nils-herrmann/clean_scirepeval huggingface dataset:
Clarifying Hypotheses by Sketching Data

Discussions between data analysts and colleagues or clients with no statistical background are difficult, as the analyst often has to teach and explain their statistical and domain knowledge. We investigate work practices of data analysts who collaborate with non-experts, and report findings regarding types of analysis, collaboration and availability of data. Based on these, we have created a tool to enhance collaboration between data analysts and their clients in the initial stages of the analytical process. Sketching time series data allows analysts to discuss expectations for later analysis. We propose function composition rather than freehand sketching, in order to structure the analyst-client conversation by independently expressing expected features in the data. We evaluate the usability of our prototype through two small studies, and report on user feedback for future iterations.
Classification of Battlefield Ground Vehicles Using Acoustic Features and Fuzzy Logic Rule-Based Classifiers

In this paper, we demonstrate, through the multicategory classification of battlefield ground vehicles using acoustic features, how it is straightforward to directly exploit the information inherent in a problem to determine the number of rules, and subsequently the architecture, of fuzzy logic rule-based classifiers (FLRBC). We propose three FLRBC architectures, one non-hierarchical and two hierarchical (HFLRBC), conduct experiments to evaluate the performances of these architectures, and compare them to a Bayesian classifier. Our experimental results show that: 1) for each classifier the performance in the adaptive mode that uses simple majority voting is much better than in the non-adaptive mode; 2) all FLRBCs perform substantially better than the Bayesian classifier; 3) interval type-2 (T2) FLRBCs perform better than their competing type-1 (T1) FLRBCs, although sometimes not by much; 4) the interval T2 nonhierarchical and HFLRBC-series architectures perform the best; and 5) all FLRBCs achieve higher than the acceptable 80% classification accuracy
Digital image forensics: a booklet for beginners

Digital visual media represent nowadays one of the principal means for communication. Lately, the reliability of digital visual information has been questioned, due to the ease in counterfeiting both its origin and content. Digital image forensics is a brand new research field which aims at validating the authenticity of images by recovering information about their history. Two main problems are addressed: the identification of the imaging device that captured the image, and the detection of traces of forgeries. Nowadays, thanks to the promising results attained by early studies and to the always growing number of applications, digital image forensics represents an appealing investigation domain for many researchers. This survey is designed for scholars and IT professionals approaching this field, reviewing existing tools and providing a view on the past, the present and the future of digital image forensics.
Dictionary Learning with Iterative Laplacian Regularisation for Unsupervised Person Re-identification

Many existing approaches to person re-identification (Re-ID) are based on supervised learning, which requires hundreds of matching pairs to be labelled for each pair of cameras. This severely limits their scalability for real-world applications. This work aims to overcome this limitation by developing a novel unsupervised Re-ID approach. The approach is based on a new dictionary learning for sparse coding formulation with a graph Laplacian regularisation term whose value is set iteratively. As an unsupervised model, the dictionary learning model is well-suited to the unsupervised task, whilst the regularisation term enables the exploitation of cross-view identity-discriminative information ignored by existing unsupervised Re-ID methods. Importantly this model is also flexible in utilising any labelled data if available. Experiments on two benchmark datasets demonstrate that the proposed approach significantly outperforms the state-of-the-arts.
Aerial-Image Denoising Based on Convolutional Neural Network with Multi-Scale Residual Learning Approach

Aerial images are subject to various types of noise, which restricts the recognition and analysis of images, target monitoring, and search services. At present, deep learning is successful in image recognition. However, traditional convolutional neural networks (CNNs) extract the main features of an image to predict directly and are limited by the requirements of the training sample size (i.e., a small data size is not successful enough). In this paper, using a small sample size, we propose an aerial-image denoising recognition model based on CNNs with a multi-scale residual learning approach. The proposed model has the following three advantages: (1) Instead of directly learning latent clean images, the proposed model learns the noise from noisy images and then subtracts the learned residual from the noisy images to obtain reconstructed (denoised) images; (2) The developed image denoising recognition model is beneficial to small training datasets; (3) We use multi-scale residual learning as a learning approach, and dropout is introduced into the model architecture to force the network to learn to generalize well enough. Our experimental results on aerial-image denoising recognition reveal that the proposed approach is highly superior to the other state-of-the-art methods.
Postnatal depression and infant growth and development in low income countries: a cohort study from Goa, India.

Postnatal depression is a recognised cause of delayed cognitive development in infants in developed countries. Being underweight is common in South Asia.



To determine whether postnatal depression contributes to poor growth and development outcomes in Goa, India.



