nils-herrmann/clean_scirepeval huggingface dataset:
Chinese character CAPTCHA recognition based on convolution neural network

CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) are increasingly used in many applications for machine and human identification. Compared with traditional English and digital characters based CAPTCHAs, Chinese characters contain more complicated characters which greatly enhance difficulty of automatic recognition. To solve that problem, we proposed a Convolution Neural Network (CNN) based approach. This approach greatly improves the recognition accuracy of Chinese Character CAPTCHAs with distortion, rotation and background noise. Our experiment results show that this approach achieves more than 95% accuracy for single character and 84% accuracy for three types of Chinese Character CAPTCHAs with four characters. This encouraging result indicates that deep neural network is useful in complicated structure perception of Chinese Character CAPTCHAs.
An annotation tool for automatically triangulating individuals' psychophysiological emotional reactions to digital media stimuli

Current affective user experience studies require both laborious and time-consuming data analysis, as well as dedicated affective classification algorithms. Moreover, the high technical complexity and lack of general guidelines for developing these affective classification algorithms further limits the comparability of the obtained results. In this paper we target this issue by presenting a tool capable of automatically annotating and triangulating players' physiologically interpreted emotional reactions to in-game events. This tool was initially motivated by an experimental psychology study regarding the emotional habituation effects of audio-visual stimuli in digital games and we expect it to contribute in future similar studies by providing both a deeper and more objective analysis on the affective aspects of user experience. We also hope it will contribute towards the rapid implementation and accessibility of this type of studies by open-sourcing it. Throughout this paper we describe the development and benefits presented by our tool, which include: enabling researchers to conduct objective a posteriori analyses without disturbing the gameplay experience, automating the annotation and emotional response identification process, and formatted data exporting for further analysis in third-party statistical software applications.
AI in computer games : generating interesting interactive opponents by the use of evolutionary computation

Which features of a computer game contribute to the player’s enjoyment of it? How can we automatically generate interesting and satisfying playing experiences for a given game? These are the two key questions addressed in this dissertation. Player satisfaction in computer games depends on a variety of factors; here the focus is on the contribution of the behaviour and strategy of game opponents in predator/prey games. A quantitative metric of the ‘interestingness’ of opponent behaviours is defined based on qualitative considerations of what is enjoyable in such games, and a mathematical formulation grounded in observable data is derived. Using this metric, neural-network opponent controllers are evolved for dynamic game environments where limited inter-agent communication is used to drive spatial coordination of opponent teams. Given the complexity of the predator task, cooperative team behaviours are investigated. Initial candidates are generated using off-line learning procedures operating on minimal neural controllers with the aim of maximising opponent performance. These example controllers are then adapted using on-line (i.e. during play) learning techniques to yield opponents that provide games of high interest. The on-line learning methodology is evaluated using two dissimilar predator/prey games with a number of different computer player strategies. It exhibits generality across the two game test-beds and robustness to changes of player, initial opponent controller selected, and complexity of the game field. The interest metric is also evaluated by comparison with human judgement of game satisfaction in an experimental survey. A statistically significant number of players were asked to rank game experiences with a test-bed game using perceived interestingness and their ranking was compared with that of the proposed interest metric. The results show that the interest metric is consistent with human judgement of game satisfaction. Finally, the generality, limitations and potential of the proposed methodology and techniques are discussed, and other factors affecting the player’s satisfaction, such as the player’s own strategy, are briefly considered. Future directions building on the work described herein are presented and discussed.
Collaborative Dynamic Sparse Topic Regression with User Profile Evolution for Item Recommendation

In many time-aware item recommender systems, modeling the accurate evolution of both user profiles and the contents of items over time is essential. However, most existing methods focus on learning users’ dynamic interests, where the contents of items are assumed to be stable over time. They thus fail to capture the dynamic changes in the item’s contents. In this paper, we present a novel method CDUE for time-aware item recommendation, which captures the evolution of both user’s interests and item’s contents information via topic dynamics. Specifically, we propose a dynamic sparse topic model to track the evolution of topics for changes in items’ contents over time and adapt a vector autoregressive model to profile users’ dynamic interests. The item’s topics and user’s interests and their evolutions are learned collaboratively and simultaneously into a unified learning framework. Experimental results on two real-world data sets demonstrate the quality and effectiveness of the proposed method and show that our method can be used to make better future recommendations.
Ferrite loaded tunable SIW slot antenna

This work proposes a design of novel magnetically tunable substrate integrated waveguide (SIW) slot antenna with one ferrite slab loaded in the antenna. The operation frequency of the antenna can be tuned more than 380 MHz in the X-band from 9.32 to 9.70 GHz when the bias magnetic field changes from 0 to 0.12 tesla. Within the tunable frequency range, antenna gain is more than 6 dBi. The radiation patterns and the gain are almost not affected by the changing of applied magnetic field, showing a good high gain antenna with frequency tunable feature.
Match makers and deal breakers: analyses of assortative mating in newlywed couples.

