nils-herrmann/clean_scirepeval huggingface dataset:
Brain mechanisms of proactive interference in working memory

It has long been known that storage of information in working memory suffers as a function of proactive interference. Here we review the results of experiments using approaches from cognitive neuroscience to reveal a pattern of brain activity that is a signature of proactive interference. Many of these results derive from a single paradigm that requires one to resolve interference from a previous experimental trial. The importance of activation in left inferior frontal cortex is shown repeatedly using this task and other tasks. We review a number of models that might account for the behavioral and imaging findings about proactive interference, raising questions about the adequacy of these models.
Surgical Simulation Training Systems : Box Trainers , Virtual Reality and Augmented Reality Simulators

In contrast to Virtual Reality (VR) and traditional surgical training, simulation-based training in Minimally Invasive Surgery (MIS), and Augmented Reality (AR) technology, constitute relatively new application fields in surgery. However, between simulation and AR there is a direct link: the utilization of state-of-the art computer graphics tools and techniques. Nevertheless, an integrated research system that introduces AR properties in simulation-based surgical training scenarios has not yet been proposed in the current literature. In this article we provide a survey on the simulation techniques and systems employed for surgical skills training based on traditional as well as more advanced VR technologies. Moreover, we summarize recent experimental results with regard to the design of a novel ARbased simulator developed in our lab for basic skills training in MIS.
A Computer Vision System for Lateral Localization

For urban driving, knowledge of ego-vehicle’s position is a critical piece of information that enables advanced driver-assistance systems or self-driving cars to execute safety-related, autonomous driving maneuvers. This is because, without knowing the current location, it is very hard to autonomously execute any driving maneuvers for the future. The existing solutions for localization rely on a combination of a Global Navigation Satellite System, an inertial measurement unit, and a digital map. However, in urban driving environments, due to poor satellite geometry and disruption of radio signal reception, their longitudinal and lateral errors are too significant to be used for an autonomous system. To enhance the existing system’s localization capability, this work presents an effort to develop a vision-based lateral localization algorithm. The algorithm aims at reliably counting, with or without observations of lane-markings, the number of road-lanes and identifying the index of the road-lane on the roadway upon which our vehicle happens to be driving. Tests of the proposed algorithms against intercity and interstate highway videos showed promising results in terms of counting the number of road-lanes and the indices of the current road-lanes.
No Please, After You: Detecting Fraud in Affiliate Marketing Networks

Cookie stuffing is an activity which allows unscrupulous actors online to defraud affiliate marketing programs by causing themselves to receive credit for purchases made by web users, even if the affiliate marketer did not actively perform any marketing for the affiliate program. Using two months of HTTP request logs from a large public university, we present an empirical study of fraud in affiliate marketing programs. First, we develop an efficient, decision-tree based technique for detecting cookie-stuffing in HTTP request logs. Our technique replicates domain-informed human labeling of the same data with 93.3% accuracy. Second, we find that over one-third of publishers in affiliate marketing programs use fraudulent cookie-stuffing techniques in an attempt to claim credit from online retailers for illicit referrals. However, most realized conversions are credited to honest publishers. Finally, we present a stake holder analysis of affiliate marketing fraud and find that the costs and rewards of affiliate marketing programs are spread across all parties involved. Biography Peter Snyder is pursuing a Ph.D. in the Department of Computer Science at the University of Illinois at Chicago. He received his B.A. in political science at Lawrence University, with a focus on economics. His current research focuses on the security and privacy of web browsing. His current projects include measuring the popularity, desirability and security costs of browser complexity, and investigating alternative web systems that prioritize client security and code predictability at minimal cost to web-author expressivity.  ALL ARE WELCOME ** Host: Professor Kehuan Zhang (  ( 3943-8385)
Cylindrical panoramic image stitching method based on multi-cameras

Panoramic stitching technology is the focus of current panoramic technology, and cylindrical panoramas are commonly used because of their ease of capture and storage. Moreover it is a simple method for constructing panoramic video. This paper presents a cylindrical panoramic generation method based on multi-cameras. First, we use a backward-division model, which can rapidly solve the distortion of fish eye lens. Second, in order to maintain consistency of stitching, use the cylindrical projection. Third, we apply SIFT feature detection method to image mosaic process. To ensure the accuracy of matching, we use the RANSAC algorithm to purify the detected feature points. In addition, we use an image fusion method based on Lapalace pyramid. The original images captured by fisheye multi-cameras device are processed according to the procedure described in this paper, and then the cylindrical panoramic image is obtained.
Comparing face recognition algorithms to humans on challenging tasks

