nils-herrmann/clean_scirepeval huggingface dataset:
Lightweight security for mobile commerce transactions

This paper describes a lightweight security mechanism for protecting electronic transactions conducted over the mobile platform. In a typical mobile computing environment, one or more of the transacting parties are based on some wireless handheld devices. Electronic transactions conducted over the mobile platform are gaining popularity and it is widely accepted that mobile computing is a natural extension of the wired Internet computing world. However, security over the mobile platform is more critical due to the open nature of wireless networks. Furthermore, security is more difficult to implement on the mobile platform because of the resource limitation of mobile handheld devices. Therefore, security mechanisms for protecting traditional computer communications need to be revisited so as to ensure that electronic transactions involving mobile devices can be secured and implemented in an effective manner. This research is part of our effort in designing security infrastructure for electronic commerce systems, which extend from the wired to the wireless Internet. A lightweight mechanism was designed to meet the security needs in face of the resource constraints. The proposed mechanism is proven to be practical in real deployment environment. q 2003 Elsevier B.V. All rights reserved.
Adaptive MCMC with Bayesian Optimization

This paper proposes a new randomized strategy for adaptive MCMC using Bayesian optimization. This approach applies to nondifferentiable objective functions and trades off exploration and exploitation to reduce the number of potentially costly objective function evaluations. We demonstrate the strategy in the complex setting of sampling from constrained, discrete and densely connected probabilistic graphical models where, for each variation of the problem, one needs to adjust the parameters of the proposal mechanism automatically to ensure efficient mixing of the Markov chains.
Unsupervised Learning for Large-Scale Fiber Detection and Tracking in Microscopic Material Images

Constructing 3D structures from serial section data is a long standing problem in microscopy. The structure of a fiber reinforced composite material can be reconstructed using a tracking-by-detection model. Tracking-by-detection algorithms rely heavily on detection accuracy, especially the recall performance. The state-of-the-art fiber detection algorithms perform well under ideal conditions, but are not accurate where there are local degradations of image quality, due to contaminants on the material surface and/or defocus blur. Convolutional Neural Networks (CNN) could be used for this problem, but would require a large number of manual annotated fibers, which are not available. We propose an unsupervised learning method to accurately detect fibers on the large scale, that is robust against local degradations of image quality. The proposed method does not require manual annotations, but uses fiber shape/size priors and spatio-temporal consistency in tracking to simulate the supervision in the training of the CNN. Experiments show significant improvements over state-of-the-art fiber detection algorithms together with advanced tracking performance.
Noise Characteristics of MEMS Gyro ’ s Null Drift and Temperature Compensation

Gyroscope is one of the primary sensors for air vehicle navigation and controls. This paper investigates the noise characteristics of microelectromechanical systems (MEMS) gyroscope null drift and temperature compensation. This study mainly focuses on temperature as a long-term error source. An in-house-designed inertial measurement unit (IMU) is used to perform temperature effect testing in the study. The IMU is placed into a temperature control chamber. The chamber temperature is controlled to increase from 25 C to 80 C at approximately 0.8 degrees per minute. After that, the temperature is decreased to -40 C and then returns to 25 C. The null voltage measurements clearly demonstrate the rapidly changing short-term random drift and slowly changing long-term drift due to temperature variations. The characteristics of the short-term random drifts are analyzed and represented in probability density functions. A temperature calibration mechanism is established by using an artificial neural network to compensate the long-term drift. With the temperature calibration, the attitude computation problem due to gyro drifts can be improved significantly.
Physiological responses to interval endurance exercise at different levels of blood flow restriction

We aimed to identify a blood flow restriction (BFR) endurance exercise protocol that would both maximize cardiopulmonary and metabolic strain, and minimize the perception of effort. Twelve healthy males (23 ± 2 years, 75 ± 7 kg) performed five different exercise protocols in randomized order: HI, high-intensity exercise starting at 105% of the incremental peak power (P peak); I-BFR30, intermittent BFR at 30% P peak; C-BFR30, continuous BFR at 30% P peak; CON30, control exercise without BFR at 30% P peak; I-BFR0, intermittent BFR during unloaded exercise. Cardiopulmonary, gastrocnemius oxygenation (StO2), capillary lactate ([La]), and perceived exertion (RPE) were measured. V̇O2, ventilation (V̇ E), heart rate (HR), [La] and RPE were greater in HI than all other protocols. However, muscle StO2 was not different between HI (set1—57.8 ± 5.8; set2—58.1 ± 7.2%) and I-BRF30 (set1—59.4 ± 4.1; set2—60.5 ± 6.6%, p < 0.05). While physiologic responses were mostly similar between I-BFR30 and C-BFR30, [La] was greater in I-BFR30 (4.2 ± 1.1 vs. 2.6 ± 1.1 mmol L−1, p = 0.014) and RPE was less (5.6 ± 2.1 and 7.4 ± 2.6; p = 0.014). I-BFR30 showed similar reduced muscle StO2 compared with HI, and increased blood lactate compared to C-BFR30 exercise. Therefore, this study demonstrate that endurance cycling with intermittent BFR promotes muscle deoxygenation and metabolic strain, which may translate into increased endurance training adaptations while minimizing power output and RPE.
The Effect of Breast Hypertrophy on Patient Posture