We conducted a comprehensive analysis of assortative mating (i.e., the similarity between wives and husbands on a given characteristic) in a newlywed sample. These newlyweds showed (a) strong similarity in age, religiousness, and political orientation; (b) moderate similarity in education and verbal intelligence; (c) modest similarity in values; and (d) little similarity in matrix reasoning, self- and spouse-rated personality, emotional experience and expression, and attachment. Further analyses established that similarity was not simply due to background variables such as age and education and reflected initial assortment (i.e., similarity at the time of marriage) rather than convergence (i.e., increasing similarity with time). Finally, marital satisfaction primarily was a function of the rater's own traits and showed little relation to spousal similarity.
Visual scanning of faces in autism.

The visual scanpaths of five high-functioning adult autistic males and five adult male controls were recorded using an infrared corneal reflection technique as they viewed photographs of human faces. Analyses of the scanpath data revealed marked differences in the scanpaths of the two groups. The autistic participants viewed nonfeature areas of the faces significantly more often and core feature areas of the faces (i.e., eyes, nose, and mouth) significantly less often than did control participants. Across both groups of participants, scanpaths generally did not differ as a function of the instructions given to the participants (i.e., "Please look at the faces in any manner you wish." vs. "Please identify the emotions portrayed in these faces."). Autistic participants showed a deficit in emotion recognition, but this effect was driven primarily by deficits in the recognition of fear. Collectively, these results indicate disorganized processing of face stimuli in autistic individuals and suggest a mechanism that may subserve the social information processing deficits that characterize autism spectrum disorders.
Clothes Size Prediction from Dressed-Human Silhouettes

We propose an effective and efficient way to automatically predict clothes size for users to buy clothes online. We take human height and dressed-human silhouettes in front and side views as input, and estimate 3D body sizes with a data-driven method. We adopt 20 body sizes which are closely related to clothes size, and use such 3D body sizes to get clothes size by searching corresponding size chart. Previous image-based methods need to calibrate camera to estimate 3D information from 2D images, because the same person has different appearances of silhouettes (e.g. size and shape) when the camera configuration (intrinsic and extrinsic parameters) is different. Our method avoids camera calibration, which is much more convenient. We set up our virtual camera and train the relationship between human height and silhouette size under this camera configuration. After estimating silhouette size, we regress the positions of 2D body landmarks. We define 2D body sizes as the distances between corresponding 2D body landmarks. Finally, we learn the relationship between 2D body sizes and 3D body sizes. The training samples for each regression process come from a database of 3D naked and dressed bodies created by previous work. We evaluate the whole procedure and each process of our framework. We also compare the performance with several regression models. The total time-consumption for clothes size prediction is less than 0.1 second and the average estimation error of body sizes is 0.824 cm, which can satisfy the tolerance for customers to shop clothes online.
An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest

In-hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track-and-trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high false-alarm rates. We propose a deep learning-based early warning system that shows higher performance than the existing track-and-trigger systems.



This retrospective cohort study reviewed patients who were admitted to 2 hospitals from June 2010 to July 2017. A total of 52 131 patients were included. Specifically, a recurrent neural network was trained using data from June 2010 to January 2017. The result was tested using the data from February to July 2017. The primary outcome was cardiac arrest, and the secondary outcome was death without attempted resuscitation. As comparative measures, we used the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), and the net reclassification index. Furthermore, we evaluated sensitivity while varying the number of alarms. The deep learning-based early warning system (AUROC: 0.850; AUPRC: 0.044) significantly outperformed a modified early warning score (AUROC: 0.603; AUPRC: 0.003), a random forest algorithm (AUROC: 0.780; AUPRC: 0.014), and logistic regression (AUROC: 0.613; AUPRC: 0.007). Furthermore, the deep learning-based early warning system reduced the number of alarms by 82.2%, 13.5%, and 42.1% compared with the modified early warning system, random forest, and logistic regression, respectively, at the same sensitivity.



