nils-herrmann/clean_scirepeval huggingface dataset:
Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models

We present a new deep learning architecture (called Kdnetwork) that is designed for 3D model recognition tasks and works with unstructured point clouds. The new architecture performs multiplicative transformations and shares parameters of these transformations according to the subdivisions of the point clouds imposed onto them by kdtrees. Unlike the currently dominant convolutional architectures that usually require rasterization on uniform twodimensional or three-dimensional grids, Kd-networks do not rely on such grids in any way and therefore avoid poor scaling behavior. In a series of experiments with popular shape recognition benchmarks, Kd-networks demonstrate competitive performance in a number of shape recognition tasks such as shape classification, shape retrieval and shape part segmentation.
Strategies for Affect-Controlled Action-Selection in Soar-RL

Reinforcement learning (RL) agents can benefit from adaptive exploration/exploitation behavior, especially in dynamic environments. We focus on regulating this exploration/exploitation behavior by controlling the action-selection mechanism of RL. Inspired by psychological studies which show that affect influences human decision making, we use artificial affect to influence an agent’s action-selection. Two existing affective strategies are implemented and, in addition, a new hybrid method that combines both. These strategies are tested on ‘maze tasks’ in which a RL agent has to find food (rewarded location) in a maze. We use Soar-RL, the new RL-enabled version of Soar, as a model environment. One task tests the ability to quickly adapt to an environmental change, while the other tests the ability to escape a local optimum in order to find the global optimum. We show that artificial affect-controlled action-selection in some cases helps agents to faster adapt to changes in the environment.
Emotion in Games

Emotion has been investigated from various perspectives and across several domains within human computer interaction (HCI) including intelligent tutoring systems, interactive web applications, social media and human-robot interaction. One of the most promising and, nevertheless, challenging applications of affective computing (AC) research is within computer games. This chapter focuses on the study of emotion in the computer games domain, reviews seminal work at the crossroads of game technology, game design and affective computing and details the key phases for efficient affectbased interaction in games.
Using Statistical Testing in the Evaluation of Retrieval Experiments

The standard strategies for evaluation based on precision and recall are examined and their relative advantages and disadvantages are discussed. In particular, it is suggested that relevance feedback be evaluated from the perspective of the user. A number of different statistical tests are described for determining if differences in performance between retrieval methods are significant. These tests have often been ignored in the past because most are based on an assumption of normality which is not strictly valid for the standard performance measures. However, one can test this assumption using simple diagnostic plots, and if it is a poor approximation, there are a number of non-parametric alternatives.
Cross-Lingual Sentiment Classification with Bilingual Document Representation Learning

Cross-lingual sentiment classification aims to adapt the sentiment resource in a resource-rich language to a resource-poor language. In this study, we propose a representation learning approach which simultaneously learns vector representations for the texts in both the source and the target languages. Different from previous research which only gets bilingual word embedding, our Bilingual Document Representation Learning model BiDRL directly learns document representations. Both semantic and sentiment correlations are utilized to map the bilingual texts into the same embedding space. The experiments are based on the multilingual multi-domain Amazon review dataset. We use English as the source language and use Japanese, German and French as the target languages. The experimental results show that BiDRL outperforms the state-of-the-art methods for all the target languages.
Lateral velocity estimation based on automotive radar sensors

Automotive radar sensors are applied to measure the target range, azimuth angle and radial velocity simultaneously even in multiple target situations. The single target measured data are necessary for target tracking in advanced driver assistance systems (ADAS) e.g. in highway scenarios. In typical city traffic situations the radar measurement is also important but additionally even the lateral velocity component of each detected target such as a vehicle is of large interest in this case. It is shown in this paper that the lateral velocity of an extended target can be measured even in a mono observation situation. For an automotive radar sensor a high spectral resolution is required in this case which means the time on target should be sufficiently large
EEG-based continuous control of a game using a 3 channel motor imagery BCI: BCI game

This paper presents an overview of a multistage signal processing framework to tackle the main challenges in continuous control protocols for motor imagery based synchronous and self-paced BCIs. The BCI can be setup rapidly and automatically even when conducting an extensive search for subject-specific parameters. A new BCI-based game training paradigm which enables assessment of continuous control performance is also introduced. A range of offline results and online analysis of the new game illustrate the potential for the proposed BCI and the advantages of using the game as a BCI training paradigm.
Principles of Gestalt Psychology