One of the reasons women with macromastia chose to undergo a breast reduction is to relieve their complaints of back, neck, and shoulder pain. We hypothesized that changes in posture after surgery may be the reason for the pain relief and that patient posture may correlate with symptomatic macromastia and may serve as an objective measure for complaints. The purpose of our study was to evaluate the effect of reduction mammaplasty on the posture of women with macromastia.



A prospective controlled study at a university medical center. Forty-two patients that underwent breast reduction were studied before surgery and an average of 4.3 years following surgery. Thirty-seven healthy women served as controls. Standardized lateral photos were taken. The inclination angle of the back was measured. Regression analysis was performed for the inclination angle.



Preoperatively, the mean inclination angle was 1.61 degrees ventrally; this diminished postoperatively to 0.72 degrees ventrally. This change was not significant (P-value=0.104). In the control group that angle was 0.28 degrees dorsally. Univariate regression analysis revealed that the inclination was dependent on body mass index (BMI) and having symptomatic macromastia; on multiple regression it was only dependent on BMI.



The inclination angle of the back in breast reduction candidates is significantly different from that of controls; however, this difference is small and probably does not account for the symptoms associated with macromastia. Back inclination should not be used as a surrogate "objective" measure for symptomatic macromastia.
Towards learning-to-learn

In good old-fashioned artificial intelligence (GOFAI), humans specified systems that solved problems. Much of the recent progress in AI has come from replacing human insights by learning. However, learning itself is still usually built by humans – specifically the choice that parameter updates should follow the gradient of a cost function. Yet, in analogy with GOFAI, there is no reason to believe that humans are particularly good at defining such learning systems: we may expect learning itself to be better if we learn it. Recent research in machine learning has started to realize the benefits of that strategy. We should thus expect this to be relevant for neuroscience: how could the correct learning rules be acquired? Indeed, behavioral science has long shown that humans learn-to-learn, which is potentially responsible for their impressive learning abilities. Here we discuss ideas across machine learning, neuroscience, and behavioral science that matter for the principle of learning-to-learn.
Time, rate, and conditioning.

The authors draw together and develop previous timing models for a broad range of conditioning phenomena to reveal their common conceptual foundations: First, conditioning depends on the learning of the temporal intervals between events and the reciprocals of these intervals, the rates of event occurrence. Second, remembered intervals and rates translate into observed behavior through decision processes whose structure is adapted to noise in the decision variables. The noise and the uncertainties consequent on it have both subjective and objective origins. A third feature of these models is their timescale invariance, which the authors argue is a very important property evident in the available experimental data. This conceptual framework is similar to the psychophysical conceptual framework in which contemporary models of sensory processing are rooted. The authors contrast it with the associative conceptual framework.
The Mediating Role of Appearance Comparisons in the Relationship Between Media Usage and Self-Objectification in Young Women

The media’s portrayal of women is often sexually objectifying, and greater exposure to objectifying media is associated with higher levels of self-objectification among young women. One reason why media usage may be associated with selfobjectification is because women may be comparing their appearance to others in the media. The present study examined (a) the relationship between the usage of different media types (online social media [Facebook], Internet, television, music videos, and magazines) and self-objectification among young women, (b) whether appearance comparison tendencies in general mediated any observed relationships, and (c) whether appearance comparisons to specific types of women on Facebook (self, family, close friends, distant peers, and celebrities) mediated any relationship between Facebook usage and self-objectification. Female participants (N 1⁄4 150) aged 17–25 years completed questionnaires about their media usage, appearance comparison tendency in general, appearance comparisons to specific target groups on Facebook, and selfobjectification. Results showed that Facebook usage and magazine usage were positively correlated with self-objectification and that these relationships were mediated by appearance comparisons in general. In addition, the relationship between Facebook usage and self-objectification was mediated by comparisons to one’s peers on Facebook. These findings suggest that appearance comparisons can play an important role in self-objectification among young women.
Pet therapy: an effective strategy to care for the elderly? An experimental study in a nursing home.

