nils-herrmann/clean_scirepeval huggingface dataset:
Advertising influences on young children's food choices and parental influence.

To evaluate whether advertising for food influences choices made by children, the strength of these influences, and whether they might be easily undone by parental influences.



Children between 3 and 8 years of age (n=75) were randomized to watch a series of programs with embedded commercials. Some children watched a commercial for a relatively healthy food item, the other children watched a commercial for a less healthy item, both from the same fast-food company. Children were also randomized either to receive parental encouragement to choose the healthy item or to choose whichever item they preferred.



Results indicated that children were more likely to choose the advertised item, despite parental input. Parental input only slightly moderated this influence.



Although advertising impact on children's food choices is moderate in size, it appears resilient to parental efforts to intervene. Food advertisements directed at children may have a small but meaningful effect on their healthy food choices.
Proportions of Hand Segments

Such investigation methods as thermography or isotope scanning allow visualizing inflammation, neoplastic and some other pathological processes, and their location in the body. But the knowledge of regional topography is necessary for exact localization of the process. Such external anatomical landmarks of the hand as bone prominences and creases help to localize its joints and other structures during the clinical examination. On X-ray pictures, MRIs and CT images or during ultrasonography, anatomical structures are well visualized and their localization is not a problem. However, thermography and isotope scanning allow us to see only the shape and outlines of the anatomical region. So the identification of anatomical structures, particularly hand joints, on thermograms and bone scans is often difficult.
Solving the Shortest Vector Problem in 2n Time Using Discrete Gaussian Sampling: Extended Abstract

We give a randomized 2n+o(n)-time and space algorithm for solving the Shortest Vector Problem (SVP) on n-dimensional Euclidean lattices. This improves on the previous fastest algorithm: the deterministic ~O(4n)-time and ~O(2n)-space algorithm of Micciancio and Voulgaris . In fact, we give a conceptually simple algorithm that solves the (in our opinion, even more interesting) problem of discrete Gaussian sampling (DGS). More specifically, we show how to sample 2n/2 vectors from the discrete Gaussian distribution at any parameter in 2n+o(n) time and space. (Prior work only solved DGS for very large parameters.) Our SVP result then follows from a natural reduction from SVP to DGS.
 In addition, we give a more refined algorithm for DGS above the so-called smoothing parameter of the lattice, which can generate 2n/2 discrete Gaussian samples in just 2n/2+o(n) time and space. Among other things, this implies a 2n/2+o(n)-time and space algorithm for 1.93-approximate decision SVP.
LEOPARD: Lightweight Encryption Operation Permutation Addition Rotation and Diffusion

Tactical unmanned vehicles are commonly used to conduct tasks (e.g. monitor and surveillance) in various civilian applications from a remote location. The characteristic of the wireless communication link allows attackers to monitor and manipulate the operation of the unmanned vehicle through passive and active attacks. Cryptography is selected as a counter measure to mitigate these threats; however, a drawback of using cryptography is the impact on the energy consumed by the unmanned vehicle as energy is often constrained and limits the duration of the mission time. This paper introduces the Lightweight Encryption Operation Permutation Addition Rotation and Diffusion (LEOPARD) cryptographic primitive with a benchmark performance analysis against the standardised Advanced Encryption Standard (AES). Results indicate that LEOPOARD is a feasible encryption approach in comparison to the AES encryption algorithm for unmanned vehicles with an average performance increase of 8%.
Understanding the Complexity of Economic, Ecological, and Social Systems

Hierarchies and adaptive cycles comprise the basis of ecosystems and social-ecological systems across scales. Together they form a panarchy. The panarchy describes how a healthy system can invent and experiment, benefiting from inventions that create opportunity while being kept safe from those that destabilize because of their nature or excessive exuberance. Each level is allowed to operate at its own pace, protected from above by slower, larger levels but invigorated from below by faster, smaller cycles of innovation. The whole panarchy is therefore both creative and conserving. The interactions between cycles in a panarchy combine learning with continuity. An analysis of this process helps to clarify the meaning of “sustainable development.” Sustainability is the capacity to create, test, and maintain adaptive capability. Development is the process of creating, testing, and maintaining opportunity. The phrase that combines the two, “sustainable development,” thus refers to the goal of fostering adaptive capabilities and creating opportunities. It is therefore not an oxymoron but a term that describes a logical partnership.
Illuminating search spaces by mapping elites