An algorithm based on deep learning had high sensitivity and a low false-alarm rate for detection of patients with cardiac arrest in the multicenter study.
Cooperative Multi-Sensor Video Surveillance

Takeo Kanade, Robert T. Collins, and Alan J. Lipton Carnegie Mellon University, Pittsburgh, PA e-mail: fkanade,rcollins,ajlg@cs.cmu.edu homepage: http://www.cs.cmu.edu/ vsam P. Anandan, Peter Burt, and Lambert Wixson David Sarno Research Center, Princeton, NJ e-mail: fpanandan,pburt,lwixsong@sarno .com  Carnegie Mellon University (CMU) and the David Sarno Research Center (Sarno ) have begun a joint, integrated feasibility demonstration in the area of Video Surveillance and Monitoring (VSAM). The objective is to develop a cooperative, multi-sensor video surveillance system that provides continuous coverage over large battle eld areas. Image Understanding (IU) technologies will be developed to: 1) coordinate multiple sensors to seamlessly track moving targets over an extended area, 2) actively control sensor and platform parameters to track multiple moving targets, 3) integrate multisensor output with collateral data to maintain an evolving, scene-level representation of all targets and platforms, and 4) monitor the scene for unusual \trigger" events and activities. These technologies will be integrated into an experimental testbed to support evaluation, data collection, and demonstration of other VSAM technologies developed within the DARPA IU community.
Area , Power , and Latency Considerations of STT-MRAM to Substitute for Main Memory

STT-MRAM is one of the most promising non-volatile memory technologies with the potential of becoming a universal memory. However, because of its area, power and latency limitations, STT-MRAM is facing critical bottlenecks in substituting DRAM for main memory. Compared to modern DRAM technology, STT-MRAMs cell area and write power consumption are about four times larger and higher, respectively. In this paper, we study diverse device-level parameters of STT-MRAM to make the storage capacity of STT-MRAM comparable to DRAM with better performance as well as power consumption behavior. We then present analytic models to finely tune the thermal stability factor, which is related to STT-MRAM’s magnetic tunnel junction (MTJ) and the corresponding transistor, and address the challenges that storage-class STT-MRAM faces in replacing DRAM as a working memory. Our preliminary evaluation results show that, our early-stage optimized STT-MRAM can offer shorter latency and lower power consumption than a baseline DRAM by on average 18.4% and 66.2%, respectively.
Optimal Multi-taxi Dispatch for Mobile Taxi-Hailing Systems

Traditional taxi-hailing systems through wireless networks in metropolitan areas allow taxis to compete for passengers chaotically and accidentally, which generally result in inefficiencies, long waiting time and low satisfaction of taxi-hailing passengers. In this paper, we propose a new Mobile Taxi-hailing System (called MTS) based on optimal multi-taxi dispatch, which can be used by taxi service companies (TSCs). Different from the competition modes used in traditional taxi-hailing systems, MTS assigns vacant taxis to taxi-hailing passengers proactively. For the taxi dispatch problem in MTS, we define a system utility function, which involves the total net profits of taxis and waiting time of passengers. Moreover, in the utility function, we take into consideration the various classes of taxis with different resource configurations, and the cost associated with taxis' empty travel distances. Our goal is to maximize the system utility function, restricted by the individual net profits of taxis and the passengers' requirements for specified classes of taxis. To solve this problem, we design an optimal algorithm based on the idea of Kuhn-Munkres (called KMBA), and prove the correctness and optimality of the proposed algorithm. Additionally, we demonstrate the significant performances of our algorithm through extensive simulations.
Chemical Process Control Education and Practice

Chemical process control textbooks and courses differ significantly from their electrical or mechanical-oriented brethren. It is our experience that colleagues in electrical engineering (EE) and mechanical engineering (ME) assume that we teach the same theory in our courses and merely have different application examples. The primary goals of this article are to i) emphasize the distinctly challenging characteristics of chemical processes, ii) present a typical process control curriculum, and iii) discuss how chemical process control courses can be revised to better meet the needs of a typical B.S.-level chemical engineer. In addition to a review of material covered in a standard process control course, we discuss innovative approaches in process control education, including the use of case studies, distributed control systems in laboratories, identification and control simulation packages, and studio-based approaches combining lecture, simulation, and experiments in the same room. We also provide perspectives on needed developments in process control education.
CubeSat networks: Balancing power with satellite-to-ground data throughput

