nils-herrmann/clean_scirepeval huggingface dataset:
Video anomaly detection and localization using hierarchical feature representation and Gaussian process regression

This paper presents a hierarchical framework for detecting local and global anomalies via hierarchical feature representation and Gaussian process regression. While local anomaly is typically detected as a 3D pattern matching problem, we are more interested in global anomaly that involves multiple normal events interacting in an unusual manner such as car accident. To simultaneously detect local and global anomalies, we formulate the extraction of normal interactions from training video as the problem of efficiently finding the frequent geometric relations of the nearby sparse spatio-temporal interest points. A codebook of interaction templates is then constructed and modeled using Gaussian process regression. A novel inference method for computing the likelihood of an observed interaction is also proposed. As such, our model is robust to slight topological deformations and can handle the noise and data unbalance problems in the training data. Simulations show that our system outperforms the main state-of-the-art methods on this topic and achieves at least 80% detection rates based on three challenging datasets.
7-2011 Linking Ethical Leadership to Employee Performance : The Roles of Leader-Member Exchange , Self-Efficacy , and Organizational Identification

This research investigated the link between ethical leadership and performance using data from the People’s Republic of China. Consistent with social exchange, social learning, and social identity theories, we examined leader–member exchange (LMX), self-efficacy, and organizational identification as mediators of the ethical leadership to performance relationship. Results from 72 supervisors and 201 immediate direct reports revealed that ethical leadership was positively and significantly related to employee performance as rated by their immediate supervisors and that this relationship was fully mediated by LMX, self-efficacy, and organizational identification, controlling for procedural fairness. We discuss implications of our findings for theory and practice.
LORELEI Language Packs: Data, Tools, and Resources for Technology Development in Low Resource Languages

In this paper, we describe the textual linguistic resources in nearly 3 dozen languages being produced by Linguistic Data Consortium for DARPA’s LORELEI (Low Resource Languages for Emergent Incidents) Program. The goal of LORELEI is to improve the performance of human language technologies for low-resource languages and enable rapid re-training of such technologies for new languages, with a focus on the use case of deployment of resources in sudden emergencies such as natural disasters. Representative languages have been selected to provide broad typological coverage for training, and surprise incident languages for testing will be selected over the course of the program. Our approach treats the full set of language packs as a coherent whole, maintaining LORELEI-wide specifications, tag sets and guidelines, while allowing for adaptation to the specific needs created by each language. Each representative language corpus, therefore, both stands on its own as a resource for the specific language and forms part of a large multilingual resource for broader cross-language technology development.
Finding Good Conversations Online: The Yahoo News Annotated Comments Corpus

This work presents a dataset and annotation scheme for the new task of identifying “good” conversations that occur online, which we call ERICs: Engaging, Respectful, and/or Informative Conversations. We develop a taxonomy to reflect features of entire threads and individual comments which we believe contribute to identifying ERICs; code a novel dataset of Yahoo News comment threads (2.4k threads and 10k comments) and 1k threads from the Internet Argument Corpus; and analyze the features characteristic of ERICs. This is one of the largest annotated corpora of online human dialogues, with the most detailed set of annotations. It will be valuable for identifying ERICs and other aspects of argumentation, dialogue, and discourse.
Augmented vehicle tracking under occlusions for decision-making in autonomous driving

This paper reports on an algorithm to support autonomous vehicles in reasoning about occluded regions of their environment to make safe, reliable decisions. In autonomous driving scenarios, other traffic participants are often occluded from sensor measurements by buildings or large vehicles like buses or trucks, which makes tracking dynamic objects challenging.We present a method to augment standard dynamic object trackers with means to 1) estimate the occluded state of other traffic agents and 2) robustly associate the occluded estimates with new observations after the tracked object reenters the visible region of the sensor horizon. We perform occluded state estimation using a dynamics model that accounts for the driving behavior of traffic agents and a hybrid Gaussian mixture model (hGMM) to capture multiple hypotheses over discrete behavior, such as driving along different lanes or turning left or right at an intersection. Upon new observations, we associate them to existing estimates in terms of the Kullback-Leibler divergence (KLD). We evaluate the proposed method in simulation and using a real-world traffic-tracking dataset from an autonomous vehicle platform. Results show that our method can handle significantly prolonged occlusions when compared to a standard dynamic object tracking system.
Die Stacking (3D) Microarchitecture