Nearly all science and engineering fields use search algorithms, which automatically explore a search space to find high-performing solutions: chemists search through the space of molecules to discover new drugs; engineers search for stronger, cheaper, safer designs, scientists search for models that best explain data, etc. The goal of search algorithms has traditionally been to return the single highestperforming solution in a search space. Here we describe a new, fundamentally different type of algorithm that is more useful because it provides a holistic view of how highperforming solutions are distributed throughout a search space. It creates a map of high-performing solutions at each point in a space defined by dimensions of variation that a user gets to choose. This Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) algorithm illuminates search spaces, allowing researchers to understand how interesting attributes of solutions combine to affect performance, either positively or, equally of interest, negatively. For example, a drug company may wish to understand how performance changes as the size of molecules and their costto-produce vary. MAP-Elites produces a large diversity of high-performing, yet qualitatively different solutions, which can be more helpful than a single, high-performing solution. Interestingly, because MAP-Elites explores more of the search space, it also tends to find a better overall solution than state-of-the-art search algorithms. We demonstrate the benefits of this new algorithm in three different problem domains ranging from producing modular neural networks to designing simulated and real soft robots. Because MAPElites (1) illuminates the relationship between performance and dimensions of interest in solutions, (2) returns a set of high-performing, yet diverse solutions, and (3) improves the state-of-the-art for finding a single, best solution, it will catalyze advances throughout all science and engineering fields.
Anna: A KVS for Any Scale

Modern cloud providers offer dense hardware with multiple cores and large memories, hosted in global platforms. This raises the challenge of implementing high-performance software systems that can effectively scale from a single core to multicore to the globe. Conventional wisdom says that software designed for one scale point needs to be rewritten when scaling up by 10-100X. In contrast, we explore how a system can be architected to scale across many orders of magnitude by design. We explore this challenge in the context of a new key-value store system called Anna: a partitioned, multi-mastered system that achieves high performance and elasticity via wait-free execution and coordination-free consistency. Our design rests on a simple architecture of coordination-free actors that perform state update via merge of lattice-based composite data structures. We demonstrate that a wide variety of consistency models can be elegantly implemented in this architecture with unprecedented consistency, smooth fine-grained elasticity, and performance that far exceeds the state of the art.
Using Neural Networks to Predict Emoji Usage from Twitter Data

As emojis have emerged as an increasingly important and standardized part of modern textual inputs, analyzing their usage patterns are becoming of great interest to any modern text system. We frame this investigation as a text classification problem, mapping input text to their most likely accompanying emoji, utilizing 1.5 million scraped Twitter tweets for our training set. Both our LSTM-RNN model and our CNN model outperform our baseline, although the CNN model surprisingly achieves much better accuracy and F1 results. We conclude the paper by proposing future works in this area.
Relational trauma and the developing right brain: an interface of psychoanalytic self psychology and neuroscience.

Psychoanalysis, the science of unconscious processes, has recently undergone a significant transformation. Self psychology, derived from the work of Heinz Kohut, represents perhaps the most important revision of Freud's theory as it has shifted its basic core concepts from an intrapsychic to a relational unconscious and from a cognitive ego to an emotion-processing self. As a result of a common interest in the essential, rapid, bodily based, affective processes that lie beneath conscious awareness, a productive dialogue is now occurring between psychoanalysis and neuroscience. Here I apply this interdisciplinary perspective to a deeper understanding of the nonconscious brain/mind/body mechanisms that lie at the core of self psychology. I offer a neuropsychoanalytic conception of the development and structuralization of the self, focusing on the experience-dependent maturation of the emotion-processing right brain in infancy. I then articulate an interdisciplinary model of attachment trauma and pathological dissociation, an early forming defense against overwhelming affect that is a cardinal feature of self-psychopathologies. I end with some thoughts on the mechanism of the psychotherapeutic change process and suggest that self psychology is, in essence, a psychology of the unique functions of the right brain and that a rapprochement between psychoanalysis and neuroscience is now at hand.
Automated video looping with progressive dynamism

Given a short video we create a representation that captures a spectrum of looping videos with varying levels of dynamism, ranging from a static image to a highly animated loop. In such a progressively dynamic video, scene liveliness can be adjusted interactively using a slider control. Applications include background images and slideshows, where the desired level of activity may depend on personal taste or mood. The representation also provides a segmentation of the scene into independently looping regions, enabling interactive local adjustment over dynamism. For a landscape scene, this control might correspond to selective animation and deanimation of grass motion, water ripples, and swaying trees. Converting arbitrary video to looping content is a challenging research problem. Unlike prior work, we explore an optimization in which each pixel automatically determines its own looping period. The resulting nested segmentation of static and dynamic scene regions forms an extremely compact representation.
Ubiquitous GPS vehicle tracking and management system

