nils-herrmann/clean_scirepeval huggingface dataset:
A Generative Model of People in Clothing

We present the first image-based generative model of people in clothing for the full body. We sidestep the commonly used complex graphics rendering pipeline and the need for high-quality 3D scans of dressed people. Instead, we learn generative models from a large image database. The main challenge is to cope with the high variance in human pose, shape and appearance. For this reason, pure image-based approaches have not been considered so far. We show that this challenge can be overcome by splitting the generating process in two parts. First, we learn to generate a semantic segmentation of the body and clothing. Second, we learn a conditional model on the resulting segments that creates realistic images. The full model is differentiable and can be conditioned on pose, shape or color. The result are samples of people in different clothing items and styles. The proposed model can generate entirely new people with realistic clothing. In several experiments we present encouraging results that suggest an entirely data-driven approach to people generation is possible.
Flux-Switching Motors for Automotive Applications

This paper presents a new class of brushless motor as an alternative to the permanent-magnet brushed motor used in the automotive industry. The new flux-switching motor is a very simple motor to manufacture, and, coupled with a powerelectronic controller requiring only two power semiconductor switches, it has the potential to be extremely low cost in high volume applications. The design of the motor and its simulation using coupled circuit and time-stepping finite-element analysis is presented. Tested performance has matched well with design targets and with simulation and is delivering performance comparable to a permanent-magnet brushed motor. The new motor and drive are very robust, requiring only two power MOSFETs and a simple microcontroller to create an extremely low-cost variable-speed drive for automotive applications
Automatic irrigation system using soil moisture sensor and temperature sensor with microcontroller AT89S52

Food production techniques have to be improved because of rapid demand in food. Since India has agriculture as the main source of production, proper irrigation schemes are to be employed for an efficient outcome. This design automates the irrigation process thereby reducing the manual intervention and the water losses. It is more helpful in the places where water scarcity is seen more. It consists of 2 sensors which takes the values of temperature of surroundings and moisture level of soil. Output of these sensors are given to ADC and then to microcontroller. Microcontroller compares the values with the threshold values and drives the relay which controls the motor. LCD display is used to display data in the field. GSM modem is also a part of design which helps in transmitting SMS to farmer's phone number which contains the status of motor in field. The design is cost effective and also affordable.
Some Aspects of Stability in Microgrids

This paper investigates some aspects of stability in microgrids. There are different types of microgrid applications. The system structure and the control topology vary depending on the application and so does the aspect of stability in a microgrid. This paper briefly encompasses the stability aspects of remote, utility connected and facility microgrids depending on the modes of operation, control topology, types of micro sources and network parameters. The small signal, transient and the voltage stability aspects in each type of the microgrid are discussed along with scope of improvements. With a brief review of the existing microgrid control methods in the literature and different industry solutions, this paper sets up an initial platform for different types of microgrids stability assessment. Various generalized stability improvement methods are demonstrated for different types of microgrids. The conventional stability study of microgrids presented in this paper facilitates an organized way to plan the micro source operation, microgrid controller design, islanding procedure, frequency control and the load shedding criteria. The stability investigations are presented with different control methods, eigen value analysis and time domain simulations to justify different claims.
Learning Profiles in Duplicate Question Detection

This paper presents the results of systematic and comparative experimentation with major types of methodologies for automatic duplicate question detection when these are applied to datasets of progressively larger sizes, thus allowing to study the learning profiles of this task under these different approaches and evaluate their merits. This study was made possible by resorting to the recent release for research purposes, by the Quora online question answering engine, of a new dataset with over 400,000 pairs labeled with respect to their elements being duplicate interrogative segments.
Mobile Robot Localization Using the Phase of Passive UHF RFID Signals

This paper presents a global localization system for an indoor autonomous vehicle equipped with odometry sensors and a radio-frequency identification (RFID) reader to interrogate tags located on the ceiling of the environment. The RFID reader can measure the phase of the signals coming from responding tags. This phase has non-univocal dependence on the distance robot tag, but in the considered frequency, it is really sensitive to a change in the position of the robot. For this reason, a multihypothesis Kalman filtering approach provides a really satisfactory performance even in the case that a very small density of tags is used: In the experimental tests, an average position estimation error of about 4 cm is achieved using only two tags for an area of about 5 m2.
Web Design Attributes in Building User Trust, Satisfaction, and Loyalty for a High Uncertainty Avoidance Culture

