nils-herrmann/clean_scirepeval huggingface dataset:
Evaluating C-RAN fronthaul functional splits in terms of network level energy and cost savings

The placement of the complete baseband processing in a centralized pool results in high data rate requirement and inflexibility of the fronthaul network, which challenges the energy and cost effectiveness of the cloud radio access network (C-RAN). Recently, redesign of the C-RAN through functional split in the baseband processing chain has been proposed to overcome these challenges. This paper evaluates, by mathematical and simulation methods, different splits with respect to network level energy and cost efficiency having in the mind the expected quality of service. The proposed mathematical model quantifies the multiplexing gains and the trade-offs between centralization and decentralization concerning the cost of the pool, fronthaul network capacity and resource utilization. The event-based simulation captures the influence of the traffic load dynamics and traffic type variation on designing an efficient fronthaul network. Based on the obtained results, we derive a principle for fronthaul dimensioning based on the traffic profile. This principle allows for efficient radio access network with respect to multiplexing gains while achieving the expected users' quality of service.
High-performance polyline intersection based spatial join on GPU-accelerated clusters

The rapid growing volumes of spatial data have brought significant challenges on developing high-performance spatial data processing techniques in parallel and distributed computing environments. Spatial joins are important data management techniques in gaining insights from large-scale geospatial data. While several distributed spatial join techniques based on spatial partitions have been implemented on top of existing Big Data systems, they are not capable of natively exploiting massively data parallel computing power provided by modern commodity Graphics Processing Units (GPUs). In this study, as an important component of our research initiative in developing high-performance spatial join techniques on GPUs, we have designed and implemented a polyline intersection based spatial join technique that is capable of exploiting massively data parallel computing power on GPUs. The proposed polyline intersection based spatial join technique is integrated into a customized lightweight distributed execution engine that natively supports spatial partitions. We empirically evaluate the performance of the proposed spatial join technique on both a standalone GPU-equipped workstation and Amazon EC2 GPU-accelerated clusters and demonstrate its high performance when comparing with the state-of-the-art.
The importance of being flexible: the ability to both enhance and suppress emotional expression predicts long-term adjustment.

Researchers have documented the consequences of both expressing and suppressing emotion using between-subjects designs. It may be argued, however, that successful adaptation depends not so much on any one regulatory process, but on the ability to flexibly enhance or suppress emotional expression in accord with situational demands. We tested this hypothesis among New York City college students in the aftermath of the September 11th terrorist attacks. Subjects' performance in a laboratory task in which they enhanced emotional expression, suppressed emotional expression, and behaved normally on different trials was examined as a prospective predictor of their adjustment across the first two years of college. Results supported the flexibility hypothesis. A regression analysis controlling for initial distress and motivation and cognitive resources found that subjects who were better able to enhance and suppress the expression of emotion evidenced less distress by the end of the second year. Memory deficits were also observed for both the enhancement and the suppression tasks, suggesting that both processes require cognitive resources.
TumorNet: Lung nodule characterization using multi-view Convolutional Neural Network with Gaussian Process

Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning. While the variation in the appearance of the nodules remains large, there is a need for a fast and robust computer aided system. In this work, we propose an end-to-end trainable multi-view deep Convolutional Neural Network (CNN) for nodule characterization. First, we use median intensity projection to obtain a 2D patch corresponding to each dimension. The three images are then concatenated to form a tensor, where the images serve as different channels of the input image. In order to increase the number of training samples, we perform data augmentation by scaling, rotating and adding noise to the input image. The trained network is used to extract features from the input image followed by a Gaussian Process (GP) regression to obtain the malignancy score. We also empirically establish the significance of different high level nodule attributes such as calcification, sphericity and others for malignancy determination. These attributes are found to be complementary to the deep multi-view CNN features and a significant improvement over other methods is obtained.
Minimizing Faulty Executions of Distributed Systems

When troubleshooting buggy executions of distributed systems, developers typically start by manually separating out events that are responsible for triggering the bug (signal) from those that are extraneous (noise). In this paper we present DEMi, a tool for automatically performing this minimization. We apply DEMi to buggy executions of two very different distributed systems, Raft and Spark, and find that it achieves up to 97% reduction of execution length within a small time budget.
Computer Go: An AI oriented survey