Cohort study for growth outcomes with nested case-control study for developmental outcomes. A total of 171 babies were weighed and measured at 6-8 weeks following birth. The following measures were used: Edinburgh Postnatal Depression Scale for maternal mood, and sociodemographic and infant health variables. Outcome measures were: weight (<5th centile), length (<5th centile), and Developmental Assessment Scale for Indian Infants scores at six months.



Postnatal depression was a strong, and independent, predictor of low weight and length and was significantly associated with adverse mental development quotient scores.



This study provides evidence for the first time that postnatal depression, a potentially treatable disorder, is a cause of poor growth and development in South Asia.
Deep3: Leveraging three levels of parallelism for efficient Deep Learning

This paper proposes Deep3 an automated platform-aware Deep Learning (DL) framework that brings orders of magnitude performance improvement to DL training and execution. Deep3 is the first to simultaneously leverage three levels of parallelism for performing DL: data, network, and hardware. It uses platform profiling to abstract physical characterizations of the target platform. The core of Deep3 is a new extensible methodology that enables incorporation of platform characteristics into the higher-level data and neural network transformation. We provide accompanying libraries to ensure automated customization and adaptation to different datasets and platforms. Proof-of-concept evaluations demonstrate 10-100 fold physical performance improvement compared to the state-of-the-art DL frameworks, e.g., TensorFlow.
PrivacyGuard: Enforcing Private Data Usage with Blockchain and Attested Execution

In the upcoming evolution of the Internet of Things (IoT), it is anticipated that billions of devices will be connected to the Internet. Many of these devices are capable of collecting information from individual users and their physical surroundings. They are also capable of taking smart actions, which are usually from a backend cloud server in the IoT system. While IoT promises a more connected and smarter world, this pervasive large-scale data collection, storage, sharing, and analysis raise many privacy concerns. In the current IoT ecosystem, IoT service providers have full control of the collected user data. While the original intended use of such data is primarily for smart IoT system and device control, the data is often used for other purposes not explicitly consented to by the users. We propose a novel user privacy protection framework, PrivacyGuard, that aims to empower users with full privacy control of their data. PrivacyGuard framework seamlessly integrates two new technologies, blockchain and trusted execution environment (TEE). By encoding data access policy and usage as smart contracts, PrivacyGuard can allow data owners to control who can have what access to their data, and be able to maintain a trustworthy record of their data usage. Using remote attestation and TEE, PrivacyGuard ensures that data is only used for the intended purposes approved by the data owner. Our approach represents a significant departure from traditional privacy protections which often rely on cryptography and pure software-based secure computation techniques. Addressing the fundamental problem of data usage control, PrivacyGuard will become the cornerstone for free market of private information.
Hexagonal Global Parameterization of Arbitrary Surfaces

This sketch introduces hexagonal global parameterization, a new type of periodic global parameterization that is ideal for tiling surfaces with patterns of six-fold rotational symmetries, i.e., 6-RoSy's [Palacios and Zhang 2007]. Being one of the two most fundamental rotational symmetries that are compatible with translational symmetries in the plane, 6-RoSy's appear in many places in nature, such as honeycombs, insect eyes, corals and crystals, as well as man-made objects such as Islamic patterns [Kaplan and Salesin 2004] and tri-axial weaving [Akleman et al. 2009]. Such symmetries can also provide optimal circle packing, which naturally have applications in architectural design [Schiftner et al. 2009]. A hexagonal global parameterization facilitates all of these applications.  The dual mesh of such a triangulation provides a hexagon-dominant tiling of the surface.
Microcontroller based PWM Inverter for Speed Control of a Three Phase Induction Motor

Three phase induction motor has proven to be an extremely reliable electromechanical energy conversion device for over 100 years. The speed control of induction motor is a crying need for the real world industrial applications. However, there are so many options available for the precise speed control of induction motor except by changing the frequency. Therefore to achieve the goal of speed control of induction motor, there is no alternative of inverters. With the availability of high speed power semiconductor devices, the three phase inverters play the key role for variable speed ac motor drives. In addition to the speed control, the inverter can also provide some unique features, like voltage control, torque control, power factor correction, auto breaking, built in protection system and so forth. In this paper, a three phases PWM inverter using MC3PHAC microcontroller with computer interface is proposed to run a squirrel case induction motor. Some results of the proposed inverter are presented.
Behavioral Game Theory on Online Social Networks : Colonel Blotto is on Facebook