When I first conceived the plan of writing this book I guessed, though I did not know, how much effort it would cost to carry it out, and what demands it would put on a potential reader. And I doubted, not rhetorically but very honestly and sincerely, whether such labour on the part of the author and the reader was justified. I was not so much troubled by the idea of writing another book on psychology in addition to the many books which have appeared during the last ten years, as by the idea of writing a book on psychology. Writing a book for publication is a social act. Is one justified in demanding co-operation of society for such an enterprise? What good can society, or a small fraction of it, at best derive from it? I tried to give an answer to this question, and when now, after having completed the book, I return to this first chapter, I find that the answer which then gave me sufficient courage to start on my long journey, has stayed with me to the end. I believed I had found a reason why a book on psychology might do some good. Psychology has split up into so many branches and schools, either ignoring or fighting each other, that even an outsider may have the impression surely strengthened by the publications. "Psychologies of 1925" and "Psychologies of 1930" that the plural "psychologies" should be substituted for the singular.
Stochastic Network Optimization with Application to Communication and Queueing Systems

 Queue stability (Chapter 2.1)  Scheduling for stability, capacity regions (Chapter 2.3)  Linear programs (Chapter 2.3, Chapter 3)  Energy optimality (Chapter 3.2)  Opportunistic scheduling (Chapter 2.3, Chapter 3, Chapter 4.6)  Lyapunov drift and optimization (Chapter 4.1.0-4.1.2, 4.2, 4.3)  Inequality constraints and virtual queues (Chapter 4.4)  Drift-plus-penalty algorithm (Chapter 4.5)  Performance and delay tradeoffs (Chapter 3.2, 4.5)  Backpressure routing (Ex. 4.16, Chapter 5.2, 5.3)
A survey on methods for colour image indexing and retrieval in image databases

Color is a feature of the great majority of content-based image retrieval systems. However the robustness, effectiveness, and efficiency of its use in image indexing are still open issues. This paper provides a comprehensive survey of the methods for color image indexing and retrieval described in the literature. In particular, image preprocessing, the features used to represent color information, and the measures adopted to compute the similarity between the features of two images are critically analyzed.
BOOMERANG: Exploiting the Semantic Gap in Trusted Execution Environments

In the past decade, we have come to rely on computers for various safety and security-critical tasks, such as securing our homes, operating our vehicles, and controlling our finances. To facilitate these tasks, chip manufacturers have begun including trusted execution environments (TEEs) in their processors, which enable critical code (e.g., cryptographic functions) to run in an isolated hardware environment that is protected from the traditional operating system (OS) and its applications. While code in the untrusted environment (e.g., Android or Linux) is forbidden from accessing any memory or state within the TEE, the code running in the TEE, by design, has unrestricted access to the memory of the untrusted OS and its applications. However, due to the isolation between these two environments, the TEE has very limited visibility into the untrusted environment’s security mechanisms (e.g., kernel vs. application memory). In this paper, we introduce BOOMERANG, a class of vulnerabilities that arises due to this semantic separation between the TEE and the untrusted environment. These vulnerabilities permit untrusted user-level applications to read and write any memory location in the untrusted environment, including security-sensitive kernel memory, by leveraging the TEE’s privileged position to perform the operations on its behalf. BOOMERANG can be used to steal sensitive data from other applications, bypass security checks, or even gain full control of the untrusted OS. To quantify the extent of this vulnerability, we developed an automated framework for detecting BOOMERANG bugs within the TEEs of popular mobile phones. Using this framework, we were able to confirm the existence of BOOMERANG on four different TEE platforms, affecting hundreds of millions of devices on the market today. Moreover, we confirmed that, in at least two instances, BOOMERANG could be leveraged to completely compromise the untrusted OS (i.e., Android). While the implications of these vulnerabilities are severe, defenses can be quickly implemented by vendors, and we are currently in contact with the affected TEE vendors to deploy adequate fixes. To this end, we evaluated the two most promising defense proposals and their inherent trade-offs. This analysis led the proposal of a novel BOOMERANG defense, addressing the major shortcomings of the existing defenses with minimal performance overhead. Our findings have been reported to and verified by the corresponding vendors, who are currently in the process of creating security patches.
A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition

Predicting student attrition is an intriguing yet challenging problem for any academic institution. Classimbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification techniques for imbalanced dataset can yield deceivingly high prediction accuracy where the overall predictive accuracy is usually driven by the majority class at the expense of having very poor performance on the crucial minority class. In this study, we compared different data balancing techniques to improve the predictive accuracy in minority class while maintaining satisfactory overall classification performance. Specifically, we tested three balancing techniques—oversampling, under-sampling and synthetic minority over-sampling (SMOTE)—along with four popular classification methods—logistic regression, decision trees, neuron networks and support vector machines. We used a large and feature rich institutional student data (between the years 2005 and 2011) to assess the efficacy of both balancing techniques as well as prediction methods. The results indicated that the support vector machine combined with SMOTE data-balancing technique achieved the best classification performance with a 90.24% overall accuracy on the 10-fold holdout sample. All three data-balancing techniques improved the prediction accuracy for the minority class. Applying sensitivity analyses on developed models, we also identified the most important variables for accurate prediction of student attrition. Application of these models has the potential to accurately predict at-risk students and help reduce student dropout rates.
Market Structure and Productivity : A Concrete Example ∗ Chad

Many studies have documented large and persistent productivity differences across producers, even within narrowly defined industries. This paper both extends and departs from the past literature, which focused on technological explanations for these differences, by proposing that demand-side features also play a role in creating the observed productivity variation. The specific mechanism investigated here is the effect of spatial substitutability in the product market. When producers are densely clustered in a market, it is easier for consumers to switch between suppliers (making the market in a certain sense more competitive). Relatively inefficient producers find it more difficult to operate profitably as a result. Substitutability increases truncate the productivity distribution from below, resulting in higher minimum and average productivity levels as well as less productivity dispersion. The paper presents a model that makes this process explicit and empirically tests it using data from U.S. ready-mixed concrete plants, taking advantage of geographic variation in substitutability created by the industry’s high transport costs. The results support the model’s predictions and appear robust. Markets with high demand density for ready-mixed concrete—and thus high concrete plant densities—have higher lower-bound and average productivity levels and exhibit less productivity dispersion among their producers.
Factors influencing healthy eating habits among college students: an application of the health belief model.

Poor eating habits are an important public health issue that has large health and economic implications. Many food preferences are established early, but because people make more and more independent eating decisions as they move through adolescence, the transition to independent living during the university days is an important event. To study the phenomenon of food selection, the heath belief model was applied to predict the likelihood of healthy eating among university students. Structural equation modeling was used to investigate the validity of the health belief model (HBM) among 194 students, followed by gender-based analyses. The data strongly supported the HBM. Social change campaign implications are discussed.
Improving Native Language Identification with TF-IDF Weighting

This paper presents a Native Language Identification (NLI) system based on TF-IDF weighting schemes and using linear classifiers support vector machines, logistic regressions and perceptrons. The system was one of the participants of the 2013 NLI Shared Task in the closed-training track, achieving 0.814 overall accuracy for a set of 11 native languages. This accuracy was only 2.2 percentage points lower than the winner’s performance. Furthermore, with subsequent evaluations using 10-fold cross-validation (as given by the organizers) on the combined training and development data, the best average accuracy obtained is 0.8455 and the features that contributed to this accuracy are the TF-IDF of the combined unigrams and bigrams of words.
Predicting Vulnerable Software Components through N-Gram Analysis and Statistical Feature Selection

Vulnerabilities need to be detected and removed from software. Although previous studies demonstrated the usefulness of employing prediction techniques in deciding about vulnerabilities of software components, the accuracy and improvement of effectiveness of these prediction techniques is still a grand challenging research question. This paper proposes a hybrid technique based on combining N-gram analysis and feature selection algorithms for predicting vulnerable software components where features are defined as continuous sequences of token in source code files, i.e., Java class file. Machine learning-based feature selection algorithms are then employed to reduce the feature and search space. We evaluated the proposed technique based on some Java Android applications, and the results demonstrated that the proposed technique could predict vulnerable classes, i.e., software components, with high precision, accuracy and recall.
PERCEIVED INTEGRITY OF TRANSFORMATIONAL LEADERS IN ORGANISATIONAL SETTINGS

