nils-herrmann/clean_scirepeval huggingface dataset:
Reconciling intuitive physics and Newtonian mechanics for colliding objects.

People have strong intuitions about the influence objects exert upon one another when they collide. Because people's judgments appear to deviate from Newtonian mechanics, psychologists have suggested that people depend on a variety of task-specific heuristics. This leaves open the question of how these heuristics could be chosen, and how to integrate them into a unified model that can explain human judgments across a wide range of physical reasoning tasks. We propose an alternative framework, in which people's judgments are based on optimal statistical inference over a Newtonian physical model that incorporates sensory noise and intrinsic uncertainty about the physical properties of the objects being viewed. This noisy Newton framework can be applied to a multitude of judgments, with people's answers determined by the uncertainty they have for physical variables and the constraints of Newtonian mechanics. We investigate a range of effects in mass judgments that have been taken as strong evidence for heuristic use and show that they are well explained by the interplay between Newtonian constraints and sensory uncertainty. We also consider an extended model that handles causality judgments, and obtain good quantitative agreement with human judgments across tasks that involve different judgment types with a single consistent set of parameters.
Cognitive Radio Wireless Sensor Networks: Applications, Challenges and Research Trends

A cognitive radio wireless sensor network is one of the candidate areas where cognitive techniques can be used for opportunistic spectrum access. Research in this area is still in its infancy, but it is progressing rapidly. The aim of this study is to classify the existing literature of this fast emerging application area of cognitive radio wireless sensor networks, highlight the key research that has already been undertaken, and indicate open problems. This paper describes the advantages of cognitive radio wireless sensor networks, the difference between ad hoc cognitive radio networks, wireless sensor networks, and cognitive radio wireless sensor networks, potential application areas of cognitive radio wireless sensor networks, challenges and research trend in cognitive radio wireless sensor networks. The sensing schemes suited for cognitive radio wireless sensor networks scenarios are discussed with an emphasis on cooperation and spectrum access methods that ensure the availability of the required QoS. Finally, this paper lists several open research challenges aimed at drawing the attention of the readers toward the important issues that need to be addressed before the vision of completely autonomous cognitive radio wireless sensor networks can be realized.
Fixed Effects vs . Random Effects Meta-Analysis Models : Implications for Cumulative Research Knowledge

Research conclusions in the social sciences are increasingly based on meta-analysis, making questions of the accuracy of meta-analysis critical to the integrity of the base of cumulative knowledge. Both fixed effects (FE) and random effects (RE) meta-analysis models have been used widely in published meta-analyses. This article shows that FE models typically manifest a substantial Type I bias in significance tests for mean effect sizes and for moderator variables (interactions), while RE models do not. Likewise, FE models, but not RE models, yield confidence intervals for mean effect sizes that are narrower than their nominal width, thereby overstating the degree of precision in meta-analysis findings. This article demonstrates analytically that these biases in FE procedures are large enough to create serious distortions in conclusions about cumulative knowledge in the research literature. We therefore recommend that RE methods routinely be employed in meta-analysis in preference to FE methods.
Shakespearizing Modern Language Using Copy-Enriched Sequence-to-Sequence Models

Variations in writing styles are commonly used to adapt the content to a specific context, audience, or purpose. However, applying stylistic variations is still by and large a manual process, and there have been little efforts towards automating it. In this paper we explore automated methods to transform text from modern English to Shakespearean English using an end to end trainable neural model with pointers to enable copy action. To tackle limited amount of parallel data, we pre-train embeddings of words by leveraging external dictionaries mapping Shakespearean words to modern English words as well as additional text. Our methods are able to get a BLEU score of 31+, an improvement of≈ 6 points above the strongest baseline.
Effective Character-augmented Word Embedding for Machine Reading Comprehension

Machine reading comprehension is a task to model relationship between passage and query. In terms of deep learning framework, most of stateof-the-art models simply concatenate word and character level representations, which has been shown suboptimal for the concerned task. In this paper, we empirically explore different integration strategies of word and character embeddings and propose a character-augmented reader which attends character-level representation to augment word embedding with a short list to improve word representations, especially for rare words. Experimental results show that the proposed approach helps the baseline model significantly outperform state-of-the-art baselines on various public benchmarks.
Measuring tie-strength in virtual social networks

