nils-herrmann/clean_scirepeval huggingface dataset:
Reward Functions for Accelerated Learning

This paper discusses why traditional reinforcement learning methods, and algorithms applied to those models, result in poor performance in situated domains characterized by multiple goals, noisy state, and inconsistent reinforcement. We propose a methodology for designing reinforcement functions that take advantage of implicit domain knowledge in order to accelerate learning in such domains. The methodology involves the use of heterogeneous reinforcement functions and progress estimators, and applies to learning in domains with a single agent or with multiple agents. The methodology is experimentally validated on a group of mobile robots learning a foraging task.
High bandwidth current sensing of SiC MOSFET with a Si current mirror

SiC Intelligent Power Module (IPM) with high bandwidth integrated current sensors is a future trend to improve the device protection capability and chip utilization. In this work, an integrated current sensing scheme for Silicon Carbide (SiC) MOSFET power module using a Si MOSFET current mirror is proposed, analyzed and tested. The use of Si MOSFET not only lowers the overall cost, but compensates the temperature variation as well. The influence of device mismatching on sensing accuracy are discussed. Optimal selection and trade-off of sensing resistor value are calculated. Discrete device circuit and conceptual DBC-based module are tested to verify the scheme's feasibility and performance. The results show good steady state accuracy and high bandwidth performance.
The neuropsychology of narrative: story comprehension, story production and their interrelation

Stories are used extensively for human communication; both the comprehension and production of oral and written narratives constitute a fundamental part of our experience. While study of this topic has largely been the domain of cognitive psychology, neuroscience has also made progress in uncovering the processes underlying these abilities. In an attempt to synthesize work from both literatures, this review: (1) summarizes the current neuroimaging and patient research pertaining to narrative comprehension and production, (2) attempts to integrate this information with the processes described by the discourse models of cognitive psychology, and (3) uses this information to examine the possible interrelation between comprehension and production. Story comprehension appears to entail a network of frontal, temporal and cingulate areas that support working-memory and theory-of-mind processes. The specific functions associated with these areas are congruent with the processes proposed by cognitive models of comprehension. Moreover, these same areas appear necessary for story production, and the causal-temporal ordering of selected information may partially account for this common ground. A basic description of comprehension and production based solely on neuropsychological evidence is presented to complement current cognitive models, and a number of avenues for future research are suggested.
Robot self-assembly by folding: A printed inchworm robot

Printing and folding are fast and inexpensive methods for prototyping complex machines. Self-assembly of the folding step would expand the possibilities of this method to include applications where external manipulation is costly, such as micro-assembly, mass production, and space applications. This paper presents a method for self-folding of printed robots from two-dimensional materials based on shape memory polymers actuated by joule heating using embedded circuits. This method was shown to be capable of sequential folding, angle-controlled folds, slot-and-tab assembly, and mountain and valley folds. An inchworm robot was designed to demonstrate the merits of this technique. Upon the application of sufficient current, the robot was able to fold into its functional form with fold angle deviations within six degrees. This printed robot demonstrated locomotion at a speed of two millimeters per second.
Bandwidth Enhancement of a Horizontally Polarized Omnidirectional Antenna by Adding Parasitic Strips

A horizontally polarized omnidirectional antenna with enhanced impedance bandwidth is presented in this letter. The proposed antenna consists of a feeding network, four printed dipole elements with etched slots, parasitic strips, and director elements. Four identically curved and printed dipole elements are placed in a square array and fed by a feeding network with uniform magnitude and phase; thus, the proposed antenna can achieve an omnidirectional radiation. To enhance the impedance bandwidth, parasitic strips and etched slots are introduced to produce additional lower and upper resonant frequencies, respectively. By utilizing four director elements, the gain variation in the horizontal plane can be improved, especially for the upper frequency band. With the structure, a reduced size of <inline-formula> <tex-math notation="LaTeX">$0.63\,\lambda _{L} \times 0.63\,\lambda _{L} \times 0.01\,\lambda _{L}$</tex-math> </inline-formula> (<inline-formula><tex-math notation="LaTeX">$\lambda _{L}$</tex-math></inline-formula> is the free-space wavelength at the lowest frequency) is obtained. The proposed antenna is designed and fabricated. Measurement results reveal that the proposed antenna can provide an impedance bandwidth of 84.2% (1.58–3.88 GHz). Additionally, the gain variation in the horizontal plane is less than 1.5 dB over the frequency band 1.58–3.50 GHz, and increased to 2.2 dB at 3.80 GHz. Within the impedance bandwidth, the cross-polarization level is less than –23 dB in the horizontal plane.
Estimating crop yields with deep learning and remotely sensed data