We show how online social networks such as Facebook can be used in Behavioral Game Theory research. We report the deployment of a Facebook application ‘Project Waterloo’ that allows users to play the Colonel Blotto game against their friends and strangers. Unlike conventional studies performed in the laboratory environment, which rely on monetary incentives to attract human subjects to play games, our framework does not use money and instead relies on reputation and entertainment incentives. We describe the Facebook application we created for conducting this experiment, and perform a preliminary analysis of the data collected in the game. We conclude by discussing the advantages of our approach and list some ideas for future work.
Game theory-based opponent modeling in large imperfect-information games

We develop an algorithm for opponent modeling in large extensive-form games of imperfect information. It works by observing the opponent’s action frequencies and building an opponent model by combining information from a precomputed equilibrium strategy with the observations. It then computes and plays a best response to this opponent model; the opponent model and best response are both updated continually in real time. The approach combines gametheoretic reasoning and pure opponent modeling, yielding a hybrid that can effectively exploit opponents after only a small number of interactions. Unlike prior opponent modeling approaches, ours is fundamentally game theoretic and takes advantage of recent algorithms for automated abstraction and equilibrium computation rather than relying on domain-specific prior distributions, historical data, or a handcrafted set of features. Experiments show that our algorithm leads to significantly higher win rates (than an approximateequilibrium strategy) against several opponents in limit Texas Hold’em — the most studied imperfect-information game in computer science — including competitors from recent AAAI computer poker competitions.
Effectiveness of Blog Advertising: Impact of Communicator Expertise, Advertising Intent, and Product Involvement

Blog advertising, which refers to the paid sponsorship of bloggers to review, promote, or sell products in their blog writing, is becoming prevalent. This paper investigates the impact of three critical factors on blog advertising effectiveness: communicator expertise, advertising intent, and product involvement. An experiment with a 2×2×2 factorial design was used to test their interaction effects on advertising effectiveness. The results indicate that, for low-involvement products, there is better advertising effectiveness when low-expertise communicators are explicit about the advertising intent or when high-expertise communicators are implicit about the advertising intent. But for high-involvement products, the results show that when low-expertise communicators are explicit about the advertising intent, the outcome is lesser advertising effectiveness. For such products, advertising effectiveness does not differ when high-expertise communicators are implicit or explicit about the advertising intent. Based on these results, some implications for further research and practice are given.
Natural regulatory T cells in infectious disease

This review discusses the control exerted by natural CD4+ CD25+ regulatory T cells (natural Treg cells) during infectious processes. Natural Treg cells may limit the magnitude of effector responses, which may result in failure to adequately control infection. However, natural Treg cells also help limit collateral tissue damage caused by vigorous antimicrobial immune responses. We describe here various situations in which the balance between natural Treg cells and effector immune functions influences the outcome of infection and discuss how manipulating this equilibrium might be exploited therapeutically.
A novel approach to face recognition under various facial expressions, occlusion and tilt angles

The proposed method on face recognition have been focused on color co-occurrence matrix approach & principal component analysis — applies PCA only on CCM classified Images. Traditionally, to recognize the human face, PCA is performed on the whole facial image database, whereas in the proposed method, Color Co-occurrence is used to extract the certain features of an image with different attribute levels, and PCA is applied only for the classified images to recognize face images under various facial expressions, occlusion and tilt angles. In comparison with the traditional use of PCA, the proposed method gives better recognition accuracy and less computational time for different facial expressions; further, the proposed method is analyzed with different orientations of face angles which reduces the computational load significantly when the image database is large.
To appear in the Proceedings of the Tenth Conference on Computer Generated Forces and Behavioral Representation 1 Multi-Tasking and Cognitive Workload in an ACT-R Model of a Simplified Air Traffic Control Task

We present a human performance model of operator control of a simplified air traffic control task developed under the Agent-based Modeling and Behavior Representation program (AMBR). The model was implemented using the ACT-R architecture of cognition. Using a well-developed cognitive architecture provided a number of benefits: 1 . The reuse of common design patterns, such as unit task decomposition and retrieval/computation dichotomy, greatly simplified and accelerated model development. 2. The model inherited parameter values from previous models and required no fine-tuning of parameters. 3. The architectural learning mechanisms provided automatic learning of situations. The resulting model was quite simple, consisting of only five declarative chunks of information and 36 production rules, while providing a highly accurate model of human performance. The model matched a wide range of performance measures, including amount and type of errors, response latency and choice percentages. Performance variability is a fundamental aspect of human behavior in complex task. The model accounted not only for the average but also for the distribution of performance through the fundamentally stochastic nature of the architecture, amplified by the interaction with the dynamic simulation environment. Although ACT-R is a goal-directed architecture, the model provided a straightforward account of multi-tasking behavior through the use of interruptions triggered by the onset of messages. The model also provided a theory of cognitive workload based on architectural primitives. The port of the model to the High Level Architecture (HLA) was relatively simple and straightforward. Providing a stochastic model of behavior, however, put strong demands on the efficiency of the simulation. The implications of such demands for HLA-enabled human performance models are discussed.
Combining Cloud and sensors in a smart city environment