In this study, we attempt to evaluate the user preferences for web design attributes (i.e., typography, color, content quality, interactivity, and navigation) to determine the trust, satisfaction, and loyalty for uncertainty avoidance cultures. Content quality and navigation have been observed as strong factors in building user trust with e-commerce websites. In contrast, interactivity, color, and typography have been observed as strong determinants of user satisfaction. The most relevant and interesting finding is related to typography, which has been rarely discussed in e-commerce literature. A questionnaire was designed to collect data to corroborate the proposed model and hypotheses. Furthermore, the partial least-squares method was adopted to analyze the collected data from the students who participated in the test ($n$ = 558). Finally, the results of this study provide strong support to the proposed model and hypotheses. Therefore, all the web design attributes were observed as important design features to develop user trust and satisfaction for uncertainty avoidance cultures. Although both factors seem to be relevant, the relationship between trust and loyalty was observed to be stronger than between satisfaction and loyalty; thus, trust seems to be a stronger determinant of loyalty for risk/high uncertainty avoidance cultures.
Ultrafast photonic reinforcement learning based on laser chaos

Reinforcement learning involves decision making in dynamic and uncertain environments and constitutes an important element of artificial intelligence (AI). In this work, we experimentally demonstrate that the ultrafast chaotic oscillatory dynamics of lasers efficiently solve the multi-armed bandit problem (MAB), which requires decision making concerning a class of difficult trade-offs called the exploration–exploitation dilemma. To solve the MAB, a certain degree of randomness is required for exploration purposes. However, pseudorandom numbers generated using conventional electronic circuitry encounter severe limitations in terms of their data rate and the quality of randomness due to their algorithmic foundations. We generate laser chaos signals using a semiconductor laser sampled at a maximum rate of 100 GSample/s, and combine it with a simple decision-making principle called tug of war with a variable threshold, to ensure ultrafast, adaptive, and accurate decision making at a maximum adaptation speed of 1 GHz. We found that decision-making performance was maximized with an optimal sampling interval, and we highlight the exact coincidence between the negative autocorrelation inherent in laser chaos and decision-making performance. This study paves the way for a new realm of ultrafast photonics in the age of AI, where the ultrahigh bandwidth of light wave can provide new value.
Side-channel leakage aware instruction scheduling

Speed-optimized side-channel protected software implementations of block ciphers are important for the security of embedded IoT devices based on general-purpose microcontrollers. The recent work of Schwabe et al. published at SAC 2016 introduced a bit-sliced implementation of AES and a first-order Boolean-masked version of it, targeting ARM Cortex-M CPU cores. The authors claim to be secure against timing as well as first-order power and electromagnetic side-channel attacks. However, the author's security claims are not taking the actual leakage characteristics of the underlying CPU architecture into account, hence making the scheme potentially vulnerable to first-order attacks in practice. In this work we show indeed that such a masking scheme can be attacked very easily by first-order electromagnetic side-channel attacks. In order to fix the issue and provide practical first-order security, we provide a strategy to schedule program instructions in way that the specific leakage of the CPU does not impair the side-channel countermeasure.
Adversarial Geometry and Lighting using a Differentiable Renderer

Many machine learning image classifiers are vulnerable to adversarial attacks, inputs with perturbations designed to intentionally trigger misclassification. Current adversarial methods directly alter pixel colors and evaluate against pixel norm-balls: pixel perturbations smaller than a specified magnitude, according to a measurement norm. This evaluation, however, has limited practical utility since perturbations in the pixel space do not correspond to underlying real-world phenomena of image formation that lead to them and has no security motivation attached. Pixels in natural images are measurements of light that has interacted with the geometry of a physical scene. As such, we propose a novel evaluation measure, parametric normballs, by directly perturbing physical parameters that underly image formation. One enabling contribution we present is a physically-based differentiable renderer that allows us to propagate pixel gradients to the parametric space of lighting and geometry. Our approach enables physically-based adversarial attacks, and our differentiable renderer leverages models from the interactive rendering literature to balance the performance and accuracy trade-offs necessary for a memory-efficient and scalable adversarial data augmentation workflow.
Ultra-low-Voltage high-performance CMOS VCOs using transformer feedback