Since the beginning of AI, mind games have been studied as relevant application fields. Nowadays, some programs are better than human players in most classical games. Their results highlight the efficiency of AI methods that are now quite standard. Such methods are very useful to Go programs, but they do not enable a strong Go program to be built. The problems related to Computer Go require new AI problem solving methods. Given the great number of problems and the diversity of possible solutions, Computer Go is an attractive research domain for AI. Prospective methods of programming the game of Go will probably be of interest in other domains as well. The goal of this paper is to present Computer Go by showing the links between existing studies on Computer Go and different AI related domains: evaluation function, heuristic search, machine learning, automatic knowledge generation, mathematical morphology and cognitive science. In addition, this paper describes both the practical aspects of Go programming, such as program optimization, and various theoretical aspects such as combinatorial game theory, mathematical morphology, and MonteCarlo methods.
Exploring Domain Knowledge for Affective Video Content Analyses

The well-established film grammar is often used to change visual and audio elements of videos to invoke audiences' emotional experience. Such film grammar, referred to as domain knowledge, is crucial for affective video content analyses, but has not been thoroughly explored yet. In this paper, we propose a novel method to analyze video affective content through exploring domain knowledge. Specifically, take visual elements as an example, we first infer probabilistic dependencies between visual elements and emotions from the summarized film grammar. Then, we transfer the domain knowledge as constraints, and formulate affective video content analyses as a constrained optimization problem. Experiments on the LIRIS-ACCEDE database and the DEAP database demonstrate that the proposed affective content analyses method can successfully leverage well-established film grammar for better emotion classification from video content.
Revisiting Bertin Matrices: New Interactions for Crafting Tabular Visualizations

We present Bertifier, a web app for rapidly creating tabular visualizations from spreadsheets. Bertifier draws from Jacques Bertin's matrix analysis method, whose goal was to “simplify without destroying” by encoding cell values visually and grouping similar rows and columns. Although there were several attempts to bring this method to computers, no implementation exists today that is both exhaustive and accessible to a large audience. Bertifier remains faithful to Bertin's method while leveraging the power of today's interactive computers. Tables are formatted and manipulated through crossets, a new interaction technique for rapidly applying operations on rows and columns. We also introduce visual reordering, a semi-interactive reordering approach that lets users apply and tune automatic reordering algorithms in a WYSIWYG manner. Sessions with eight users from different backgrounds suggest that Bertifier has the potential to bring Bertin's method to a wider audience of both technical and non-technical users, and empower them with data analysis and communication tools that were so far only accessible to a handful of specialists.
A comprehensive model of PMOS NBTI degradation

Negative bias temperature instability has become an important reliability concern for ultra-scaled Silicon IC technology with significant implications for both analog and digital circuit design. In this paper, we construct a comprehensive model for NBTI phenomena within the framework of the standard reaction–diffusion model. We demonstrate how to solve the reaction–diffusion equations in a way that emphasizes the physical aspects of the degradation process and allows easy generalization of the existing work. We also augment this basic reaction–diffusion model by including the temperature and field-dependence of the NBTI phenomena so that reliability projections can be made under arbitrary circuit operating conditions.
Four types of context for automatic spelling correction

This paper presents an investigation on using four types of contextual information for improving the accuracy of automatic correction of single-token non-word misspellings. The task is framed as contextually-informed re-ranking of correction candidates. Immediate local context is captured by word n-grams statistics from a Web-scale language model. The second approach measures how well a candidate correction fits in the semantic fabric of the local lexical neighborhood, using a very large Distributional Semantic Model. In the third approach, recognizing a misspelling as an instance of a recurring word can be useful for reranking. The fourth approach looks at context beyond the text itself. If the approximate topic can be known in advance, spelling correction can be biased towards the topic. Effectiveness of proposed methods is demonstrated with an annotated corpus of 3,000 student essays from international high-stakes English language assessments. The paper also describes an implemented system that achieves high accuracy on this task. RÉSUMÉ. Cet article présente une enquête sur l’utilisation de quatre types d’informations contextuelles pour améliorer la précision de la correction automatique de fautes d’orthographe de mots seuls. La tâche est présentée comme un reclassement contextuellement informé. Le contexte local immédiat, capturé par statistique de mot n-grammes est modélisé à partir d’un modèle de langage à l’échelle du Web. La deuxième méthode consiste à mesurer à quel point une correction s’inscrit dans le tissu sémantique local, en utilisant un très grand modèle sémantique distributionnel. La troisième approche reconnaissant une faute d’orthographe comme une instance d’un mot récurrent peut être utile pour le reclassement. La quatrième approche s’attache au contexte au-delà du texte lui-même. Si le sujet approximatif peut être connu à l’avance, la correction orthographique peut être biaisée par rapport au sujet. L’efficacité des méthodes proposées est démontrée avec un corpus annoté de 3 000 travaux d’étudiants des évaluations internationales de langue anglaise. Le document décrit également un système mis en place qui permet d’obtenir une grande précision sur cette tâche.
A Supervised Named-Entity Extraction System for Medical Text