There has recently been a growing interest towards patients' affective and emotional needs, especially in relational therapies, which are considered vital as to increase the understanding of those needs and patients' well-being. In particular, we paid attention to those patients who are forced to spend the last phase of their existence in residential facilities, namely elderly people in nursing homes, who often feel marginalized, useless, depressed, unstimulated or unable to communicate. The aim of this study is to verify the effectiveness of pet therapy in improving well-being in the elderly living in a nursing home.



This is a longitudinal study with before and after intervention variables measurement in two groups of patients of a nursing home for elderly people. One group followed an AAI intervention (experimental group) the other one did not (control group). As to perform an assessment of well-being we measured the following dimensions in patients: anxiety (HAM-A), depression (GDS), apathy (AES), loneliness (UCLA), and quality of life (QUALID). Both groups filled the questionnaires as to measure the target variables (time 0). Once finished the scheduled meetings (time 1), all the participants, including the control group, filled the same questionnaires.



In accordance with scientific evidence the results confirmed a significant reduction of the measured variables. Especially for the quality of life, which showed a greater reduction than the other.



The implementation and success of the Pet Therapy could have a great emotional and social impact, bringing relief to patients and their family members, but also to health professionals.
Channel Equalization Using Neural Networks: A Review

Equalization refers to any signal processing technique used at the receiver to combat intersymbol interference in dispersive channels. This paper reviews the applications of artificial neural networks (ANNs) in modeling nonlinear phenomenon of channel equalization. The literature associated with different feedforward neural network (NN) based equalizers like multilayer perceptron, functional-link ANN, radial basis function, and its variants are reviewed. Feedback-based NN architectures like recurrent NN equalizers are described. Training algorithms are compared in terms of convergence time and computational complexity for nonlinear channel models. Finally, some limitation of current research activities and further research direction is provided.
Design of Fuzzy Control Algorithm for Precious Irrigation System in Greenhouse

The precious irrigation is great significance for arid and semiarid area. According to the special environment of greenhouse, a fuzzy control algorithm was proposed to make an optimal irrigation strategy based on the actual measured soil humidity during the whole plant growth process. The fuzzy control system had two inputs (soil humidity error and its rate) and one output (water level difference). In this paper, the fuzzy control algorithm was introduced in detail which included the setting of input and output, the selection of membership function and the setting of fuzzy rules. The fuzzy control system was meaningful to the smart water-saving irrigation in greenhouse.
Ethnic rhinoplasty.

This article examines the surgical techniques of rhinoplasty in relation to aesthetic considerations of various ethnic groups. Rhinoplasty in general is challenging, particularly in the ethnic population. When considering rhinoplasty in ethnic patients one must determine their aesthetic goals, which in many cases might deviate from the so-called norm of the "North European nose." An experienced rhinoplastic surgeon should be able to navigate his or her way through the nuances of the various ethnic subsets. Keeping this in mind and following the established tenets in rhinoplasty, one can expect a pleasing and congruous nose without radically violating ethnicity.
Design for Existential Crisis in the Anthropocene Age

What should be our orientation to the socio-technical as climate predictions worsen; ecological crises and wars escalate mass migration and refugee numbers; right-wing populism sweeps through politics; automation threatens workers' jobs and austerity policies destabilize society? What is to be done when it is not "business as usual" and even broken concepts of progress seem no longer to be progressing? We ask how to design for the common good, focusing on human needs for meaning, fulfillment, dignity and decency, qualities which technology struggles to support but can easily undermine. We juxtapose the design of computing that offers hope with that which offers only distraction, propose four modes to design for (being attentive, critical, different and in it together) and conclude with a plea to avoid tools that encourage a blinkered existence at a time of great uncertainty and change.
MODELING AND IMPLEMENTATION OF CONTROLLER FOR SWITCHED RELUCTANCE MOTOR WITH AC SMALL SIGNAL MODEL