Global Positioning System (GPS) is becoming widely used for tracking and monitoring vehicles. Many systems have been created to provide such services which make them popular and needed more than ever before. In this paper a “GPS vehicle tracking system” is proposed. This system is useful for fleet operators in monitoring driving behavior of employees or parents monitoring their teen drivers. Moreover, this system can be used in theft prevention as a retrieval device in addition of working as a security system combined with car alarms. The main contribution of this paper is providing two types of end user applications, a web application and a mobile application. This way the proposed system provides a ubiquitous vehicle tracking system with maximum accessibility for the user anytime and anywhere. The system's tracking services includes acquiring the location and ground speed of a given vehicle in the current moment or on any previous date. It also monitors the vehicle by setting speed and geographical limits and therefore receiving SMS alerts when the vehicle exceeds these pre-defined limits. Additionally, all the movements and stops of a given vehicle can also be monitored. Tracking vehicles in our system uses a wide range of new technologies and communication networks including General Packet Radio Service (GPRS), Global System for Mobile Communication (GSM), the Internet or the World Wide Web and Global Positioning System (GPS).
A Scalable and Self-Configuring Architecture for Service Discovery in the Internet of Things

The Internet of Things (IoT) aims at connecting billions of devices in an Internet-like structure. This gigantic information exchange enables new opportunities and new forms of interactions among things and people. A crucial enabler of robust applications and easy smart objects' deployment is the availability of mechanisms that minimize (ideally, cancel) the need for external human intervention for configuration and maintenance of deployed objects. These mechanisms must also be scalable, since the number of deployed objects is expected to constantly grow in the next years. In this work, we propose a scalable and self-configuring peer-to-peer (P2P)-based architecture for large-scale IoT networks, aiming at providing automated service and resource discovery mechanisms, which require no human intervention for their configuration. In particular, we focus on both local and global service discovery (SD), showing how the proposed architecture allows the local and global mechanisms to successfully interact, while keeping their mutual independence (from an operational viewpoint). The effectiveness of the proposed architecture is confirmed by experimental results obtained through a real-world deployment.
Sensorless PMSM Drive Based on Stator Feedforward Voltage Estimation Improved With MRAS Multiparameter Estimation

In order to reduce the adverse effect of parameter variation in position sensorless speed control of permanent magnet synchronous motor (PMSM) based on stator feedforward voltage estimation (FFVE), multiparameter estimation using a model reference adaptive system is proposed. Since the FFVE scheme relies on motor parameters, the stator resistance and rotor flux linkage are estimated and continuously updated in the FFVE model in a closed-loop fashion, and the sensitivity to multiparameter changes at low speed is eliminated. To improve the dynamics and stability of the overall system and eliminate transient oscillations in speed estimation, a phase-locked loop like speed estimation method is proposed, which is obtained by passing the q-axis proportional integrator (PI) current regulator output through a first-order filter in the FFVE scheme. The proposed control method is similar to V/f control as in induction motors; therefore, starting from zero speed is possible. The experimental tests are implemented with 1-kW PMSM drive controlled by a TMS320F28335 DSP. The proposed sensorless scheme is also compared with the classical sliding mode observer (SMO). Experimental results show that the proposed sensorless scheme exhibits greater stability at lower speed than the classical SMO under parameter detuning. Experimental results and stability analysis demonstrate the feasibility and effectiveness of the proposed sensorless scheme for PMSM under various load and speed conditions.
Advice weaving in AspectJ

This paper describes the implementation of advice weaving in AspectJ. The AspectJ language picks out dynamic join points in a program's execution with pointcuts and uses advice to change the behavior at those join points. The core task of AspectJ's advice weaver is to statically transform a program so that at runtime it will behave according to the AspeetJ language semantics. This paper describes the 1.1 implementation which is based on transforming bytecode. We describe how AspectJ's join points are mapped to regions of bytecode, how these regions are efficiently matched by AspectJ's pointcuts, and how pieces of advice are efficiently implemented at these regions. We also discuss both run-time and compile-time performance of this implementation.
Analysis, synthesis, and perception of voice quality variations among female and male talkers.