Tie-strength has been in the focus of social science research for decades, yet the use of measurement tools or scales has been relatively scarce. The aim of this study was to fill the gap and provide a tool that is able to provide a quantitative and continuous measure of tie strength in social networks. The focus was on virtual communities because the fast expansion of Internet use and the constant growth of on-line communities provide today’s researchers with an excellent opportunity for effective and speedy data collection regarding tie-strength measures in these virtual social groups. The Virtual Tie-Strength Scale (VTS-Scale) consist of 11 questions and it was developed on a sample of 56 people (3080 asymmetric ties) and tested for reliability of smaller sample of 16 (204 asymmetric ties) independent sample participation regularly on a Hungarian discussion board like forum. Reliability coefficients were reassuringly high for both samples, Cronbach alphas of 0.92 and 0.86, respectively. Data triangulation offered evidence for scale validity. In summary, the VTS-Scale and its scoring method seem to provide a valid and reliable measure of tie strength in virtual communities. Although the aim of the research was to develop a tool that measures tie-strength in virtual communities, the tool can be easily modified for off-line social groups. The VTS-Scale is also capable of distinguishing between two components of tie-strength: acquaintances and friendship. However, the content of each component needs further investigation.
Personalized Book Recommendations Created by Using Social Media Data

Book recommendation systems can benefit commercial websites, social media sites, and digital libraries, to name a few, by alleviating the knowledge acquisition process of users who look for books that are appealing to them. Even though existing book recommenders, which are based on either collaborative filtering, text content, or the hybrid approach, aid users in locating books (among the millions available), their recommendations are not personalized enough to meet users’ expectations due to their collective assumption on group preference and/or exact content matching, which is a failure. To address this problem, we have developed PBRecS, a book recommendation system that is based on social interactions and personal interests to suggest books appealing to users. PBRecS relies on the friendships established on a social networking site, such as LibraryThing, to generate more personalized suggestions by including in the recommendations solely books that belong to a user’s friends who share common interests with the user, in addition to applying word-correlation factors for partially matching book tags to disclose books similar in contents. The conducted empirical study on data extracted from LibraryThing has verified (i) the effectiveness of PBRecS using social-media data to improve the quality of book recommendations and (ii) that PBRecS outperforms the recommenders employed by Amazon and LibraryThing.
Stacked convolutional auto-encoders for steganalysis of digital images

In this paper, we point out that SRM (Spatial-domain Rich Model), the most successful steganalysis framework of digital images possesses a similar architecture to CNN (convolutional neural network). The reasonable expectation is that the steganalysis performance of a well-trained CNN should be comparable to or even better than that of the hand-coded SRM. However, a CNN without pre-training always get stuck at local plateaus or even diverge which result in rather poor solutions. In order to circumvent this obstacle, we use convolutional auto-encoder in the pre-training procedure. A stack of convolutional auto-encoders forms a CNN. The experimental results show that initializing a CNN with the mixture of the filters from a trained stack of convolutional auto-encoders and feature pooling layers, although still can not compete with SRM, yields superior performance compared to traditional CNN for the detection of HUGO generated stego images in BOSSBase image database.
Multimodal Guitar: A Toolbox For Augmented Guitar Performances

This project aims at studying how recent interactive and interactions technologies would help extend how we play the guitar, thus defining the “multimodal guitar”. Our contributions target three main axes: audio analysis, gestural control and audio synthesis. For this purpose, we designed and developed a freely-available toolbox for augmented guitar performances, compliant with the PureData and Max/MSP environments, gathering tools for: polyphonic pitch estimation, fretboard visualization and grouping, pressure sensing, modal synthesis, infinite sustain, rearranging looping and “smart” harmonizing.
An Analytical Continuous-Curvature Path-Smoothing Algorithm

An efficient and analytical continuous-curvature path-smoothing algorithm, which fits an ordered sequence of waypoints generated by an obstacle-avoidance path planner, is proposed. The algorithm is based upon parametric cubic Bézier curves; thus, it is inherently closed-form in its expression, and the algorithm only requires the maximum curvature to be defined. The algorithm is, thus, computational efficient and easy to implement. Results show the effectiveness of the analytical algorithm in generating a continuous-curvature path, which satisfies an upper bound-curvature constraint, and that the path generated requires less control effort to track and minimizes control-input variability.
A new hand exoskeleton device for rehabilitation using a three-layered sliding spring mechanism

In this paper, a new hand exoskeleton device using a three-layered sliding spring mechanism is presented. In contrast to state of the art hand exoskeleton mechanisms (typically link, wire or pneumatically driven), the proposed mechanism is driven through large deformations of the compliant mechanism body. The mechanism can be made compact and lightweight by adequately positioning the compliant elements. In addition, the mechanism is designed to distribute 1-DOF actuated linear motion into three rotational motions of the finger joints, which translate into natural finger flexion/extension. The primary application of the proposed mechanism is to provide robotic support during physical therapy at the hospital (e.g. Continuous Passive Motion). However, thanks to its light and wearable structure, the proposed device could also be used at home as an assistive/therapeutic device to support activities of daily living. We introduce the mechanical structure of the three-layered sliding spring mechanism, present a prototype implementation as a hand exoskeleton device, and provide a preliminary evaluation.
Neurodegenerative Diseases Target Large-Scale Human Brain Networks