We compared face identification by humans and machines using images taken under a variety of uncontrolled illumination conditions in both indoor and outdoor settings. Natural variations in a person's day-to-day appearance (e.g., hair style, facial expression, hats, glasses, etc.) contributed to the difficulty of the task. Both humans and machines matched the identity of people (same or different) in pairs of frontal view face images. The degree of difficulty introduced by photometric and appearance-based variability was estimated using a face recognition algorithm created by fusing three top-performing algorithms from a recent international competition. The algorithm computed similarity scores for a constant set of same-identity and different-identity pairings from multiple images. Image pairs were assigned to good, moderate, and poor accuracy groups by ranking the similarity scores for each identity pairing, and dividing these rankings into three strata. This procedure isolated the role of photometric variables from the effects of the distinctiveness of particular identities. Algorithm performance for these constant identity pairings varied dramatically across the groups. In a series of experiments, humans matched image pairs from the good, moderate, and poor conditions, rating the likelihood that the images were of the same person (1: sure same - 5: sure different). Algorithms were more accurate than humans in the good and moderate conditions, but were comparable to humans in the poor accuracy condition. To date, these are the most variable illumination- and appearance-based recognition conditions on which humans and machines have been compared. The finding that machines were never less accurate than humans on these challenging frontal images suggests that face recognition systems may be ready for applications with comparable difficulty. We speculate that the superiority of algorithms over humans in the less challenging conditions may be due to the algorithms' use of detailed, view-specific identity information. Humans may consider this information less important due to its limited potential for robust generalization in suboptimal viewing conditions.
Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries

A rating system provides relative measures of superiority between adversaries. We propose a novel and simple approach, which we call pi-rating, for dynamically rating Association Football teams solely on the basis of the relative discrepancies in scores through relevant match instances. The pi-rating system is applicable to any other sport where the score is considered as a good indicator for prediction purposes, as well as determining the relative performances between adversaries. In an attempt to examine how well the ratings capture a team’s performance, we have a) assessed them against two recently proposed football ELO rating variants and b) used them as the basis of a football betting strategy against published market odds. The results show that the pi-ratings outperform considerably the widely accepted ELO ratings and, perhaps more importantly, demonstrate profitability over a period of five English Premier League seasons (2007/08 to 2011/12), even allowing for the bookmakers' built-in profit margin. This is the first academic study to demonstrate profitability against market odds using such a relatively simple technique, and the resulting pi-ratings can be incorporated as parameters into other more sophisticated models in an attempt to further enhance forecasting capability.
Unsupervised learning of object semantic parts from internal states of CNNs by population encoding

We address the key question of how object part representations can be found from the internal states of CNNs that are trained for high-level tasks, such as object classification. This work provides a new unsupervised method to learn semantic parts and gives new understanding of the internal representations of CNNs. Our technique is based on the hypothesis that semantic parts are represented by populations of neurons rather than by single filters. We propose a clustering technique to extract part representations, which we call Visual Concepts. We show that visual concepts are semantically coherent in that they represent semantic parts, and visually coherent in that corresponding image patches appear very similar. Also, visual concepts provide full spatial coverage of the parts of an object, rather than a few sparse parts as is typically found in keypoint annotations. Furthermore, We treat each visual concept as part detector and evaluate it for keypoint detection using the PASCAL3D+ dataset, and for part detection using our newly annotated ImageNetPart dataset. The experiments demonstrate that visual concepts can be used to detect parts. We also show that some visual concepts respond to several semantic parts, provided these parts are visually similar. Note that our ImageNetPart dataset gives rich part annotations which cover the whole object, making it useful for other part-related applications.
A heuristic approach for detection of obfuscated malware

Obfuscated malware has become popular because of pure benefits brought by obfuscation: low cost and readily availability of obfuscation tools accompanied with good result of evading signature based anti-virus detection as well as prevention of reverse engineer from understanding malwares' true nature. Regardless obfuscation methods, a malware must deobfuscate its core code back to clear executable machine code so that malicious portion will be executed. Thus, to analyze the obfuscation pattern before unpacking provide a chance for us to prevent malware from further execution. In this paper, we propose a heuristic detection approach that targets obfuscated windows binary files being loaded into memory - prior to execution. We perform a series of static check on binary file's PE structure for common traces of a packer or obfuscation, and gauge a binary's maliciousness with a simple risk rating mechanism. As a result, a newly created process, if flagged as possibly malicious by the static screening, will be prevented from further execution. This paper explores the foundation of this research, as well as the testing methodology and current results.
Minimum-energy translational trajectory planning for battery-powered three-wheeled omni-directional mobile robots