A transformer-feedback voltage-controlled oscillator (TF-VCO) is proposed to achieve low-phase-noise and low-power designs even at a supply below the threshold voltage. The advantages of the proposed TF-VCO are described together with its detailed analysis and its cyclo-stationary characteristic. Two prototypes using the proposed TF-VCO techniques are demonstrated in a standard 0.18-/spl mu/m CMOS process. The first design using two single-ended transformers is operated at 1.4 GHz at a 0.35-V supply using PMOS transistors whose threshold voltage is around 0.52 V. The power consumption is 1.46 mW while the measured phase noise is -128.6 dBc/Hz at 1-MHz frequency offset. Using an optimum differential transformer to maximize quality factor and to minimize the chip area, the second design is operated at 3.8 GHz at a 0.5-V supply with power consumption of 570 /spl mu/W and a measured phase noise of -119 dBc/Hz at 1-MHz frequency offset. The figures of merits are comparable or better to that of other state-of-the-art VCO designs operating at much higher supply voltage.
Metamaterial advances for radar and communications

Metamaterial antennas have progressed considerably in the last few years. Kymeta expects to have its Ku-band metamaterial antenna commercially available in 2017. It is intended for use on vessels anywhere in the world for high speed internet using satellites. Echodyne and PARC, a Xerox Co., have developed a metamaterial array for radar and cell phone usage. The Army Research Laboratory in Adelphi MD has funded the development of a metamaterial 250–505 MHZ antenna with a λ/20 thickness. Complementing this a conventional tightly coupled dipole antenna (TCDA) has been developed which provides a 20∶1 bandwidth with a λ/40 thickness. Target cloaking has been demonstrated at microwaves using metamaterials. With cloaking the electromagnetic wave signal transmitted by a radar goes around the target making it invisible. It has also been demonstrated at L-band with a 50% bandwidth using fractals. Stealthing by absorption using a thin flexible and stretchable metamaterials sheet has been demonstrated to give 6 dB absorption over the band from 8–10 GHz, larger absorption over a narrower band. Using fractals material < 1 mm thick simulation gave a 10 dB reduction in backscatter from 2 to 20 GHz, ∼20 dB reduction from 9 to 15 GHz. Good results were obtained for large incidence angles and both polarizations. Using metamaterial one can focus 6× beyond diffraction limit at 0.38 µm (Moore's Law marches on); 40× diffraction limit, λ/80, at 375 MHz. Has been used in cell phones to provide antennas 5× smaller (1/10th λ) having 700 MHz–2.7 GHz bandwidth. Provided isolation between antennas having 2.5 cm separation equivalent to lm separation. Used for phased array wide angle impedance matching (WAIM). It has been found that n-doped graphene has a negative index of refraction.
Nanoparticle therapeutics: an emerging treatment modality for cancer

Nanoparticles — particles in the size range 1–100 nm — are emerging as a class of therapeutics for cancer. Early clinical results suggest that nanoparticle therapeutics can show enhanced efficacy, while simultaneously reducing side effects, owing to properties such as more targeted localization in tumours and active cellular uptake. Here, we highlight the features of nanoparticle therapeutics that distinguish them from previous anticancer therapies, and describe how these features provide the potential for therapeutic effects that are not achievable with other modalities. While large numbers of preclinical studies have been published, the emphasis here is placed on preclinical and clinical studies that are likely to affect clinical investigations and their implications for advancing the treatment of patients with cancer.
Single image haze removal with WLS-based edge-preserving smoothing filter

Images captured under hazy conditions have low contrast and poor color. This is primarily due to air-light which degrades image quality according to the transmission map. The approach to enhance these hazy images we introduce here is based on the `Dark-Channel Prior' method with image refinement by the `Weighted Least Square' based edge-preserving smoothing. Local contrast is further enhanced by multi-scale tone manipulation. The proposed method improves the contrast, color and detail for the entire image domain effectively. In the experiment, we compare the proposed method with conventional methods to validate performance.
DroidCat: Effective Android Malware Detection and Categorization via App-Level Profiling

Most existing Android malware detection and categorization techniques are static approaches, which suffer from evasion attacks, such as obfuscation. By analyzing program behaviors, dynamic approaches are potentially more resilient against these attacks. Yet existing dynamic approaches mostly rely on characterizing system calls which are subject to system-call obfuscation. This paper presents DroidCat, a novel dynamic app classification technique, to complement existing approaches. By using a diverse set of dynamic features based on method calls and inter-component communication (ICC) Intents without involving permission, app resources, or system calls while fully handling reflection, DroidCat achieves superior robustness than static approaches as well as dynamic approaches relying on system calls. The features were distilled from a behavioral characterization study of benign versus malicious apps. Through three complementary evaluation studies with 34 343 apps from various sources and spanning the past nine years, we demonstrated the stability of DroidCat in achieving high classification performance and superior accuracy compared with the two state-of-the-art peer techniques that represent both static and dynamic approaches. Overall, DroidCat achieved 97% F1-measure accuracy consistently for classifying apps evolving over the nine years, detecting or categorizing malware, 16%–27% higher than any of the two baselines compared. Furthermore, our experiments with obfuscated benchmarks confirmed higher robustness of DroidCat over these baseline techniques. We also investigated the effects of various design decisions on DroidCat’s effectiveness and the most important features for our dynamic classification. We found that features capturing app execution structure such as the distribution of method calls over user code and libraries are much more important than typical security features such as sensitive flows.
Vaping on Instagram: cloud chasing, hand checks and product placement.