During development, the healthy human brain constructs a host of large-scale, distributed, function-critical neural networks. Neurodegenerative diseases have been thought to target these systems, but this hypothesis has not been systematically tested in living humans. We used network-sensitive neuroimaging methods to show that five different neurodegenerative syndromes cause circumscribed atrophy within five distinct, healthy, human intrinsic functional connectivity networks. We further discovered a direct link between intrinsic connectivity and gray matter structure. Across healthy individuals, nodes within each functional network exhibited tightly correlated gray matter volumes. The findings suggest that human neural networks can be defined by synchronous baseline activity, a unified corticotrophic fate, and selective vulnerability to neurodegenerative illness. Future studies may clarify how these complex systems are assembled during development and undermined by disease.
On the Differential Privacy of Bayesian Inference

We study how to communicate findings of Bayesian inference to third parties, while preserving the strong guarantee of differential privacy. Our main contributions are four different algorithms for private Bayesian inference on probabilistic graphical models. These include two mechanisms for adding noise to the Bayesian updates, either directly to the posterior parameters, or to their Fourier transform so as to preserve update consistency. We also utilise a recently introduced posterior sampling mechanism, for which we prove bounds for the specific but general case of discrete Bayesian networks; and we introduce a maximum-a-posteriori private mechanism. Our analysis includes utility and privacy bounds, with a novel focus on the influence of graph structure on privacy. Worked examples and experiments with Bayesian naïve Bayes and Bayesian linear regression illustrate the application of our mechanisms.
Hybrid Question Answering over Knowledge Base and Free Text

Recent trend in question answering (QA) systems focuses on using structured knowledge bases (KBs) to find answers. While these systems are able to provide more precise answers than information retrieval (IR) based QA systems, the natural incompleteness of KB inevitably limits the question scope that the system can answer. In this paper, we present a hybrid question answering (hybrid-QA) system which exploits both structured knowledge base and free text to answer a question. The main challenge is to recognize the meaning of a question using these two resources, i.e., structured KB and free text. To address this, we map relational phrases to KB predicates and textual relations simultaneously, and further develop an integer linear program (ILP) model to infer on these candidates and provide a globally optimal solution. Experiments on benchmark datasets show that our system can benefit from both structured KB and free text, outperforming the state-of-the-art systems.
Study on sliding-mode based MPPT for PV array and supplying power to AC grid

This paper aims to study the recent work regarding the maximum power point tracker (MPPT) based on a sliding-mode manner for a PV array. Such a MPPT accompanies a perturbation and observation algorithm as well as a boost converter. Moreover, it needs a straight sliding-line and some adjustments on its location until a PV characteristic curve and this sliding line cross each other at the maximum power point (MPP). The switching pattern of the boost converter is influenced by this straight line so that the PV operating point is compelled to move along an instant PV curve and onto the sliding line. Thereby the PV array will generate the maximum power. An easier explanation for convergence towards the sliding line is provided. Through the boost converter and the single-phase inverter, power delivery from a PV array to an AC grid under the above MPPT will be also considered.
An approximation algorithm for the generalized assignment problem

The generalized assignment problem can be viewed as the following problem of scheduling parallel machines with costs. Each job is to be processed by exactly one machine; processing job j on machine i requires time pif and incurs a cost of c,f, each machine / is available for 7", t ime units, and the objective is.t»minimize the total cost incurred. Our main result is as follows. There is a polynomial-time algorithm that, given a value C, either proves that no feasible schedule of cost C exists, or else finds a schedule of cost at most C where each machine / is used for at most 27", time units. We also extend this result to a variant of the problem where, instead of a fixed processing time p,r there is a range of possible processing times for each machine-job pair, and the cost linearly increases as (he processing time decreases. We show that these results imply a polynomial-time 2-approximation algorithm to minimize a weighted sum of the cost and the makespan, i.e., the maximum job completion time. We also consider the objective of minimizing the mean job completion time. We show that there is a polynomial-time algorithm that, given values M and 7", either proves that no schedule of mean job completion time M and makespan /"exists, or else finds a schedule of mean job completion time at most M and makespan at most 27".
Half-Select Free and Bit-Line Sharing 9T SRAM for Reliable Supply Voltage Scaling

