nils-herrmann/clean_scirepeval huggingface dataset:
DeepEye: Resource Efficient Local Execution of Multiple Deep Vision Models using Wearable Commodity Hardware

Wearable devices with built-in cameras present interesting opportunities for users to capture various aspects of their daily life and are potentially also useful in supporting users with low vision in their everyday tasks. However, state-of-the-art image wearables available in the market are limited to capturing images periodically and do not provide any real-time analysis of the data that might be useful for the wearers. In this paper, we present DeepEye - a match-box sized wearable camera that is capable of running multiple cloud-scale deep learn- ing models locally on the device, thereby enabling rich analysis of the captured images in near real-time without offloading them to the cloud. DeepEye is powered by a commodity wearable processor (Snapdragon 410) which ensures its wearable form factor. The software architecture for DeepEye addresses a key limitation with executing multiple deep learning models on constrained hardware, that is their limited runtime memory. We propose a novel inference software pipeline that targets the local execution of multiple deep vision models (specifically, CNNs) by interleaving the execution of computation-heavy convolutional layers with the loading of memory-heavy fully-connected layers. Beyond this core idea, the execution framework incorporates: a memory caching scheme and a selective use of model compression techniques that further minimizes memory bottlenecks. Through a series of experiments, we show that our execution framework outperforms the baseline approaches significantly in terms of inference latency, memory requirements and energy consumption.
Motivations and Methodologies for Automation of the Compositional Process

Our aim in this paper is to clarify the range of motivations that have inspired the development of computer programs for the composition of music. We consider this to be important since different methodologies are appropriate for different motivations and goals. We argue that a widespread failure to specify the motivations and goals involved has lead to a methodological malaise in music related research. A brief consideration of some of the earliest attempts to produce computational systems for the composition of music leads us to identify four activities involving the development of computer programs which compose music each of which is inspired by different practical or theoretical motivations. These activities are algorithmic composition, the design of compositional tools, the computational modelling of musical styles and the computational modelling of music cognition. We consider these four motivations in turn, illustrating the problems that have arisen from failing to distinguish between them. We propose a terminology that clearly differentiates the activities defined by the four motivations and present methodological suggestions for research in each domain. While it is clearly important for researchers to embrace developments in related disciplines, we argue that research in the four domains will continue to stagnate unless the motivations and aims of research projects are clearly stated and appropriate methodologies are adopted for developing and evaluating systems that compose music.
Child abuse training and knowledge: a national survey of emergency medicine, family medicine, and pediatric residents and program directors.

The objective of this study was to determine the level of knowledge, comfort, and training related to the medical management of child abuse among pediatrics, emergency medicine, and family medicine residents.



Surveys were administered to program directors and third-year residents at 67 residency programs. The resident survey included a 24-item quiz to assess knowledge regarding the medical management of physical and sexual child abuse. Sites were solicited from members of a network of child abuse physicians practicing at institutions with residency programs.



Analyzable surveys were received from 53 program directors and 462 residents. Compared with emergency medicine and family medicine programs, pediatric programs were significantly larger and more likely to have a medical provider specializing in child abuse pediatrics, have faculty primarily responsible for child abuse training, use a written curriculum for child abuse training, and offer an elective rotation in child abuse. Exposure to child abuse training and abused patients was highest for pediatric residents and lowest for family medicine residents. Comfort with managing child abuse cases was lowest among family medicine residents. On the knowledge quiz, pediatric residents significantly outperformed emergency medicine and family medicine residents. Residents with high knowledge scores were significantly more likely to come from larger programs and programs that had a center, provider, or interdisciplinary team that specialized in child abuse pediatrics; had a physician on faculty responsible for child abuse training; used a written curriculum for child abuse training; and had a required rotation in child abuse pediatrics.



By analyzing the relationship between program characteristics and residents' child abuse knowledge, we found that pediatric programs provide far more training and resources for child abuse education than emergency medicine and family medicine programs. As leaders, pediatricians must establish the importance of this topic in the pediatric education of residents of all specialties.
PalmHashing: a novel approach for cancelable biometrics

We propose a novel cancelable biometric approach, known as PalmHashing, to solve the non-revocable biometric proposed method hashes palmprint templates with a set of pseudo-random keys to obtain a unique code called palmhash. The palmhash code can be stored in portable devices such tokens and smartcards for verification. Multiple sets of palmha can be maintained in multiple applications. Thus the privacy and security of the applications can be greatly enhance compromised, revocation can also be achieved via direct replacement of a new set of palmhash code. In addition, PalmHashin offers several advantages over contemporary biometric approaches such as clear separation of the genuine-imposter and zero EER occurrences. In this paper, we outline the implementation details of this method and also highlight its p in security-critical applications.
Security Evaluation of Pattern Classifiers under Attack