This study documented images posted on Instagram of electronic cigarettes (e-cigarette) and vaping (activity associated with e-cigarette use). Although e-cigarettes have been studied on Twitter, few studies have focused on Instagram, despite having 500 million users. Instagram's emphasis on images warranted investigation of e-cigarettes, as past tobacco industry strategies demonstrated that images could be used to mislead in advertisements, or normalise tobacco-related behaviours. Findings should prove informative to tobacco control policies in the future.



3 months of publicly available data were collected from Instagram, including images and associated metadata (n=2208). Themes of images were classified as (1) activity, for example, a person blowing vapour; (2) product, for example, a personal photo of an e-cigarette device; (3) advertisement; (4) text, for example, 'meme' or image containing mostly text and (5) other. User endorsement (likes) of each type of image was recorded. Caption text was analysed to explore different trends in vaping and e-cigarette-related text.



Analyses found that advertisement-themed images were most common (29%), followed by product (28%), and activity (18%). Likes were more likely to accompany activity and product-themed images compared with advertisement or text-themed images (p<0.01). Vaping-related text greatly outnumbered e-cigarette-related text in the image captions.



Instagram affords its users the ability to post images of e-cigarette-related behaviours and gives advertisers the opportunity to display their product. Future research should incorporate novel data streams to improve public health surveillance, survey development and educational campaigns.
Hash Function Based on Block Ciphers

Iterated hash functions based on block ciphers are treated. Five attacks on an iterated hash function and on its round function are formulated. The wisdom of strengthening such hash functions by constraining the last block of the message to be hashed is stressed. Schemes for constructing m-bit and 2m-bit hash round functions fromm-bit block ciphers are studied. A principle is formalized for evaluating the strength of hash round functions, viz., that applying computationally simple (in both directions) invertible transformations to the input and output of a hash round function yields a new hash round function with the same security. By applying this principle, four attacks on three previously proposed 2m-bit hash round functions are formulated. Finally, three new hash round functions based on an m-bit block cipher with a 2m-bit key are proposed.
Influence of Specific AR Capabilities on the Learning Effectiveness and Efficiency

Augmented reality (AR) is a promising technology for improving the applicative and comprehension skills of students. The ARiSE project developed an Augmented Reality Teaching Platform (ARTP) for secondary schools. A Chemistry learning scenario was implemented that is based on the interaction paradigm “building with guidance”. This study aims at assessing the extent to which specific capabilities of the ARTP support the understanding of Chemistry concepts as well as their contribution to the perceived utility. The results of a multiple-regression analysis show that the specific features of the Chemistry scenario enable students to better understand the subject matter with less effort in learning. Overall, the interaction paradigm proved to have a positive influence on the effectiveness and efficiency of the learning process.
An open-label study to evaluate sildenafil for the treatment of lymphatic malformations.

Lymphatic malformations can be challenging to treat. Mainstay interventions including surgery and sclerotherapy are invasive and can result in local recurrence and complications.



We sought to assess the effect of 20 weeks of oral sildenafil on reducing lymphatic malformation volume and symptoms in children.



Seven children (4 boys, 3 girls; ages 13-85 months) with lymphatic malformations were given oral sildenafil for 20 weeks in this open-label study. The volume of the lymphatic malformation was calculated blindly using magnetic resonance imaging performed before and after 20 weeks of sildenafil. Lymphatic malformations were assessed clinically on weeks 4, 12, 20, and 32. Both the physician and parents evaluated the lymphatic malformation in comparison with baseline.



Four subjects had a lymphatic malformation volume decrease (1.0%-31.7%). In 2 subjects, despite a lymphatic malformation volume increase (1.1%-3.7%), clinical improvement was noted while on sildenafil. One subject had a 29.6% increase in lymphatic malformation volume and no therapeutic response. Lymphatic malformations of all 6 subjects who experienced a therapeutic response on sildenafil softened and became easily compressible. Adverse events were minimal.



A randomized controlled trial will be necessary to verify the effects of sildenafil on lymphatic malformations.



Sildenafil can reduce lymphatic malformation volume and symptoms in some children.