This paper presents a half-select free 9T SRAM to facilitate reliable SRAM operation in the near-threshold voltage region. In the proposed SRAM, the half-select disturbance, which results in instable operations in 6T SRAM cell, can be completely eliminated by adopting cross-access selection of row and column word-lines. To minimize the area overhead of the half-select free 9T SRAM cell, a bit-line and access transistors between the adjacent cells are shared using a symmetric shared node that connects two cells. In addition, a selective pre-charge scheme considering the preferably isolated unselected cells has also been proposed to reduce the dynamic power consumption. The simulation results with the most probable failure point method show that the proposed 9T SRAM cell has a minimum operating voltage (VMIN) of 0.45 V among the half-select free SRAM cells. The test chip with 65-nm CMOS technology shows that the proposed 9T SRAM is fully operated at 0.35 V and 25 °C condition. Under the supply voltages between 0.35 and 1.1 V, the 4-kb SRAM macro is operated between 640 kHz and 560 MHz, respectively. The proposed 9T SRAM shows the best voltage scalability without any assist circuit while maintaining small macro area and fast operation frequency.
Evaluation of Decision Tree Pruning Algorithms for Complexity and Classification Accuracy

Classification is an important problem in data mining. Given a database of records, each with a class label, a classifier generates a concise and meaningful description for each class that can be used to classify subsequent records. A number of popular classifiers construct decision trees to generate class models. These classifiers first build a decision tree and then prune subtrees from the decision tree in a subsequent pruning phase to improve accuracy and prevent “overfitting”. In this paper, the different pruning methodologies available & their various features are discussed. Also the effectiveness of pruning is evaluated in terms of complexity and classification accuracy by applying C4.5 decision tree classification algorithm on Credit Card Database with pruning and without pruning. Instead of classifying the transactions either fraud or non-fraud the transactions are classified in four risk levels which is an innovative concept.
Driver Action Prediction Using Deep (Bidirectional) Recurrent Neural Network

Advanced driver assistance systems (ADAS) can be significantly improved with effective driver action prediction (DAP). Predicting driver actions early and accurately can help mitigate the effects of potentially unsafe driving behaviors and avoid possible accidents. In this paper, we formulate driver action prediction as a timeseries anomaly prediction problem. While the anomaly (driver actions of interest) detection might be trivial in this context, finding patterns that consistently precede an anomaly requires searching for or extracting features across multi-modal sensory inputs. We present such a driver action prediction system, including a real-time data acquisition, processing and learning framework for predicting future or impending driver action. The proposed system incorporates camera-based knowledge of the driving environment and the driver themselves, in addition to traditional vehicle dynamics. It then uses a deep bidirectional recurrent neural network (DBRNN) to learn the correlation between sensory inputs and impending driver behavior achieving accurate and high horizon action prediction. The proposed system performs better than other existing systems on driver action prediction tasks and can accurately predict key driver actions including acceleration, braking, lane change and turning at durations of 5sec before the action is executed by the driver.  timeseries modeling, driving assistant system, driver action prediction, driver intent estimation, deep recurrent neural network
Face Identification by Fitting a 3D Morphable Model Using Linear Shape and Texture Error Functions

This paper presents a novel algorithm aiming at analysis and identification of faces viewed from different poses and illumination conditions. Face analysis from a single image is performed by recovering the shape and textures parameters of a 3D Morphable Model in an analysis-by-synthesis fashion. The shape parameters are computed from a shape error estimated by optical flow and the texture parameters are obtained from a texture error. The algorithm uses linear equations to recover the shape and texture parameters irrespective of pose and lighting conditions of the face image. Identification experiments are reported on more than 5000 images from the publicly available CMU-PIE database which includes faces viewed from 13 different poses and under 22 different illuminations. Extensive identification results are available on our web page for future comparison with novel algorithms.
Neural signature of fictive learning signals in a sequential investment task.

Reinforcement learning models now provide principled guides for a wide range of reward learning experiments in animals and humans. One key learning (error) signal in these models is experiential and reports ongoing temporal differences between expected and experienced reward. However, these same abstract learning models also accommodate the existence of another class of learning signal that takes the form of a fictive error encoding ongoing differences between experienced returns and returns that "could-have-been-experienced" if decisions had been different. These observations suggest the hypothesis that, for all real-world learning tasks, one should expect the presence of both experiential and fictive learning signals. Motivated by this possibility, we used a sequential investment game and fMRI to probe ongoing brain responses to both experiential and fictive learning signals generated throughout the game. Using a large cohort of subjects (n = 54), we report that fictive learning signals strongly predict changes in subjects' investment behavior and correlate with fMRI signals measured in dopaminoceptive structures known to be involved in valuation and choice.