TOKEN LEMMA POS Chunk Capitalized Token Class Dependency Sem Group Doc Type Section CUI TUI SemGroup UMLS Categ LEXIQUE SG LEXIQUE CUI DISO TOKEN ANAT TOKEN Wiki Gold label Nails nail NNS B-NP Capitalized Word ROOT DIS heent B-C1 B-T023 B-ANAT B DISO B-ANAT B-C0027342 1 1 – O : : : O – Punct P ANAT DIS heent – – – O O O 0 0 – O No no DT B-NP Capitalized Word NMOD #NA DIS heent – – – O O O 0 0 – O bed bed NN I-NP – Word NMOD #NA DIS heent – – – O O O 1 1 – B-DISORDER abnormalities abnormality NNS I-NP – Word NMOD DISO DIS heent – – – O O O 1 0 – I-DISORDER , , , O – Punct P #NA DIS heent – – – O O O 0 0 – O lunulas lunula NNS B-NP – Word AMOD #NA DIS heent – – – O O O 0 0 – O present present JJ B-ADJP – Word APPO #NA DIS heent – – – O O O 1 1 – O , , , O – Punct P ANAT DIS heent – – – O O O 0 0 – O no no DT B-NP – Word NMOD DISO DIS heent – – – O O O 0 0 – O splinters splinter NNS I-NP – Word ROOT DIS heent B-C2 C3 B-T037 B-DISO O O O 1 0 B-DISO B-DISORDER , , , I-NP – Punct P DISO DIS heent – – – O O O 0 0 – O pulses pulse NNS I-NP – Word COORD DISO DIS heent – – – O B-PHYS B-C0391850 0 0 – O I Lexical and morphological features (e.g., capitalization, lemma)
Integrating ACT-R Cognitive Models with the Unity Game Engine

Cognitive architectures are computational frameworks that support the development of computational models of human cognitive processes. They have typically been used to advance our understanding of human cognition in specific task environments; however, they have also been used to support the development of a variety of intelligent systems and agents (e.g., cognitive robots). Although a variety of cognitive architectures are available, such as SOAR (Laird, 2012; Laird, Newell, & Rosenbloom, 1987), ACT-R (Anderson, 2007; Anderson et al., 2004) and CLARION (Sun, 2006a; Sun, 2007), the focus of the current chapter is on ACT-R. ACT-R is a rule-based system that has been widely used by cognitive scientists to model aspects of human cognitive performance. Of all the cognitive architectures that are currently available, the ACT-R architecture is probably the one that is best grounded in the cognitive psychology literature. In addition, it is one of the few cognitive architectures that attempts to make explicit predictions about the recruitment of different brain regions in cognitive processes: the structural elements of the core ACT-R architecture (i.e., its modules and buffers) map onto different regions of the human brain (Anderson, 2007), and this enables cognitive modellers to make precise predictions about the activity of brain regions at specific junctures in a cognitive process (see Anderson, Qin, Jung, & Carter, 2007).
Traceable Air Baggage Handling System Based on RFID Tags in the Airport

The RFID is not only a feasible, novel, and cost-effective candidate for daily object identification but it is also considered as a significant tool to provide traceable visibility along different stages of the aviation supply chain. In the air baggage handing application, the RFID tags are used to enhance the ability for baggage tracking, dispatching and conveyance so as to improve the management efficiency and the users’ satisfaction. We surveyed current related work and introduce the IATA RP1740c protocol used for the standard to recognize the baggage tags. One distributed aviation baggage traceable application is designed based on the RFID networks. We describe the RFID-based baggage tracking experiment in the BCIA (Beijing Capital International Airport). In this experiment the tags are sealed in the printed baggage label and the RFID readers are fixed in the certain interested positions of the BHS in the Terminal 2. We measure the accurate recognition rate and monitor the baggage’s real-time situation on the monitor’s screen. Through the analysis of the measured results within two months we emphasize the advantage of the adoption of RFID tags in this high noisy BHS environment. The economical benefits achieved by the extensive deployment of RFID in the baggage handing system are also outlined.
Optimal design and tradeoffs analysis for planar transformer in high power DC-DC converters