As traditional control schemes, open-loop Hysteresis and closed-loop pulse-width-modulation (PWM) have been used for the switched reluctance motor (SRM) current controller. The Hysteresis controller induces large unpleasant audible noises because it needs to vary the switching frequency to maintain constant Hysteresis current band. In contract, the PWM controller is very quiet but difficult to design proper gains and control bandwidth due to the nonlinear nature of the SRM. In this thesis, the ac small signal modeling technique is proposed for linearization of the SRM model such that a conventional PI controller can be designed accordingly for the PWM current controller. With the linearized SRM model, the duty-cycle to output transfer function can be derived, and the controller can be designed with sufficient stability margins. The proposed PWM controller has been simulated to compare the performance against the conventional Hysteresis controller based system. It was found that through the frequency spectrum analysis, the noise spectra in audible range disappeared with the fixed switching frequency PWM controller, but was pronounced with the conventional Hysteresis controller. A hardware prototype is then implemented with digital signal processor to verify the quiet nature of the PWM controller when running at 20 kHz switching frequency. The experimental results also indicate a stable current loop operation.
ATtention Spanned: Comprehensive Vulnerability Analysis of AT Commands Within the Android Ecosystem

AT commands, originally designed in the early 80s for controlling modems, are still in use in most modern smartphones to support telephony functions. The role of AT commands in these devices has vastly expanded through vendor-specific customizations, yet the extent of their functionality is unclear and poorly documented. In this paper, we systematically retrieve and extract 3,500 AT commands from over 2,000 Android smartphone firmware images across 11 vendors. We methodically test our corpus of AT commands against eight Android devices from four different vendors through their USB interface and characterize the powerful functionality exposed, including the ability to rewrite device firmware, bypass Android security mechanisms, exfiltrate sensitive device information, perform screen unlocks, and inject touch events solely through the use of AT commands. We demonstrate that the AT command interface contains an alarming amount of unconstrained functionality and represents a broad attack surface on Android devices.
Review of Hardware Platforms for Real-Time Simulation of Electric Machines

This paper presents a review of the research status for real-time simulation of electric machines. The machine models considered are the lumped parameter models, including the phase-domain, <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>, and voltage-behind-reactance models, as well as the physics-based models, including the finite-element method and magnetic equivalent circuit models. These models are initially presented along with their relative advantages and disadvantages with respect to modeling fidelity and their computational intensity. A field-programmable gate array, a graphics processing unit, a chip multiprocessor, and computer clusters are the main hardware platforms for real-time simulations. An overview of such hardware platforms is presented and their comparative performances are evaluated with respect to real-time simulation of electric machines and drives on the basis of simulation acceleration, machine types, and modeling methodology.
Stable Matching-Based Selection in Evolutionary Multiobjective Optimization

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem into a set of scalar optimization subproblems and optimizes them in a collaborative manner. Subproblems and solutions are two sets of agents that naturally exist in MOEA/D. The selection of promising solutions for subproblems can be regarded as a matching between subproblems and solutions. Stable matching, proposed in economics, can effectively resolve conflicts of interests among selfish agents in the market. In this paper, we advocate the use of a simple and effective stable matching (STM) model to coordinate the selection process in MOEA/D. In this model, subproblem agents can express their preferences over the solution agents, and vice versa. The stable outcome produced by the STM model matches each subproblem with one single solution, and it tradeoffs convergence and diversity of the evolutionary search. Comprehensive experiments have shown the effectiveness and competitiveness of our MOEA/D algorithm with the STM model. We have also demonstrated that user-preference information can be readily used in our proposed algorithm to find a region that decision makers are interested in.
Audio-Visual Scene Analysis with Self-Supervised Multisensory Features

The thud of a bouncing ball, the onset of speech as lips open — when visual and audio events occur together, it suggests that there might be a common, underlying event that produced both signals. In this paper, we argue that the visual and audio components of a video signal should be modeled jointly using a fused multisensory representation. We propose to learn such a representation in a self-supervised way, by training a neural network to predict whether video frames and audio are temporally aligned. We use this learned representation for three applications: (a) sound source localization, i.e. visualizing the source of sound in a video; (b) audio-visual action recognition; and (c) on/offscreen audio source separation, e.g. removing the off-screen translator’s voice from a foreign official’s speech. Code, models, and video results are available on our webpage: http://andrewowens.com/multisensory.
Multi-criteria decision making on strategic selection of wind farms

With maturity of advanced technologies and urgent requirement for maintaining a healthy environment with reasonable price, China is moving toward a trend of generating electricity from renewable wind resources. How to select a suitable wind farm becomes an important focus for stakeholders. This paper first briefly introduces wind farm and then develops its critical success criteria. A new multi-criteria decision-making (MCDM) model, based on the analytic hierarchy process (AHP) associated with benefits, opportunities, costs and risks (BOCR), is proposed to help select a suitable wind farm project. Multiple factors that affect the success of wind farm operations are analyzed by taking into account experts’ opinions, and a performance ranking of the wind farms is generated.