CubeSats are small satellite platforms which have significantly reduced the cost of access to low Earth orbit over the past decade. Recent CubeSat missions have demonstrated the platform's ability to form in-orbit networks. This work addresses a trade-off unique to CubeSat Networks. CubeSat satellite-to-ground (S2G) communication requires high levels of energy consumption to achieve data rates in the order of kilobytes per second. In comparison, CubeSats are capable of more energy efficient satellite-to-satellite (S2S) communication at rates an order of magnitude above those of S2G communication. This asymmetry underpins this work's trade-off of interest, that of CSN energy use against S2G data throughput. Modifications to a specialized Medium Access Control (MAC) and a MANET routing protocol are proposed to explore this trade-off. This work's modified protocols are developed alongside a simulation of a hypothetical CSN mission using the open-source network simulator, OMNeT++. Proposed MAC protocol energy saving features are shown to decrease CSN energy consumption without a reduction in S2G throughput. Modifications to this work's proposed routing protocol introduces the basic election of a CubeSat dedicated to performing S2G communication. The development of this election approach reveals the potential unsuitability of the use of a dynamic routing protocol alongside an inflexible TDMA based MAC protocol in CSNs.
Morphological, molecular and statistical tools to identify Castanea species and their hybrids

The aim of this study was to investigate, in relation to reference samples, the genetic structure of several natural Castanea populations of unknown origin and of hybrids from the clone collection of the Lourizán Forest Research Center. A total of 115 individuals sampled from four Castanea sativa stands located in the northwest of Spain, 61 Castanea crenata individuals and 27 Castanea mollissima individuals were classified on the basis of four morphological traits, and genotyped with 11 microsatellite loci to define a set of reference samples. The data analyzed with the program STRUCTURE detected four clusters: the two Asiatic species and two clusters in C. sativa. From these reference samples, pure individuals and hybrids of known genealogy were simulated to determine the efficiency with which STRUCTURE and NEWHYBRIDS assigned them to pure or hybrid groups and to a specific genealogical class, respectively. As expected, the discrimination and assignment of the simulated individuals improved with increasing F st value. The two clusters identified within C. sativa may correspond to gene pools with different adaptive characteristics previously identified in provenance tests; pure and admixed populations of both C. sativa gene pools were identified.
Application of data mining methods for link prediction in social networks

Using social networking services is becoming more popular day by day. Social network analysis views relationships in terms of nodes (people) and edges (links or connections—the relationship between the people). The websites of the social networks such as Facebook currently are among the most popular internet services just after giant portals such as Yahoo, MSN and search engines such as Google. One of the main problems in analyzing these networks is the prediction of relationships between people in the network. It is hard to find one method that can identify relation between people in the social network. The purpose of this paper is to forecast the friendship relation between individuals among a social network, especially the likelihood of a relation between an existing member with a new member. For this purpose, we used a few hypotheses to make the graph of relationships between members of social network, and we used the method of logistic regression to complete the graph. Test data from Flickr website are used to evaluate the proposed method. The results show that the method has achieved 99 % accuracy in prediction of friendship relationships.
Multi-Label Zero-Shot Human Action Recognition via Joint Latent Embedding

Human action recognition refers to automatic recognizing human actions from a video clip, which is one of the most challenging tasks in computer vision. Due to the fact that annotating video data is laborious and timeconsuming, most of the existing works in human action recognition are limited to a number of small scale benchmark datasets where there are a small number of video clips associated with only a few human actions and a video clip often contains only a single action. In reality, however, there often exist multiple human actions in a video stream. Such a video stream is often weakly-annotated with a set of relevant human action labels at a global level rather than assigning each label to a specific video episode corresponding to a single action, which leads to a multi-label learning problem. Furthermore, there are a great number of meaningful human actions in reality but it would be extremely difficult, if not impossible, to collect/annotate video clips regarding all of various human actions, which leads to a zero-shot learning scenario. To the best of our knowledge, there is no work that has addressed all the above issues together in human action recognition. In this paper, we formulate a real-world human action recognition task as a multi-label zero-shot learning problem and propose a framework to tackle this problem. Our framework simultaneously tackles the issue of unknown temporal boundaries between different actions for multilabel learning and exploits the side information regarding the semantic relationship between different human actions for zero-shot learning. As a result, our framework leads to a joint latent embedding representation for multi-label zeroQian Wang The University of Manchester, UK E-mail: Qian.Wang@manchester.ac.uk Ke Chen (corresponding author) The University of Manchester, UK E-mail: Ke.Chen@manchester.ac.uk shot human action recognition. The joint latent embedding is learned with two component models by exploring temporal coherence underlying video data and the intrinsic relationship between visual and semantic domain. We evaluate our framework with different settings, including a novel data split scheme designed especially for evaluating multi-label zero-shot learning, on two weakly annotated multi-label human action datasets: Breakfast and Charades. The experimental results demonstrate the effectiveness of our framework in multi-label zero-shot human action recognition.