This paper describes Illinois corn yield estimation using deep learning and another machine learning, SVR. Deep learning is a technique that has been attracting attention in recent years of machine learning, it is possible to implement using the Caffe. High accuracy estimation of crop yield is very important from the viewpoint of food security. However, since every country prepare data inhomogeneously, the implementation of the crop model in all regions is difficult. Deep learning is possible to extract important features for estimating the object from the input data, so it can be expected to reduce dependency of input data. The network model of two InnerProductLayer was the best algorithm in this study, achieving RMSE of 6.298 (standard value). This study highlights the advantages of deep learning for agricultural yield estimating.
Design Principles of the Hippocampal Cognitive Map

Hippocampal place fields have been shown to reflect behaviorally relevant aspects of space. For instance, place fields tend to be skewed along commonly traveled directions, they cluster around rewarded locations, and they are constrained by the geometric structure of the environment. We hypothesize a set of design principles for the hippocampal cognitive map that explain how place fields represent space in a way that facilitates navigation and reinforcement learning. In particular, we suggest that place fields encode not just information about the current location, but also predictions about future locations under the current transition distribution. Under this model, a variety of place field phenomena arise naturally from the structure of rewards, barriers, and directional biases as reflected in the transition policy. Furthermore, we demonstrate that this representation of space can support efficient reinforcement learning. We also propose that grid cells compute the eigendecomposition of place fields in part because is useful for segmenting an enclosure along natural boundaries. When applied recursively, this segmentation can be used to discover a hierarchical decomposition of space. Thus, grid cells might be involved in computing subgoals for hierarchical reinforcement learning.
Predicting Pre-click Quality for Native Advertisements

Native advertising is a specific form of online advertising where ads replicate the look-and-feel of their serving platform. In such context, providing a good user experience with the served ads is crucial to ensure long-term user engagement. In this work, we explore the notion of ad quality, namely the effectiveness of advertising from a user experience perspective. We design a learning framework to predict the pre-click quality of native ads. More specifically, we look at detecting offensive native ads, showing that, to quantify ad quality, ad offensive user feedback rates are more reliable than the commonly used click-through rate metrics. We then conduct a crowd-sourcing study to identify which criteria drive user preferences in native advertising. We translate these criteria into a set of ad quality features that we extract from the ad text, image and advertiser, and then use them to train a model able to identify offensive ads. We show that our model is very effective in detecting offensive ads, and provide in-depth insights on how different features affect ad quality. Finally, we deploy a preliminary version of such model and show its effectiveness in the reduction of the offensive ad feedback rate.
Hough Technique for Bar Charts Detection and Recognition in Document Images

Charts are common graphic representation for scientific data in technical and business papers. We present a robust system for detecting and recognizing bar charts. The system includes three stages, preprocessing, detection and recognition. The kernel algorithm in detection is newly developed Modified Probabilistic Hough Transform algorithm for parallel lines clusters detection. The main algorithms in recognition are bar pattern reconstruction and text primitives grouping in the Hough space which are also original. The Experiments show the system can also recognize slant bar charts, or even hand-drawn charts.
Incentive Mechanisms for Time Window Dependent Tasks in Mobile Crowdsensing

Mobile crowdsensing can enable numerous attractive novel sensing applications due to the prominent advantages such as wide spatiotemporal coverage, low cost, good scalability, pervasive application scenarios, etc. In mobile crowdsensing applications, incentive mechanisms are necessary to stimulate more potential smartphone users and to achieve good service quality. In this paper, we focus on exploring truthful incentive mechanisms for a novel and practical scenario where the tasks are time window dependent, and the platform has strong requirement of data integrity. We present a universal system model for this scenario based on reverse auction framework and formulate the problem as the Social Optimization User Selection (SOUS) problem. We design two incentive mechanisms, MST and MMT. In single time window case, we design an optimal algorithm based on dynamic programming to select users. Then we determine the payment for each user by VCG auction; while in multiple time window case, we show the general SOUS problem is NP-hard, and we design MMT based on greedy approach, which approximates the optimal solution within a factor of In|W| + 1, where |W| is the length of sensing time window defined by the platform. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve high computation efficiency, individual rationality and truthfulness.
A study of parameter tuning for term frequency normalization