The ethical nature of transformational leadership has been hotly debated. This debate is demonstrated in the range of descriptors that have been used to label transformational leaders including narcissistic, manipulative, and self-centred, but also ethical, just and effective. Therefore, the purpose of the present research was to address this issue directly by assessing the statistical relationship between perceived leader integrity and transformational leadership using the Perceived Leader Integrity Scale (PLIS) and the Multi-Factor Leadership Questionnaire (MLQ). In a national sample of 1354 managers a moderate to strong positive relationship was found between perceived integrity and the demonstration of transformational leadership behaviours. A similar relationship was found between perceived integrity and developmental exchange leadership. A systematic leniency bias was identified when respondents rated subordinates vis-à-vis peer ratings. In support of previous findings, perceived integrity was also found to correlate positively with leader and organisational effectiveness measures.
Database analytics acceleration using FPGAs

Business growth and technology advancements have resulted in growing amounts of enterprise data. To gain valuable business insight and competitive advantage, businesses demand the capability of performing real-time analytics on such data. This, however, involves expensive query operations that are very time consuming on traditional CPUs. Additionally, in traditional database management systems (DBMS), the CPU resources are dedicated to mission-critical transactional workloads. Offloading expensive analytics query operations to a co-processor can allow efficient execution of analytics workloads in parallel with transactional workloads.
 In this paper, we present a Field Programmable Gate Array (FPGA) based acceleration engine for database operations in analytics queries. The proposed solution provides a mechanism for a DBMS to seamlessly harness the FPGA compute power without requiring any changes in the application or the existing data layout. Using a software-programmed query control block, the accelerator can be tailored to execute different queries without reconfiguration. Our prototype is implemented in a PCIe-attached FPGA system and is integrated into a commercial DBMS platform. The results demonstrate up to 94% CPU savings on real customer data compared to the baseline software cost with up to an order of magnitude speedup in the offloaded computations and up to 6.2x improvement in end-to-end performance.
Neural Sequence Chunkers

This paper addresses the problem of learning tòdivide and conquer' by meaningful hierarchical adaptive decomposition of temporal sequences. This problem is relevant for time-series analysis as well as for goal-directed learning, particularily if event sequences tend to have hierarchical temporal structure. The rst neural systems for recursively chunking sequences are described. These systems are based on a principle called thèprinciple of history compression'. This principle essentially says: As long as a predictor is able to predict future environmental inputs from previous ones, no additional knowledge can be obtained by observing these inputs in reality. Only unexpected inputs deserve attention. A focus is on a class of 2-network systems which try to collapse a self-organizing (possibly multi-level) hierarchy of temporal predictors into a single recurrent network. Only those input events that were not expected by the rst recurrent net are transferred to the second recurrent net. Therefore the second net receives a reduced discription of the input history. It tries to develop internal representations for`higher-level' temporal structure. These internal representations in turn serve to create additional training signals for the rst net, thus helping the rst net to create longer and longer`chunks' for the second net. Experiments show that chunking systems can be superior to the conventional training algorithms for recurrent nets. 1 OUTLINE Section 2 motivates the search for sequence-composing systems by describing major drawbacks of`conventional' learning algorithms for recurrent networks with time-varying inputs and outputs. Section 3 describes a simple observation which is essential for the rest of this paper: It describes thèprinciple of history compression'. This principle essentially says: As long as a predictor is able to predict future environmental inputs from previous ones, no additional knowledge can be obtained by observing these inputs in reality. Only unexpected inputs deserve attention. This principle is of particular interest if typical event sequences have hierarchical temporal structure. Basic schemes for constructing sequence chunking systems based on the principle of history compression are described. Section 4 then describes on-line and o-line versions of a particular 2-network chunking system which tries to collapse a self-organizing (possibly multi-level) predictor hierarchy into a single recurrent network (the automatizer). The idea is to feed everything that is unexpected into a `higher-level' recurrent net (the chunker). Since the expected things can be derived from the unexpected things by the automatizer, the chunker is fed with a reduced description of the input history. The chunker has a …
Totem-Pole Power-Factor-Correction Converter under Critical-Conduction-Mode Interleaved Operation

This paper proposes a new power-factor-correction (PFC) topology, and explains its operation principle, its control mechanism, related application problems followed by experimental results. In this proposed topology, critical-conduction-mode (CRM) interleaved technique is applied to a bridgeless PFC in order to achieve high efficiency by combining benefits of each topology. This application is targeted toward low to middle power applications that normally employs continuous-conductionmode boost converter. key words: PFC, Interleaved, critical-conduction-mode, totem-pole