nils-herrmann/clean_scirepeval huggingface dataset:
Survey on Big Data and Mining Algorithm

An information stream is a requested arrangement of examples that can be perused just once or a little number of times utilizing constrained processing and stockpiling abilities. Numerous applications take a shot at stream information like web pursuit, system activity, phone call and so on. In this application information is persistently changing in view of time. In this paper we will examine future patterns of information digging that are utilized for examination and expectation of big data. We will talk about difficulties while performing mining on big information. Stream information is likewise alluding as constant information. Constant information produced through web, every second a huge number of information created, so how to oversee and dissect this information, we examine in this paper.
Thinking Through Language *

What would it be like to have never learned English, but instead only to know Hopi, Mandarin Chinese, or American Sign Language? Would that change the way you think? Imagine entirely losing your language, as the result of stroke or trauma. You are aphasic, unable to speak or listen, read or write. What would your thoughts now be like? As the most extreme case, imagine having been raised without any language at all, as a wild child. What—if anything—would it be like to be such a person? Could you be smart; could you reminisce about the past, plan the future? There is a common sense set of answers to these questions, one that represents the mainstream in many circles of cognitive science (see Pinker, 1994 for a lucid exposition). Under this view, the language you speak does not affect how you think. Rich, powerful and abstract cognition can take place within minds that, due to injury or deprivation, have no natural language. Even babies know about the kinds and individuals that occupy their world. They just don’t know their names. Before being exposed to words in a language such as English, all humans possess the concepts that these words correspond to, as part of what Jerry Fodor (1975) calls ‘mentalese’ or ‘a language of thought’. Under this view, as Fodor puts it, all language learning is actually second language learning—when a child learns the vocabulary of English, all that happens is that the child learns the mappings from the English words onto the symbols of this prior language of thought. There is another perspective, however, one that is also rooted in common sense, and which is popular across many disciplines. Many linguists and anthropologists claim that the language one learns has a profound influence on how
Blur Robust Optical Flow using Motion Channel

It is hard to estimate optical flow given a realworld video sequence with camera shake and other motion blur. In this paper, we first investigate the blur parameterization for video footage using near linear motion elements. We then combine a commercial 3D pose sensor with an RGB camera, in order to film video footage of interest together with the camera motion. We illustrates that this additional camera motion/trajectory channel can be embedded into a hybrid framework by interleaving an iterative blind deconvolution and warping based optical flow scheme. Our method yields improved accuracy within three other state-of-the-art baselines given our proposed ground truth blurry sequences; and several other realworld sequences filmed by our imaging system.
Cell-free protein synthesis: applications come of age.

Cell-free protein synthesis has emerged as a powerful technology platform to help satisfy the growing demand for simple and efficient protein production. While used for decades as a foundational research tool for understanding transcription and translation, recent advances have made possible cost-effective microscale to manufacturing scale synthesis of complex proteins. Protein yields exceed grams protein produced per liter reaction volume, batch reactions last for multiple hours, costs have been reduced orders of magnitude, and reaction scale has reached the 100-liter milestone. These advances have inspired new applications in the synthesis of protein libraries for functional genomics and structural biology, the production of personalized medicines, and the expression of virus-like particles, among others. In the coming years, cell-free protein synthesis promises new industrial processes where short protein production timelines are crucial as well as innovative approaches to a wide range of applications.
RelNN: A Deep Neural Model for Relational Learning

Statistical relational AI (StarAI) aims at reasoning and learning in noisy domains described in terms of objects and relationships by combining probability with first-order logic. With huge advances in deep learning in the current years, combining deep networks with first-order logic has been the focus of several recent studies. Many of the existing attempts, however, only focus on relations and ignore object properties. The attempts that do consider object properties are limited in terms of modelling power or scalability. In this paper, we develop relational neural networks (RelNNs) by adding hidden layers to relational logistic regression (the relational counterpart of logistic regression). We learn latent properties for objects both directly and through general rules. Back-propagation is used for training these models. A modular, layer-wise architecture facilitates utilizing the techniques developed within deep learning community to our architecture. Initial experiments on eight tasks over three real-world datasets show that RelNNs are promising models for relational learning.
How Music and Instruments Began: A Brief Overview of the Origin and Entire Development of Music, from Its Earliest Stages