Pattern classification systems are commonly used in adversarial applications, like biometric authentication, network intrusion detection, and spam filtering, in which data can be purposely manipulated by humans to undermine their operation. As this adversarial scenario is not taken into account by classical design methods, pattern classification systems may exhibit vulnerabilities,whose exploitation may severely affect their performance, and consequently limit their practical utility. Extending pattern classification theory and design methods to adversarial settings is thus a novel and very relevant research direction, which has not yet been pursued in a systematic way. In this paper, we address one of the main open issues: evaluating at design phase the security of pattern classifiers, namely, the performance degradation under potential attacks they may incur during operation. We propose a framework for empirical evaluation of classifier security that formalizes and generalizes the main ideas proposed in the literature, and give examples of its use in three real applications. Reported results show that security evaluation can provide a more complete understanding of the classifier’s behavior in adversarial environments, and lead to better design choices micans infotech +91 90036 28940 +91 94435 11725 MICANS INFOTECH, NO: 8 , 100 FEET ROAD,PONDICHERRY. WWW.MICANSINFOTECH.COM ; MICANSINFOTECH@GMAIL.COM +91 90036 28940; +91 94435 11725  Projects 100% WORKING CODE + DOCUMENTATION+ EXPLAINATION – BEST PRICE LOW PRICE GUARANTEED
The free-energy principle: a rough guide to the brain?

This article reviews a free-energy formulation that advances Helmholtz's agenda to find principles of brain function based on conservation laws and neuronal energy. It rests on advances in statistical physics, theoretical biology and machine learning to explain a remarkable range of facts about brain structure and function. We could have just scratched the surface of what this formulation offers; for example, it is becoming clear that the Bayesian brain is just one facet of the free-energy principle and that perception is an inevitable consequence of active exchange with the environment. Furthermore, one can see easily how constructs like memory, attention, value, reinforcement and salience might disclose their simple relationships within this framework.
A Nine-Phase 18-Slot 14-Pole Interior Permanent Magnet Machine With Low Space Harmonics for Electric Vehicle Applications

One of the key challenges of utilizing concentrated winding in interior permanent magnet machines (IPMs) is the high rotor eddy current losses in both magnets and rotor iron due to the presence of a large number of lower and higher order space harmonics in the stator magnetomotive force (MMF). These MMF harmonics also result in other undesirable effects, such as localized core saturation, acoustic noise, and vibrations. This paper proposes a nine-phase 18-slot 14-pole IPM machine using the multiple three-phase winding sets to reduce MMF harmonics. All the subharmonics and some of the higher order harmonics are cancelled out, while the advantages of the concentrate windings are retained. The proposed machine exhibits a high efficiency over wide torque and speed ranges. A 10-kW machine prototype is built and tested in generator mode for the experimental validation. The experimental results indicate the effectiveness of the MMF harmonics cancellation in the proposed machine.
Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection

Local descriptors based on the image noise residual have proven extremely effective for a number of forensic applications, like forgery detection and localization. Nonetheless, motivated by promising results in computer vision, the focus of the research community is now shifting on deep learning. In this paper we show that a class of residual-based descriptors can be actually regarded as a simple constrained convolutional neural network (CNN). Then, by relaxing the constraints, and fine-tuning the net on a relatively small training set, we obtain a significant performance improvement with respect to the conventional detector.
The Sleep Disorder Canine Narcolepsy Is Caused by a Mutation in the Hypocretin (Orexin) Receptor 2 Gene

Narcolepsy is a disabling sleep disorder affecting humans and animals. It is characterized by daytime sleepiness, cataplexy, and striking transitions from wakefulness into rapid eye movement (REM) sleep. In this study, we used positional cloning to identify an autosomal recessive mutation responsible for this sleep disorder in a well-established canine model. We have determined that canine narcolepsy is caused by disruption of the hypocretin (orexin) receptor 2 gene (Hcrtr2). This result identifies hypocretins as major sleep-modulating neurotransmitters and opens novel potential therapeutic approaches for narcoleptic patients.
Emotion Recognition from Facial Expressions using Multilevel HMM (

In this paper we have shown emotion recognition through EEG processing. Based on the literature work, we have made the best selection of the techniques available so far. This paper is easy to perceive, understand, and interpret the proposed method and the techniques like signal preprocessing, feature extraction and, signal classification in BCI. Our aim in this paper is to come up with an efficient and reliable emotion recognition system.  Computer-Interface, Signal Processing, Feature Extraction, EEG signals.
Fingerprint Recognition Using Minutia Score Matching