A planar magnetic is a low profile transformer or inductor utilizing planar windings instead of the traditional windings made of Cu-wires. In this paper, the important factors for planar transformer design including winding loss, core loss, leakage inductance and stray capacitance have been investigated individually. The tradeoffs among these factors have to be analyzed in order to achieve optimal parameters. Combined with a certain application, four typical winding arrangements have been compared to illustrate each their advantages and disadvantages. An improved interleaving structure with optimal behaviors is proposed, which constructs the top layer paralleling with the bottom layer and then in series with the other turns of the primary so that a lower magneto motive force (MMF) ratio m can be obtained as well as minimized AC resistance, leakage inductance and even stray capacitance. A 1.2-kW full-bridge DC-DC converter prototype employing the improved planar transformer structure has been constructed, over 96% efficiency is achieved and a 2.7% improvement compared to the non-interleaving structure is obtained.
A comparison of track-to-track fusion algorithms for automotive sensor fusion

In exteroceptive automotive sensor fusion, sensor data are usually only available as processed, tracked object data and not as raw sensor data. Applying a Kalman filter to such data leads to additional delays and generally underestimates the fused objectspsila covariance due to temporal correlations of individual sensor data as well as inter-sensor correlations. We compare the performance of a standard asynchronous Kalman filter applied to tracked sensor data to several algorithms for the track-to-track fusion of sensor objects of unknown correlation, namely covariance union, covariance intersection, and use of cross-covariance. For the simulation setup used in this paper, covariance intersection and use of cross-covariance turn out to yield significantly lower errors than a Kalman filter at a comparable computational load.
Device-Free Motion & Trajectory Detection via RFID

Compared with traditional methods that employ inertial sensors or wireless sensors, device-free approaches do not require that people carry devices, and they are considered a useful technique for indoor navigation and posture recognition. However, few existing methods can detect the trajectory and movements of humans at the same time. In this study, we propose a scheme called PADAR for addressing these two problems simultaneously by using passive radio frequency identification (RFID) tags but without attaching them to the human body. The idea is based on the principle of radio tomographic imaging, where the variance in a tag’s backscattered radio frequency signal strength is influenced by human movement. We integrated a commodity off-the-shelf RFID reader with a two-dimensional phased array antenna and a matrix of passive tags to evaluate the performance of our scheme. We conducted experiments in a simulated indoor environment. The experimental results showed that PADAR achieved an accuracy of over 70%.
Investigating the appropriateness of social network question asking as a resource for blind users

Recent work has shown the potential of having remote humans answer visual questions that blind users have. On the surface social networking sites (SNSs) offer an attractive free source of human-powered answers that can be personalized to the user. In this paper, we explore the potential of blind users asking visual questions to their social networks. We present the first formal study of how blind people use social networking sites via a survey of 191 blind adults. We also explore whether blind users find SNSs an appropriate venue for Q&A through a log analysis of questions asked using VizWiz Social, an iPhone app with over 5,000 users, which lets blind users ask questions to either the crowd or friends. We then report findings of a field experiment with 23 blind VizWiz Social users, which explored question asking on VizWiz Social in the presence of monetary costs for non-social sources. We find that blind people have a large presence on social networking sites, but do not see them as an appropriate venue for asking questions due to high perceived social costs.
State-Plane Analysis of Regenerative Snubber for Flyback Converters

The flyback converter is a popular topology for implementing low power and multiple output power supplies. However, the high leakage inductance of the flyback transformer causes high voltage spikes that can damage the main transistor when the switch is turned OFF. Therefore, a turn-off snubber is needed to limit the peak voltage stress. This paper presents the analysis of an energy regenerative snubber using the graphical state-plane technique. The undertaken approach yields a clear-cut design procedure for minimum switch voltage stress. Experimental evaluation of the energy regenerative snubber in comparison with other common snubbers shows that under the same voltage stress the efficiency of energy regenerative snubber has 8% improvement on average over an RCD snubber and 2% improvement over the nondissipative LC snubber.
A graph-based method of newspaper article reconstruction

The primary information units in a newspaper are the articles. Article reconstruction from newspapers including article aggregation and reading order recovery is known to be a quite challenging task due to the complexity of the multi-article page layout. In this paper, we propose a novel approach for article reconstruction using a bipartite graph framework, which models the complex relationships between text blocks as one-to-one correspondences, and accomplishes the task by finding the optimal match on this graph. During the optimization process, various information sources, including geometric layout, linguistic and semantic content, are deeply mined in the bipartite graph model to deal with the wide range of complex newspaper layouts. Moreover, quite different from the existing methods, we perform the two sub-tasks of article reconstruction in reverse order, that is, we detect the reading orders of the text blocks first and then use the reading order to aggregate blocks belonging to the same articles. Experimental results on 3312 newspaper pages with 23184 articles demonstrate that our method outperforms the state-of-the-art methods for newspaper article reconstruction. In addition, this method has been adopted in several large-scale newspaper digitalization projects.