Music must first be defined and distinguished from speech, and from animal and bird cries. We discuss the stages of hominid anatomy that permit music to be perceived and created, with the likelihood of both Homo neanderthalensis and Homo sapiens both being capable. The earlier hominid ability to emit sounds of variable pitch with some meaning shows that music at its simplest level must have predated speech. The possibilities of anthropoid motor impulse suggest that rhythm may have preceded melody, though full control of rhythm may well not have come any earlier than the perception of music above. There are four evident purposes for music: dance, ritual, entertainment personal, and communal, and above all social cohesion, again on both personal and communal levels. We then proceed to how instruments began, with a brief survey of the surviving examples from the Mousterian period onward, including the possible Neanderthal evidence and the extent to which they showed “artistic” potential in other fields. We warn that our performance on replicas of surviving instruments may bear little or no resemblance to that of the original players. We continue with how later instruments, strings, and skin-drums began and developed into instruments we know in worldwide cultures today. The sound of music is then discussed, scales and intervals, and the lack of any consistency of consonant tonality around the world. This is followed by iconographic evidence of the instruments of later antiquity into the European Middle Ages, and finally, the history of public performance, again from the possibilities of early humanity into more modern times. This paper draws the ethnomusicological perspective on the entire development of music, instruments, and performance, from the times of H. neanderthalensis and H. sapiens into those of modern musical history, and it is written with the deliberate intention of informing readers who are without special education in music, and providing necessary information for inquiries into the origin of music by cognitive scientists.
The role of social networks in information diffusion

Online social networking technologies enable individuals to simultaneously share information with any number of peers. Quantifying the causal effect of these mediums on the dissemination of information requires not only identification of who influences whom, but also of whether individuals would still propagate information in the absence of social signals about that information. We examine the role of social networks in online information diffusion with a large-scale field experiment that randomizes exposure to signals about friends' information sharing among 253 million subjects in situ. Those who are exposed are significantly more likely to spread information, and do so sooner than those who are not exposed. We further examine the relative role of strong and weak ties in information propagation. We show that, although stronger ties are individually more influential, it is the more abundant weak ties who are responsible for the propagation of novel information. This suggests that weak ties may play a more dominant role in the dissemination of information online than currently believed.
Software Defined Networking Architecture, Security and Energy Efficiency: A Survey

Software-defined networking (SDN) is an emerging paradigm, which breaks the vertical integration in traditional networks to provide the flexibility to program the network through (logical) centralized network control. SDN has the capability to adapt its network parameters on the fly based on its operating environment. The decoupled structure of SDN serves as a solution for managing the network with more flexibility and ease. In SDN, the centralized cost effective architecture provides network visibility which helps to achieve efficient resource utilization and high performance. Due to the increasingly pervasive existence of smart programmable devices in the network, SDN provides security, energy efficiency, and network virtualization for enhancing the overall network performance. We present various security threats that are resolved by SDN and new threats that arise as a result of SDN implementation. The recent security attacks and countermeasures in SDN are also summarized in the form of tables. We also provide a survey on the different strategies that are implemented to achieve energy efficiency and network security through SDN implementation. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts, challenges, and research trends in this area. With this paper, readers can have a more thorough understanding of SDN architecture, different security attacks and countermeasures, and energy efficiency.
A Simple and Effective Approach to the Story Cloze Test

In the Story Cloze Test, a system is presented with a 4-sentence prompt to a story, and must determine which one of two potential endings is the ‘right’ ending to the story. Previous work has shown that ignoring the training set and training a model on the validation set can achieve high accuracy on this task due to stylistic differences between the story endings in the training set and validation and test sets. Following this approach, we present a simpler fully-neural approach to the Story Cloze Test using skip-thought embeddings of the stories in a feed-forward network that achieves close to state-of-the-art performance on this task without any feature engineering. We also find that considering just the last sentence of the prompt instead of the whole prompt yields higher accuracy with our approach.
IoT Forensic: identification and classification of evidence in criminal investigations

The Internet of Things (IoT) is everywhere around us. Smart communicating objects offer the digitalization of lives. Thus, IoT opens new opportunities in criminal investigations such as a protagonist or a witness to the event. Any investigation process involves four phases: firstly the identification of an incident and its evidence, secondly device collection and preservation, thirdly data examination and extraction and then finally data analysis and formalization.
 In recent years, the scientific community sought to develop a common digital framework and methodology adapted to IoT-based infrastructure. However, the difficulty of IoT lies in the heterogeneous nature of the device, lack of standards and the complex architecture. Although digital forensics are considered and adopted in IoT investigations, this work only focuses on collection. Indeed the identification phase is relatively unexplored. It addresses challenges of finding the best evidence and locating hidden devices. So, the traditional method of digital forensics does not fully fit the IoT environment.
 In this paperwork, we investigate the mobility in the context of IoT at the crime scene. This paper discusses the data identification and the classification methodology from IoT to looking for the best evidences. We propose tools and techniques to identify and locate IoT devices. We develop the recent concept of "digital footprint" in the crime area based on frequencies and interactions mapping between devices. We propose technical and data criteria to efficiently select IoT devices. Finally, the paper introduces a generalist classification table as well as the limits of such an approach.
Adolescent brain development