The popular Biometric used to authenticate a person is Fingerprint which is unique and permanent throughout a person’s life. A minutia matching is widely used for fingerprint recognition and can be classified as ridge ending and ridge bifurcation. In this paper we projected Fingerprint Recognition using Minutia Score Matching method (FRMSM). For Fingerprint thinning, the Block Filter is used, which scans the image at the boundary to preserves the quality of the image and extract the minutiae from the thinned image. The false matching ratio is better compared to the existing algorithm.  Recognition, Binarization, Block Filter Method, Matching score and Minutia.
Efficient Computation of the Skyline Cube

Skyline has been proposed as an important operator for multi-criteria decision making, data mining and visualization, and userpreference queries. In this paper, we consider the problem of efficiently computing a Skycube, which consists of skylines of all possible non-empty subsets of a given set of dimensions. While existing skyline computation algorithms can be immediately extended to computing each skyline query independently, such “shared-nothing” algorithms are inefficient. We develop several computation sharing strategies based on effectively identifying the computation dependencies among multiple related skyline queries. Based on these sharing strategies, two novel algorithms, Bottom-Up and Top-Down algorithms, are proposed to compute Skycube efficiently. Finally, our extensive performance evaluations confirm the effectiveness of the sharing strategies. It is shown that new algorithms significantly outperform the näıve ones.
Improving speech recognition performance through gender separation

Speaker attributed variability are undesirable in speaker independent speech recognition systems. The gender of the speaker is one of the influential sources of this variability. Common speech recognition systems tuned to the ensemble statistics over many speakers to compensate the inherent variability of speech signal. In this paper we will separate the datasets based on the gender to build gender dependent hidden Markov model for each word. The gender separation criterion is the average pitch frequency of the speaker. Experimental evaluation shows significant improvement in word recognition accuracy over the gender independent method with a slight increase in the processing computation.
Securing cyberspace: Identifying key actors in hacker communities

As the computer becomes more ubiquitous throughout society, the security of networks and information technologies is a growing concern. Recent research has found hackers making use of social media platforms to form communities where sharing of knowledge and tools that enable cybercriminal activity is common. However, past studies often report only generalized community behaviors and do not scrutinize individual members; in particular, current research has yet to explore the mechanisms in which some hackers become key actors within their communities. Here we explore two major hacker communities from the United States and China in order to identify potential cues for determining key actors. The relationships between various hacker posting behaviors and reputation are observed through the use of ordinary least squares regression. Results suggest that the hackers who contribute to the cognitive advance of their community are generally considered the most reputable and trustworthy among their peers. Conversely, the tenure of hackers and their discussion quality were not significantly correlated with reputation. Results are consistent across both forums, indicating the presence of a common hacker culture that spans multiple geopolitical regions.
A Survey and Tutorial of RFID Anti-Collision Protocols

RFID technologies have revolutionized the asset tracking industry, with applications ranging from automated checkout to monitoring the medication intakes of elderlies. In all these applications, fast, and in some cases energy efficient, tag reading is desirable, especially with increasing tag numbers. In practice, tag reading protocols face many problems. A key one being tag collision, which occurs when multiple tags reply simultaneously to a reader. As a result, an RFID reader experiences low tag reading performance, and wastes valuable energy. Therefore, it is important that RFID application developers are aware of current tag reading protocols. To this end, this paper surveys, classifies, and compares state-of-the-art tag reading protocols. Moreover, it presents research directions for existing and future tag reading protocols.
Theoretical Propositions of Life-Span Developmental Psychology : On the Dynamics Between Growth and Decline

Life-span developmental psychology involves the study of constancy and change in behavior throughout the life course. One aspect of life-span research has been the advancement of a more general, metatheoretical view on the nature of development. The family of theoretical perspectives associated with this metatheoretical view of life-span developmental psychology includes the recognition of multidirectionality in ontogenetic change, consideration of both age-connected and disconnected developmental factors, a focus on the dynamic and continuous interplay between growth (gain) and decline (loss), emphasis on historical embeddedness and other structural contextual factors, and the study of the range of plasticity in development. Application of the family of perspectives associated with life-span developmental psychology is illustrated for the domain of intellectual development. Two recently emerging perspectives of the family of beliefs are given particular attention. The first proposition is methodological and suggests that plasticity can best be studied with a research strategy called testing-the-limits. The second proposition is theoretical and proffers that any developmental change includes the joint occurrence of gain (growth) and loss (decline) in adaptive capacity. To assess the pattern of positive (gains) and negative (losses) consequences resulting from development, it is necessary to know the criterion demands posed by the individual and the environment during the lifelong process of adaptation.
Structured Convolution Matrices for Energy-efficient Deep learning