In the current worldwide ICT scenario, a constantly growing number of ever more powerful devices (smartphones, sensors, household appliances, RFID devices, etc.) join the Internet, significantly impacting the global traffic volume (data sharing, voice, multimedia, etc.) and foreshadowing a world of (more or less) smart devices, or “things” in the Internet of Things (IoT) perspective. Heterogeneous resources can be aggregated and abstracted according to tailored thing-like semantics, thus enabling Things as a Service paradigm, or better a “Cloud of Things”. In the Future Internet initiatives, sensor networks will assume even more of a crucial role, especially for making smarter cities. Smarter sensors will be the peripheral elements of a complex future ICT world. However, due to differences in the “appliances” being sensed, smart sensors are very heterogeneous in terms of communication technologies, sensing features and elaboration capabilities. This article intends to contribute to the design of a pervasive infrastructure where new generation services interact with the surrounding environment, thus creating new opportunities for contextualization and geo-awareness. The architecture proposal is based on Sensor Web Enablement standard specifications and makes use of the Contiki Operating System for accomplishing the IoT. Smart cities are assumed as the reference scenario.
Deep Probabilistic Programming

We propose Edward, a Turing-complete probabilistic progra mming language. Edward builds on two compositional representations—random v ariables and inference. By treating inference as a first class citizen, on a par w ith modeling, we show that probabilistic programming can be as flexible and co mputationally efficient as traditional deep learning. For flexibility, Edward makes it easy to fit the same model using a variety of composable inference methods, ranging from point estimation, to variational inference, to MCMC. In addition, Edward can reuse the modeling representation as part of inference, facilitatin g he design of rich variational models and generative adversarial networks. For ef ficiency, Edward is integrated into TensorFlow, providing significant speedup s over existing probabilistic systems. For example, on a benchmark logistic regr ession task, Edward is at least 35x faster than Stan and PyMC3.
Rollin' Justin - Mobile platform with variable base

Research on humanoid robots for use in servicing tasks, e.g. fetching and delivery, attracts steadily more interest. With “Rollin' Justin” a mobile robotic system and research platform is presented that allows sophisticated control algorithms and dexterous manipulation. This video gives an overview of the mobile humanoid robotic system “Rollin' Justin” with special emphasis on mechanical design features, control issues and high-level system capabilities such as human robot interaction.
A note on multi-image denoising

Taking photographs under low light conditions with a hand-held camera is problematic. A long exposure time can cause motion blur due to the camera shaking and a short exposure time gives a noisy image. We consider the new technical possibility offered by cameras that take image bursts. Each image of the burst is sharp but noisy. In this preliminary investigation, we explore a strategy to efficiently denoise multi-images or video. The proposed algorithm is a complex image processing chain involving accurate registration, video equalization, noise estimation and the use of state-of-the-art denoising methods. Yet, we show that this complex chain may become risk free thanks to a key feature: the noise model can be estimated accurately from the image burst. Preliminary tests will be presented. On the technical side, the method can already be used to estimate a non parametric camera noise model from any image burst.
SOC and SOH Estimation for Multicell Lithium-ion Batteries Based on an Adaptive Hybrid Battery Model and Sliding-Mode Observer

This paper proposes an adaptive hybrid battery model-based high-fidelity state of charge (SOC) and state of health (SOH) estimation method for rechargeable multicell batteries. The hybrid battery model consists of an enhanced Coulomb counting algorithm for SOC estimation and an electrical circuit battery model. A variable-length sliding window least squares (VSWLS)-based online parameter identification algorithm is designed to estimate the electrical parameters of the electrical battery model, which are then used as the parameters of an adaptive discrete-time sliding-mode observer (ADSMO) for terminal and open-circuit voltage estimation of a battery cell. The error of the SOC estimated from the enhanced Coulomb counting algorithm is then corrected by using the SOC obtained from the ADSMOestimated open-circuit voltage. This leads to an accurate, robust real-time SOC estimation. In addition, the maximum capacity of the cell is estimated to determine the SOH of the cell. The proposed method is validated by simulation and experimental results for a four-cell cylindrical lithium-ion battery pack.