Adolescence starts with puberty and ends when individuals attain an independent role in society. Cognitive neuroscience research in the last two decades has improved our understanding of adolescent brain development. The evidence indicates a prolonged structural maturation of grey matter and white matter tracts supporting higher cognitive functions such as cognitive control and social cognition. These changes are associated with a greater strengthening and separation of brain networks, both in terms of structure and function, as well as improved cognitive skills. Adolescent-specific sub-cortical reactivity to emotions and rewards, contrasted with their developing self-control skills, are thought to account for their greater sensitivity to the socio-affective context. The present review examines these findings and their implications for training interventions and education.
Interpreting Visual Question Answering Models

Deep neural networks have shown striking progress and obtained state-of-the-art results in many AI research fields in the recent years. However, it is often unsatisfying to not know why they predict what they do. In this paper, we address the problem of interpreting Visual Question Answering (VQA) models. Specifically, we are interested in finding what part of the input (pixels in images or words in questions) the VQA model focuses on while answering the question. To tackle this problem, we use two visualization techniques – guided backpropagation and occlusion – to find important words in the question and important regions in the image. We then present qualitative and quantitative analyses of these importance maps.
Concept evolution in sensory integration: a proposed nosology for diagnosis.

and growing area of practice in occupational therapy. Debate and discussion with colleagues have led us to develop a proposed taxonomy reflecting a new classification scheme to enhance diagnostic specificity. The nosology proposed here is rooted in empirical data first published by Ayres (Ayres, 1972b, 1989) that has evolved based on empirical and theoretical information. This new nosology provides a viewpoint for discussion and research. Two sociopolitical trends contribute to the timeliness of the ideas presented. First, a call exists throughout health and developmental services for evidence-based practice (Sackett, Richardson, Rosenberg, & Haynes, 1997). Diagnostic precision is crucial for homogeneity of samples in empirical research, affecting the validity of the research findings. Second, the condition of sensory processing disorders (SPD) has recently been acknowledged outside the occupational therapy profession in three diagnostic classification references: the Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood, Revised (known as the DC: 0–3R) (Zero to Three, 2005), the Diagnostic Manual for Infancy and Early Childhood of the Interdisciplinary Council on Developmental and Learning Disorders (ICDL, 2005), and the Psychodynamic Diagnostic Manual (PDM Task Force, 2006). Both manuals include diagnostic taxonomies with subtypes of SPD suggested by a committee of occupational therapists, who assisted in developing guidelines for diagnostic specificity related to sensory-based disorders (Miller, Cermak, Lane, Anzalone, & Koomar, 2004).
Deep Learning of Representations: Looking Forward

Deep learning research aims at discovering learning algorithms that discover multiple levels of distributed representations, with higher levels representing more abstract concepts. Although the study of deep learning has already led to impressive theoretical results, learning algorithms and breakthrough experiments, several challenges lie ahead. This paper proposes to examine some of these challenges, centering on the questions of scaling deep learning algorithms to much larger models and datasets, reducing optimization difficulties due to ill-conditioning or local minima, designing more efficient and powerful inference and sampling procedures, and learning to disentangle the factors of variation underlying the observed data. It also proposes a few forward-looking research directions aimed at overcoming these
Modeling of MEMS capacitive differential pressure sensor

In this paper, modeling of high sensitive capacitive differential pressure sensor (CDPS) structure was proposed. The-working principle of CDPS was explained. Different types of CDPS structure for aircraft altimeter were designed. Polymide square diaphragm membrane is used to sense the pressure. Simulation was carried out for the range of pressure from 116 mbar to 1100 mbar, which is equivalent to aircraft flying altitude. Analysis was done with simulation results for centre deflection sensitivity and capacitive sensitivity for different models of CDPS structure. From the simulated results an improved CDPS models with bossed diaphragm membrane shows linear characteristics. The deflection sensitivity and capacitive sensitivity are 10.912nm/mbar and 3.025fF/mbar respectively.
The Hidden Agenda User Simulation Model