We derive a relationship between network representation in energy-efficient neuromorphic architectures and block Toplitz convolutional matrices. Inspired by this connection, we develop deep convolutional networks using a family of structured convolutional matrices and achieve state-of-the-art trade-off between energy efficiency and classification accuracy for well-known image recognition tasks. We also put forward a novel method to train binary convolutional networks by utilising an existing connection between noisy-rectified linear units and binary activations.
Roget's Thesaurus and Semantic Similarity

Roget’s Thesaurus has not been sufficiently appreciated in Natural Language Processing. We show that Roget's and WordNet are birds of a feather. In a few typical tests, we compare how the two resources help measure semantic similarity. One of the benchmarks is Miller and Charles’ list of 30 noun pairs to which human judges had assigned similarity measures. We correlate these measures with those computed by several NLP systems. The 30 pairs can be traced back to Rubenstein and Goodenough’s 65 pairs, which we have also studied. Our Roget’sbased system gets correlations of .878 for the smaller and .818 for the larger list of noun pairs; this is quite close to the .885 that Resnik obtained when he employed humans to replicate the Miller and Charles experiment. We further evaluate our measure by using Roget’s and WordNet to answer 80 TOEFL, 50 ESL and 300 Reader’s Digest questions: the correct synonym must be selected amongst a group of four words. Our system gets 78.75%, 82.00% and 74.33% of the questions respectively, better than any published results.
Rediscovering ACL Discoveries Through the Lens of ACL Anthology Network Citing Sentences

The ACL Anthology Network (AAN)1 is a comprehensive manually curated networked database of citations and collaborations in the field of Computational Linguistics. Each citation edge in AAN is associated with one or more citing sentences. A citing sentence is one that appears in a scientific article and contains an explicit reference to another article. In this paper, we shed the light on the usefulness of AAN citing sentences for understanding research trends and summarizing previous discoveries and contributions. We also propose and motivate several different uses and applications of citing sentences.
Risk Aversion and Engagement in the Sharing Economy

The sharing economy is a new online community that has important implications for offline behavior. This study evaluates whether engagement in the sharing economy is associated with an actor’s aversion to risk. Using a web-based survey and a field experiment, we apply an adaptation of Holt and Laury’s (2002) risk lottery game to a representative sample of sharing economy participants. We find that frequency of activity in the sharing economy predicts risk aversion, but only in interaction with satisfaction. While greater satisfaction with sharing economy websites is associated with a decrease in risk aversion, greater frequency of usage is associated with greater risk aversion. This analysis shows the limitations of a static perspective on how risk attitudes relate to participation in the sharing economy.
Entrepreneurship : Productive , Unproductive , and Destructive

The basic hypothesis is that, while the total supply of entrepreneurs varies anlong societies, the productive contribution of the society's entrepreneurial activities varies much more because of their allocation between productive activities such as innovation and largely unproductive activities such as rent seeking or organized crime. This allocation is heavily influenced by the relative payoffs society offers to such activities. This implies that policy can influence the allocation of entrepreneurship more effectively than it can influence its supply. Historical evidence from ancient Rome. early China, and the Middle Ages and Renaissance in Europe is used to investigate the hypotheses.
Motion Prediction Under Multimodality with Conditional Stochastic Networks

Given a visual history, multiple future outcomes for a video scene are equally probable, in other words, the distribution of future outcomes has multiple modes. Multimodality is notoriously hard to handle by standard regressors or classifiers: the former regress to the mean and the latter discretize a continuous high dimensional output space. In this work, we present stochastic neural network architectures that handle such multimodality through stochasticity: future trajectories of objects, body joints or frames are represented as deep, non-linear transformations of random (as opposed to deterministic) variables. Such random variables are sampled from simple Gaussian distributions whose means and variances are parametrized by the output of convolutional encoders over the visual history. We introduce novel convolutional architectures for predicting future body joint trajectories that outperform fully connected alternatives [29]. We introduce stochastic spatial transformers through optical flow warping for predicting future frames, which outperform their deterministic equivalents [17]. Training stochastic networks involves an intractable marginalization over stochastic variables. We compare various training schemes that handle such marginalization through a) straightforward sampling from the prior, b) conditional variational autoencoders [23, 29], and, c) a proposed K-best-sample loss that penalizes the best prediction under a fixed “ prediction budget”. We show experimental results on object trajectory prediction, human body joint trajectory prediction and video prediction under varying future uncertainty, validating quantitatively and qualitatively our architectural choices and training schemes.