The minimum-energy translational trajectory planning algorithm is proposed for battery-powered three-wheeled omni-directional mobile robots (TOMRs). We have chosen a practical cost function as the total energy drawn from the batteries, in order to lengthen the operational time of a mobile robot with given batteries. After establishing the dynamic equations of TOMRs, the optimal control theory is used to solve the minimum-energy trajectory, which gives the velocity profile in analytic form. Various simulations are performed and the consumed energy is compared to other velocity trajectories. Simulation results reveal that the energy saving is achieved of up to 2.4% compared with loss-minimization control, and up to 4.3% compared with conventional trapezoidal velocity profile.
Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls

Despite extensive preventive efforts, falls continue to be a major source of morbidity and mortality among elderly. Real-time detection of falls and their urgent communication to a telecare center may enable rapid medical assistance, thus increasing the sense of security of the elderly and reducing some of the negative consequences of falls. Many different approaches have been explored to automatically detect a fall using inertial sensors. Although previously published algorithms report high sensitivity (SE) and high specificity (SP), they have usually been tested on simulated falls performed by healthy volunteers. We recently collected acceleration data during a number of real-world falls among a patient population with a high-fall-risk as part of the SensAction-AAL European project. The aim of the present study is to benchmark the performance of thirteen published fall-detection algorithms when they are applied to the database of 29 real-world falls. To the best of our knowledge, this is the first systematic comparison of fall detection algorithms tested on real-world falls. We found that the SP average of the thirteen algorithms, was (mean ± std) 83.0% ± 30.3% (maximum value = 98%). The SE was considerably lower (SE = 57.0% ± 27.3%, maximum value = 82.8%), much lower than the values obtained on simulated falls. The number of false alarms generated by the algorithms during 1-day monitoring of three representative fallers ranged from 3 to 85. The factors that affect the performance of the published algorithms, when they are applied to the real-world falls, are also discussed. These findings indicate the importance of testing fall-detection algorithms in real-life conditions in order to produce more effective automated alarm systems with higher acceptance. Further, the present results support the idea that a large, shared real-world fall database could, potentially, provide an enhanced understanding of the fall process and the information needed to design and evaluate a high-performance fall detector.
Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning

This article addresses open questions of the discussions in the first SIRTEL workshop at the EC-TEL conference 2007. It argues why personal recommender systems have to be adjusted to the specific characteristics of learning in Learning Networks. Personal recommender systems strongly depend on the context or domain they operate in, and it is often not possible to take one recommender system with a specific purpose from one context and transfer it to another context or domain. The article describes a number of distinctive differences for personalised recommendation to learners when compared to recommendations for consumers. Similarities and differences for informal and formal learning are discussed and used to define the recommendation goal that recommender systems in informal learning networks have to address. The article further suggests an evaluation approach for recommender systems in
Magnetic Adder Based on Racetrack Memory

The miniaturization of integrated circuits based on complementary metal oxide semiconductor (CMOS) technology meets a significant slowdown in this decade due to several technological and scientific difficulties. Spintronic devices such as magnetic tunnel junction (MTJ) nanopillar become one of the most promising candidates for the next generation of memory and logic chips thanks to their non-volatility, infinite endurance, and high density. A magnetic processor based on spintronic devices is then expected to overcome the issue of increasing standby power due to leakage currents and high dynamic power dedicated to data moving. For the purpose of fabricating such a non-volatile magnetic processor, a new design of multi-bit magnetic adder (MA)-the basic element of arithmetic/logic unit for any processor-whose input and output data are stored in perpendicular magnetic anisotropy (PMA) domain wall (DW) racetrack memory (RM)-is presented in this paper. The proposed multi-bit MA circuit promises nearly zero standby power, instant ON/OFF capability, and smaller die area. By using an accurate racetrack memory spice model, we validated this design and simulated its performance such as speed, power and area, etc.
Practical online failure prediction for Blue Gene/P: Period-based vs event-driven