A key advantage of taking a statistical approach to spoken dialogue systems is the ability to formalise dialogue policy design as a stochastic optimization problem. However, since dialogue policies are learnt by interactively exploring alternative dialogue paths, conventional static dialogue corpora cannot be used directly for training and instead, a user simulator is commonly used. This paper describes a novel statistical user model based on a compact stack-like state representation called a user agenda which allows state transitions to be modeled as sequences of push- and pop-operations and elegantly encodes the dialogue history from a user's point of view. An expectation-maximisation based algorithm is presented which models the observable user output in terms of a sequence of hidden states and thereby allows the model to be trained on a corpus of minimally annotated data. Experimental results with a real-world dialogue system demonstrate that the trained user model can be successfully used to optimise a dialogue policy which outperforms a hand-crafted baseline in terms of task completion rates and user satisfaction scores.
An Analysis of Single-Layer Networks in Unsupervised Feature Learning

A great deal of research has focused on algorithms for learning features from unlabeled data. Indeed, much progress has been made on benchmark datasets like NORB and CIFAR by employing increasingly complex unsupervised learning algorithms and deep models. In this paper, however, we show that several simple factors, such as the number of hidden nodes in the model, may be more important to achieving high performance than the learning algorithm or the depth of the model. Specifically, we will apply several offthe-shelf feature learning algorithms (sparse auto-encoders, sparse RBMs, K-means clustering, and Gaussian mixtures) to CIFAR, NORB, and STL datasets using only singlelayer networks. We then present a detailed analysis of the effect of changes in the model setup: the receptive field size, number of hidden nodes (features), the step-size (“stride”) between extracted features, and the effect of whitening. Our results show that large numbers of hidden nodes and dense feature extraction are critical to achieving high performance—so critical, in fact, that when these parameters are pushed to their limits, we achieve state-of-the-art performance on both CIFAR-10 and NORB using only a single layer of features. More surprisingly, our best performance is based on K-means clustering, which is extremely fast, has no hyperparameters to tune beyond the model structure itself, and is very easy to implement. Despite the simplicity of our system, we achieve accuracy beyond all previously published results on the CIFAR-10 and NORB datasets (79.6% and 97.2% respectively). Appearing in Proceedings of the 14 International Conference on Artificial Intelligence and Statistics (AISTATS) 2011, Fort Lauderdale, FL, USA. Volume 15 of JMLR: W&CP 15.
The Making of a 3D-Printed, Cable-Driven, Single-Model, Lightweight Humanoid Robotic Hand

Dexterity robotic hands can (Cummings, 1996) greatly enhance the functionality of humanoid robots, but the making of such hands with not only human-like appearance but also the capability of performing the natural movement of social robots is a challenging problem. The first challenge is to create the hand’s articulated structure and the second challenge is to actuate it to move like a human hand. A robotic hand for humanoid robot should look and behave human like. At the same time, it also needs to be light and cheap for widely used purposes. We start with studying the biomechanical features of a human hand and propose a simplified mechanical model of robotic hands, which can achieve the important local motions of the hand. Then, we use 3D modeling techniques to create a single interlocked hand model that integrates pin and ball joints to our hand model. Compared to other robotic hands, our design saves the time required for assembling and adjusting, which makes our robotic hand ready-to-use right after the 3D printing is completed. Finally, the actuation of the hand is realized by cables and motors. Based on this approach, we have designed a cost-effective, 3D printable, compact, and lightweight robotic hand. Our robotic hand weighs 150 g, has 15 joints, which are similar to a real human hand, and 6 Degree of Freedom (DOFs). It is actuated by only six small size actuators. The wrist connecting part is also integrated into the hand model and could be customized for different robots such as Nadine robot (Magnenat Thalmann et al., 2017). The compact servo bed can be hidden inside the Nadine robot’s sleeve and the whole robotic hand platform will not cause extra load to her arm as the total weight (150 g robotic hand and 162 g artificial skin) is almost the same as her previous unarticulated robotic hand which is 348 g. The paper also shows our test results with and without silicon artificial hand skin, and on Nadine robot.
Living the American Dream in Finland : The Social Mobility of Inventors

In this paper we merge individual census data, individual patenting data, and individual IQ data from Finnish Defence Force to look at the probability of becoming an innovator and at the returns to invention. On the former, we find that: (i) it is strongly correlated with parental income; (ii) this correlation is greatly decreased when we control for parental education and child IQ. Turning to the returns to invention, we find that: (i) inventing increases the annual wage rate of the inventor by a significant amounts over a prolonged period after the invention; (ii) coworkers in the same firm also benefit from an innovation, the highest returns being earned by senior managers and entrepreneurs in the firm, especially in the long term. Finally, we find that becoming an inventor enhances both, intragenerational and intergenerational income mobility, and that inventors are very likely to make it to top income brackets.