Most current term frequency normalization approaches for information retrieval involve the use of parameters. The tuning of these parameters has an important impact on the overall performance of the information retrieval system. Indeed, a small variation in the involved parameter(s) could lead to an important variation in the precision/recall values. Most current tuning approaches are dependent on the document collections. As a consequence, the effective parameter value cannot be obtained for a given new collection without extensive training data. In this paper, we propose a novel and robust method for the tuning of term frequency normalization parameter(s), by measuring the normalization effect on the within document frequency of the query terms. As an illustration, we apply our method on Amati \& Van Rijsbergen's so-called normalization 2. The experiments for the ad-hoc TREC-6,7,8 tasks and TREC-8,9,10 Web tracks show that the new method is independent of the collections and able to provide reliable and good performance.
Predicting US Primary Elections with Twitter

Using social media for political analysis is becoming a common practice, especially during election time. Many researchers and media are trying to use social media to understand the public opinion and trend. In this paper, we investigate how we could use Twitter to predict public opinion and thus predict American republican presidential election results. We analyzed millions of tweets from September 2011 leading up to the republican primary elections. First we examine the previous methods regarding predicting election results with social media and then we integrate our understanding of social media and propose a prediction model to predict the public opinions towards Republican Presidential Elections. Our results highlight the feasibility of using social media to predict public opinions and thus replace traditional polling.
Uplift Modeling with ROC: An SRL Case Study

Uplift modeling is a classification method that determines the incremental impact of an action on a given population. Uplift modeling aims at maximizing the area under the uplift curve, which is the difference between the subject and control sets’ area under the lift curve. Lift and uplift curves are seldom used outside of the marketing domain, whereas the related ROC curve is frequently used in multiple areas. Achieving a good uplift using an ROC-based model instead of lift may be more intuitive in several areas, and may help uplift modeling reach a wider audience. We alter SAYL, an uplift-modeling statistical relational learner, to use ROC instead of lift. We test our approach on a screening mammography dataset. ROC outperforms SAYL on our data, though not significantly, suggesting that ROC can be used for uplift modeling. On the other hand, SAYL-ROC returns larger models, reducing interpretability.
Editorial: E-learning and Human-Computer Interaction: Exploring Design Synergies for more Effective Learning Experiences

1 ISSN 1436-4522 (online) and 1176-3647 (print).  The authors and the forum jointly retain the copyright of the articles. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear the full citation on the first page. Copyrights for components of this work owned by others than IFETS must be honoured. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from the editors at kinshuk@ieee.org. E-learning and Human-Computer Interaction: Exploring Design Synergies for more Effective Learning Experiences
High-performance chemical sensing using Schottky-contacted chemical vapor deposition grown monolayer MoS2 transistors.

Trace chemical detection is important for a wide range of practical applications. Recently emerged two-dimensional (2D) crystals offer unique advantages as potential sensing materials with high sensitivity, owing to their very high surface-to-bulk atom ratios and semiconducting properties. Here, we report the first use of Schottky-contacted chemical vapor deposition grown monolayer MoS2 as high-performance room temperature chemical sensors. The Schottky-contacted MoS2 transistors show current changes by 2-3 orders of magnitude upon exposure to very low concentrations of NO2 and NH3. Specifically, the MoS2 sensors show clear detection of NO2 and NH3 down to 20 ppb and 1 ppm, respectively. We attribute the observed high sensitivity to both well-known charger transfer mechanism and, more importantly, the Schottky barrier modulation upon analyte molecule adsorption, the latter of which is made possible by the Schottky contacts in the transistors and is not reported previously for MoS2 sensors. This study shows the potential of 2D semiconductors as high-performance sensors and also benefits the fundamental studies of interfacial phenomena and interactions between chemical species and monolayer 2D semiconductors.
RGB-D object tracking: A particle filter approach on GPU

This paper presents a particle filtering approach for 6-DOF object pose tracking using an RGB-D camera. Our particle filter is massively parallelized in a modern GPU so that it exhibits real-time performance even with several thousand particles. Given an a priori 3D mesh model, the proposed approach renders the object model onto texture buffers in the GPU, and the rendered results are directly used by our parallelized likelihood evaluation. Both photometric (colors) and geometric (3D points and surface normals) features are employed to determine the likelihood of each particle with respect to a given RGB-D scene. Our approach is compared with a tracker in the PCL both quantitatively and qualitatively in synthetic and real RGB-D sequences, respectively.
Designing robots for long-term social interaction

Valerie the roboceptionist is the most recent addition to Carnegie Mellon's social robots project. A permanent installation in the entranceway to Newell-Simon hall, the robot combines useful functionality - giving directions, looking up weather forecasts, etc. - with an interesting and compelling character. We are using Valerie to investigate human-robot social interaction, especially long-term human-robot "relationships". Over a nine-month period, we have found that many visitors continue to interact with the robot on a daily basis, but that few of the individual interactions last for more than 30 seconds. Our analysis of the data has indicated several design decisions that should facilitate more natural human-robot interactions.
2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy of Linear Classifiers