Voice quality variations include a set of voicing sound source modifications ranging from laryngealized to normal to breathy phonation. Analysis of reiterant imitations of two sentences by ten female and six male talkers has shown that the potential acoustic cues to this type of voice quality variation include: (1) increases to the relative amplitude of the fundamental frequency component as open quotient increases; (2) increases to the amount of aspiration noise that replaces higher frequency harmonics as the arytenoids become more separated; (3) increases to lower formant bandwidths; and (4) introduction of extra pole zeros in the vocal-tract transfer function associated with tracheal coupling. Perceptual validation of the relative importance of these cues for signaling a breathy voice quality has been accomplished using a new voicing source model for synthesis of more natural male and female voices. The new formant synthesizer, KLSYN88, is fully documented here. Results of the perception study indicate that, contrary to previous research which emphasizes the importance of increased amplitude of the fundamental component, aspiration noise is perceptually most important. Without its presence, increases to the fundamental component may induce the sensation of nasality in a high-pitched voice. Further results of the acoustic analysis include the observations that: (1) over the course of a sentence, the acoustic manifestations of breathiness vary considerably--tending to increase for unstressed syllables, in utterance-final syllables, and at the margins of voiceless consonants; (2) on average, females are more breathy than males, but there are very large differences between subjects within each gender; (3) many utterances appear to end in a "breathy-laryngealized" type of vibration; and (4) diplophonic irregularities in the timing of glottal periods occur frequently, especially at the end of an utterance. Diplophonia and other deviations from perfect periodicity may be important aspects of naturalness in synthesis.
Implementation of image edge detection on FPGA using XSG

Image edge detection plays a vital role in image processing. Edges are the pre-dominant features of an image which is mainly used to analyze the images and to process that image. Hardware implementation of image edge detection is essential for real time applications to increase the speed of operation. This paper proposes a method that uses a graphical user interface that combines MATLAB, Simulink and Xilinx System Generator(XSG) to generate a code which is hardware implemented onto Spartan-3E Field Programmable Gate Array (FPGA).This Paper also provides an image edge detection using Robert, Prewitt, Sobel and Laplacian of Gaussian (LoG) operators and implemented on FPGA.
Device-free gesture tracking using acoustic signals: demo

In this demo, we present LLAP, a hand tracking system that uses ultrasound to localize the hand of the user to enable device-free gesture inputs. LLAP utilizes speakers and microphones on Commercial-Off-The-Shelf (COTS) mobile devices to play and record sound waves that are inaudible to humans. By measuring the phase of the sound signal reflected by the hands or fingers of the user, we can accurately measure the gesture movements. With a single pair of speaker/microphone, LLAP can track hand movement with accuracy of 3.5 mm. For devices with two microphones, LLAP enables drawing-in-the air capability with tracking accuracy of 4.6 mm. Moreover, the latency for LLAP is smaller than 15 ms for both the Android and the iOS platforms so that LLAP can be used for real-time applications.
Representing Text for Joint Embedding of Text and Knowledge Bases

Models that learn to represent textual and knowledge base relations in the same continuous latent space are able to perform joint inferences among the two kinds of relations and obtain high accuracy on knowledge base completion (Riedel et al., 2013). In this paper we propose a model that captures the compositional structure of textual relations, and jointly optimizes entity, knowledge base, and textual relation representations. The proposed model significantly improves performance over a model that does not share parameters among textual relations with common sub-structure.
MaltParser: A Language-Independent System for Data-Driven Dependency Parsing

One of the potential advantages of data-driven approaches to natural language processing is that they can be ported to new languages, provided that the necessary linguistic data resources are available. In practice, this advantage can be hard to realize if models are overfitted to a particular language or linguistic annotation scheme. Thus, using two state-of-the-art statistical parsers developed for English to parse Italian, Corazza et al.  report an increase in error rate of 15–18%, and similar results have been reported for other languages [5, 1, 9, 14]. To overcome this kind of problem, systems for data-driven processing need to be designed for flexible reconfiguration, not only with respect to the selection of linguistic features but also with respect to algorithms and learning methods. In this paper, we present a data-driven approach to dependency parsing that has been applied to several languages, consistently giving a dependency accuracy of 80–90% without any language-specific enhancements and with fairly modest data resources. The methodology is based on three essential components:
Unsupervised domain adaptation for robust speech recognition via variational autoencoder-based data augmentation

Domain mismatch between training and testing can lead to significant degradation in performance in many machine learning scenarios. Unfortunately, this is not a rare situation for automatic speech recognition deployments in real-world applications. Research on robust speech recognition can be regarded as trying to overcome this domain mismatch issue. In this paper, we address the unsupervised domain adaptation problem for robust speech recognition, where both source and target domain speech are available, but word transcripts are only available for the source domain speech. We present novel augmentation-based methods that transform speech in a way that does not change the transcripts. Specifically, we first train a variational autoencoder on both source and target domain data (without supervision) to learn a latent representation of speech. We then transform nuisance attributes of speech that are irrelevant to recognition by modifying the latent representations, in order to augment labeled training data with additional data whose distribution is more similar to the target domain. The proposed method is evaluated on the CHiME-4 dataset and reduces the absolute word error rate (WER) by as much as 35% compared to the non-adapted baseline.