalized anxiety disorder

Individuals with generalized anxiety disorder (GAD) display poor emotional conflict adaptation, a cognitive control process requiring the adjustment of performance based on previous-trial conflict. It is unclear whether GAD-related conflict adaptation difficulties are present during tasks without emotionally-salient stimuli. We examined conflict adaptation using the N2 component of the event-related potential (ERP) and behavioral responses on a Flanker task from 35 individuals with GAD and 35 controls. Groups did not differ on conflict adaptation accuracy; individuals with GAD also displayed intact RT conflict adaptation. In contrast, individuals with GAD showed decreased amplitude N2 principal component for conflict adaptation. Correlations showed increased anxiety and depressive symptoms were associated with longer RT conflict adaptation effects and lower ERP amplitudes, but not when separated by group. We conclude that individuals with GAD show reduced conflict-related component processes that may be influenced by compensatory activity, even in the absence of emotionally-salient stimuli.
Fabrication and modeling of temperature-controllable artificial muscle actuator

Research about the artificial muscle made of fishing lines or sewing threads, called the twisted and coiled polymer actuator (abbreviated as TCA in this paper) has collected many interests, recently. Since TCA has a specific power surpassing the human skeletal muscle theoretically, it is expected to be a new generation of the artificial muscle actuator. In order that the TCA is utilized as a useful actuator, this paper introduces the fabrication and the modeling of the temperature-controllable TCA. With an embedded micro thermistor, the TCA is able to measure temperature directly, and feedback control is realized. The safe range of the force and temperature for the continuous use of the TCA was identified through experiments, and the closed-loop temperature control is successfully performed without the breakage of TCA.
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Accurate prediction of rainfall distribution in landfalling tropical cyclones (LTCs) is very important to disaster prevention but quite challenging to operational forecasters. This chapter will describe the rainfall distribution in LTCs, including both axisymmetric and asymmetric distributions and their major controlling parameters, such as environmental vertical wind shear, TC intensity and motion, and coastline. In addition to the composite results from many LTC cases, several case studies are also given to illustrate the predominant factors that are key to the asymmetric rainfall distribution in LTCs. Future directions in this area and potential ways to improve the operational forecasts of rainfall distribution in LTCs are also discussed briefly.
Channel Coding Rate in the Finite Blocklength Regime

This paper investigates the maximal channel coding rate achievable at a given blocklength and error probability. For general classes of channels new achievability and converse bounds are given, which are tighter than existing bounds for wide ranges of parameters of interest, and lead to tight approximations of the maximal achievable rate for blocklengths n as short as 100. It is also shown analytically that the maximal rate achievable with error probability ¿ isclosely approximated by C - ¿(V/n) Q-1(¿) where C is the capacity, V is a characteristic of the channel referred to as channel dispersion , and Q is the complementary Gaussian cumulative distribution function.
Non-Computability of Consciousness

Daegene Song Abstract With the great success in simulating many intelligent behaviors using computing devices, there has been an ongoing debate whether all conscious activities are computational processes. In this paper, the answer to this question is shown to be no. A certain phenomenon of consciousness is demonstrated to be fully represented as a computational process using a quantum computer. Based on the computability criterion discussed with Turing machines, the model constructed is shown to necessarily involve a noncomputable element. The concept that this is solely a quantum effect and does not work for a classical case is also discussed.
IJECSE , Volume 2 , Number 1

Malwares are malignant softwares. It is designed t o amage computer systems without the knowledge of the owner using the system. Softwares from reputabl e vendors also contain malicious code that affects the system or leaks informations to remote servers. Malwares incl udes computer viruses, Worms, spyware, dishonest ad -ware, rootkits, Trojans, dialers etc. Malware is one of t he most serious security threats on the Internet to day. In fact, most Internet problems such as spam e-mails and denial o f service attacks have malwareas their underlying c ause. Computers that are compromised with malware are oft en networked together to form botnets and many atta cks re launched using these malicious, attacker controlled n tworks. The paper focuses on various Malware det ction and removal methods. KeywordsMalware, Intruders, Checksum, Digital Immune System , Behavior blocker
A high-power-density, high-speed gate driver for a 10 kV SiC MOSFET module

Several high-voltage silicon carbide (SiC) devices have been demonstrated over the past few years, and the latest-generation devices are showing even faster switching, and greater current densities. However, there are no commercial gate drivers that are suitable for these high-voltage, high-speed devices. Consequently, there has been a great research effort into the development of gate drivers for high-voltage SiC transistors. This work presents the first detailed report on the design and testing of a high-power-density, high-speed, and high-noise-immunity gate drive for a high-current, 10 kV SiC MOSFET module.
Fast geometric consistency test for real time logo detection

In this paper we present a method for logo detection in image collections and streams. The proposed method is based on features, extracted from reference logo images and test images. Extracted features are combined with respect to their similarity in their descriptors' space and afterwards with respect to their geometric consistency on the image plane. The contribution of this paper is a novel method for fast geometric consistency test. Using state of the art fast matching methods, it produces pairs of similar features between the test image and the reference logo image and then examines which pairs are forming a consistent geometry on both the test and the reference logo image. It is noteworthy that the proposed method is scale, rotation and translation invariant. The key advantage of the proposed method is that it exhibits a much lower computational complexity and better performance than the state of the art methods. Experimental results on large scale datasets are presented to support these statements.