Enlarged lymph nodes (LNs) can provide important information for cancer diagnosis, staging, and measuring treatment reactions, making automated detection a highly sought goal. In this paper, we propose a new algorithm representation of decomposing the LN detection problem into a set of 2D object detection subtasks on sampled CT slices, largely alleviating the curse of dimensionality issue. Our 2D detection can be effectively formulated as linear classification on a single image feature type of Histogram of Oriented Gradients (HOG), covering a moderate field-of-view of 45 by 45 voxels. We exploit both max-pooling and sparse linear fusion schemes to aggregate these 2D detection scores for the final 3D LN detection. In this manner, detection is more tractable and does not need to perform perfectly at instance level (as weak hypotheses) since our aggregation process will robustly harness collective information for LN detection. Two datasets (90 patients with 389 mediastinal LNs and 86 patients with 595 abdominal LNs) are used for validation. Cross-validation demonstrates 78.0% sensitivity at 6 false positives/volume (FP/vol.) (86.1% at 10 FP/vol.) and 73.1% sensitivity at 6 FP/vol. (87.2% at 10 FP/vol.), for the mediastinal and abdominal datasets respectively. Our results compare favorably to previous state-of-the-art methods.
Understanding Users of Commercial Music Services through Personas: Design Implications

Most of the previous literature on music users’ needs, habits, and interactions with music information retrieval (MIR) systems focuses on investigating user groups of particular demographics or testing the usability of specific interfaces/systems. In order to improve our understanding of how users’ personalities and characteristics affect their needs and interactions with MIR systems, we conducted a qualitative user study across multiple commercial music services, utilizing interviews and think-aloud sessions. Based on the empirical user data, we have developed seven personas. These personas offer a deeper understanding of the different types of MIR system users and the relative importance of various design implications for each user type. Implications for system design include a renegotiation of our understanding of desired user engagement, especially with the habit of context-switching, designing systems for specialized uses, and addressing user concerns around privacy, transparency, and control.
MergeRUCB: A Method for Large-Scale Online Ranker Evaluation

A key challenge in information retrieval is that of on-line ranker evaluation: determining which one of a finite set of rankers performs the best in expectation on the basis of user clicks on presented document lists. When the presented lists are constructed using interleaved comparison methods, which interleave lists proposed by two different candidate rankers, then the problem of minimizing the total regret accumulated while evaluating the rankers can be formalized as a K-armed dueling bandit problem. In the setting of web search, the number of rankers under consideration may be large. Scaling effectively in the presence of so many rankers is a key challenge not adequately addressed by existing algorithms.
 We propose a new method, which we call mergeRUCB, that uses "localized" comparisons to provide the first provably scalable K-armed dueling bandit algorithm. Empirical comparisons on several large learning to rank datasets show that mergeRUCB can substantially outperform the state of the art K-armed dueling bandit algorithms when many rankers must be compared. Moreover, we provide theoretical guarantees demonstrating the soundness of our algorithm.
Using hypervisor to provide data secrecy for user applications on a per-page basis

Hypervisors are increasingly utilized in modern computer systems, ranging from PCs to web servers and data centers. Aside from server applications, hypervisors are also becoming a popular target for implementing many security systems, since they provide a small and easy-to-secure trusted computing base. This paper presents a novel way of using hypervisors to protect application data privacy even when the underlying operating system is not trustable. Each page in virtual address space is rendered to user applications according to the security context the application is running in. The hypervisor encrypts and decrypts each memory page requested depending on the application's access permission to the page. The main result of this system is the complete removal of the operating system from the trust base for user applications' data privacy. To reduce the runtime overhead of the system, two optimization techniques are employed. We use page-frame replication to reduce the number ofcryptographic operations by keeping decrypted versions of a page frame. We also employ lazy synchronization to minimize overhead due to an update to one of the replicated page frame. Our system is implemented and evaluated by modifying the Xen hypervisor, showing that it increases the application execution time only by 3% for CPU and memory-intensive workloads.
V3: Unsupervised Aspect Based Sentiment Analysis for SemEval2015 Task 12

This paper presents our participation in SemEval-2015 task 12 (Aspect Based Sentiment Analysis). We participated employing only unsupervised or weakly-supervised approaches. Our attempt is based on requiring the minimum annotated or hand-crafted content, and avoids training a model using the provided training set. We use a continuous word representations (Word2Vec) to leverage in-domain semantic similarities of words for many of the involved subtasks.