To facilitate proactive fault management in large-scale systems such as IBM Blue Gene/P, online failure prediction is of paramount importance. While many techniques have been presented for online failure prediction, questions arise regarding two commonly used approaches: period-based and event-driven. Which one has better accuracy? What is the best observation window (i.e., the time interval used to collect evidence before making a prediction)? How does the lead time (i.e., the time interval from the prediction to the failure occurrence) impact prediction arruracy? To answer these questions, we analyze and compare period-based and event-driven prediction approaches via a Bayesian prediction model. We evaluate these prediction approaches, under a variety of testing parameters, by means of RAS logs collected from a production supercomputer at Argonne National Laboratory. Experimental results show that the period-based Bayesian model and the event-driven Bayesian model can achieve up to 65.0% and 83.8% prediction accuracy, respectively. Furthermore, our sensitivity study indicates that the event-driven approach seems more suitable for proactive fault management in large-scale systems like Blue Gene/P.
DETECTING PACKED EXECUTABLES BASED ON RAW BINARY DATA

Packing an executable originally referred to the compression of the file to reduce its size on disk. Nowadays, packing also introduces encryption and anti-debug techniques to protect executables from reverse engineering. This explains why packers are extensively used in creating new malware variants which are not detected by traditional signature-based anti-malware tools. Although universal unpackers exist for extracting the executable code from packed files, they often rely on methods based on dynamic analysis, thus making them computationally expensive and time consuming. Hence, it is important to detect packed executables beforehand to avoid unnecessary computations so that only protected executables need be sent to the unpacker before further analysis. In this paper, we propose a new technique for fast identification of packed executables by analyzing only the raw binary data. We extract bigram-based features on packed and unpacked executables and use a support vector machine for training and testing. Experimental results reveal that we are able to correctly identify packed executables with a high detection rate in the range of 95%-98% for a variety of packers and crypters.
REScala: bridging between object-oriented and functional style in reactive applications

Traditionally, object-oriented software adopts the Observer pattern to implement reactive behavior. Its drawbacks are well-documented and two families of alternative approaches have been proposed, extending object-oriented languages with concepts from functional reactive and dataflow programming, respectively event-driven programming. The former hardly escape the functional setting; the latter do not achieve the declarativeness of more functional approaches.
 In this paper, we present REScala, a reactive language which integrates concepts from event-based and functional-reactive programming into the object-oriented world. REScala supports the development of reactive applications by fostering a functional declarative style which complements the advantages of object-oriented design.
Critical androgen-sensitive periods of rat penis and clitoris development

Androgen control of penis development/growth is unclear. In rats, androgen action in a foetal 'masculinisation programming window' (MPW; e15.5-e18.5)' predetermines penile length and hypospadias occurrence. This has implications for humans (e.g. micropenis). Our studies aimed to establish in rats when androgen action/administration affects development/growth of the penis and if deficits in MPW androgen action were rescuable postnatally. Thus, pregnant rats were treated with flutamide during the MPW +/- postnatal testosterone propionate (TP) treatment. To assess penile growth responsiveness, rats were treated with TP in various time windows (late foetal, neonatal through early puberty, puberty onset, or combinations thereof). Phallus length, weight, and morphology, hypospadias and anogenital distance (AGD) were measured in mid-puberty (d25) or adulthood (d90) in males and females, plus serum testosterone in adult males. MPW flutamide exposure reduced adult penile length and induced hypospadias dose-dependently; this was not rescued by postnatal TP treatment. In normal rats, foetal (e14.5-e21.5) TP exposure did not affect male penis size but increased female clitoral size. In males, TP exposure from postnatal d1-24 or at puberty (d15-24), increased penile length at d25, but not ultimately in adulthood. Foetal + postnatal TP (e14-postnatal d24) increased penile size at d25 but reduced it at d90 (due to reduced endogenous testosterone). In females, this treatment caused the biggest increase in adult clitoral size but, unlike in males, phallus size was unaffected by TP during puberty (d15-24). Postnatal TP treatment advanced penile histology at d25 to more resemble adult histology. AGD strongly correlated with final penis length. It is concluded that adult penile size depends critically on androgen action during the MPW but subsequent growth depends on later androgen exposure. Foetal and/or postnatal TP exposure does not increase adult penile size above its 'predetermined' length though its growth towards this maximum is advanced by peripubertal TP treatment.
Why should my students use AR? A comparative review of the educational impacts of augmented-reality

Augmented reality is increasingly reaching young users such as elementary-school and high-school children, as their parents and teachers become aware of the technology and its potential for education. Although research has shown that AR systems have the potential to improve student learning, the educator community does not clearly understand the educational impact of AR, nor the factors which impact the educational effectiveness of AR. In this poster, we analyse 32 publications that have previously compared learning effects of AR vs non-AR applications. We identify a list of positive and negative impacts of AR on student learning, and identify potential underlying causes for these effects. Our vision is that educational initiatives will exploit these factors, in order to realize the full potential of AR to enrich learner's lives.