3D die stacking is an exciting new technology that increases transistor density by vertically integrating two or more die with a dense, high-speed interface. The result of 3D die stacking is a significant reduction of interconnect both within a die and across dies in a system. For instance, blocks within a microprocessor can be placed vertically on multiple die to reduce block to block wire distance, latency, and power. Disparate Si technologies can also be combined in a 3D die stack, such as DRAM stacked on a CPU, resulting in lower power higher BW and lower latency interfaces, without concern for technology integration into a single process flow. 3D has the potential to change processor design constraints by providing substantial power and performance benefits. Despite the promising advantages of 3D, there is significant concern for thermal impact. In this research, we study the performance advantages and thermal challenges of two forms of die stacking: Stacking a large DRAM or SRAM cache on a microprocessor and dividing a traditional microarchitecture between two die in a stack  It is shown that a 32MB 3D stacked DRAM cache can reduce the cycles per memory access of a twothreaded RMS benchmark on average by 13% and as much as 55% while increasing the peak temperature by a negligible 0.08ºC. Off-die BW and power are also reduced by 66% on average. It is also shown that a 3D floorplan of a high performance microprocessor can simultaneously reduce power 15% and increase performance 15% with a small 14ºC increase in peak temperature. Voltage scaling can reach neutral thermals with a simultaneous 34% power reduction and 8% performance improvement.
A new initialization method for categorical data clustering

In clustering algorithms, choosing a subset of representative examples is very important in data set. Such ''exemplars " can be found by randomly choosing an initial subset of data objects and then iteratively refining it, but this works well only if that initial choice is close to a good solution. In this paper, based on the frequency of attribute values, the average density of an object is defined. Furthermore, a novel ini-tialization method for categorical data is proposed, in which the distance between objects and the density of the object is considered. We also apply the proposed initialization method to k-modes algorithm and fuzzy k-modes algorithm. Experimental results illustrate that the proposed initialization method is superior to random initialization method and can be applied to large data sets for its linear time complexity with respect to the number of data objects. Clustering data based on a measure of similarity is a critical step in scientific data analysis and in engineering systems. A common method is to use data to learn a set of centers such that the sum of squared errors between objects and their nearest centers is small (Brendan & Delbert, 2007). At present, the popular partition clustering technique usually begins with an initial set of randomly selected exemplars and iteratively refines this set so as to decrease the sum of squared errors. Due to the simpleness, random initiali-zation method has been widely used. However, these clustering algorithms need to be rerun many times with different initializa-tions in an attempt to find a good solution. Furthermore, random initialization method works well only when the number of clusters is small and chances are good that at least one random initializa-tion is close to a good solution. Therefore, how to choose initial cluster centers is extremely important as they have a direct impact on the formation of final clusters. Based on the difference in data type, selection of initial cluster centers mainly can be classified into numeric data and categorical data. Aiming at numeric data, several attempts have been reported to solve the cluster initialization to date, few researches are concerned with initialization of categorical data. Huang (1998) introduced two initial mode selection methods for k-modes algorithm. The first method selects the first k distinct objects from the data set as the initial k-modes. The second method assigns the most frequent categories equally to the initial k-modes. Though the second method …
Placeless Place-Recognition

Place recognition is a core competency for any visual simultaneous localization and mapping system. Identifying previously visited places enables the creation of globally accurate maps, robust relocalization, and multi-user mapping. To match one place to another, most state-of-the-art approaches must decide a priori what constitutes a place, often in terms of how many consecutive views should overlap, or how many consecutive images should be considered together. Unfortunately, depending on thresholds such as these, limits their generality to different types of scenes. In this paper, we present a placeless place recognition algorithm using a novel vote-density estimation technique that avoids heuristically discretizing the space. Instead, our approach considers place recognition as a problem of continuous matching between image streams, automatically discovering regions of high vote density that represent overlapping trajectory segments. The resulting algorithm has a single free parameter and all remaining thresholds are set automatically using well-studied statistical tests. We demonstrate the efficiency and accuracy of our methodology on three outdoor sequences: A comprehensive evaluation against ground-truth from publicly available datasets shows that our approach outperforms several state-of-the-art algorithms for place recognition.
Design and experimental demonstration of 1.35 kV SiC super junction Schottky diode

This paper presents the first functional SiC super junction devices, SiC SJ Schottky diodes, which substantially improve the trade-off between the breakdown voltage and specific on-resistance in SiC power devices. Processes for fabricating SJ structures by using a trench-etching-and-sidewall-implant method has been developed and a functional SJ Schottky diode has been demonstrated based on this processing method. The measured cell blocking voltage was 1350V, which achieves 95% of the simulated blocking voltage for the ideally-charge-balanced SJ structure. The measured device specific on-resistance was 0.92mΩ·cm2. The SJ drift region specific on-resistance as low as 0.32 mΩ·cm2 was obtained after subtracting the substrate resistance.
Rapid development of a Latvian speech-to-text system

This paper describes the development of a Latvian speech-to-text (STT) system at LIMSI within the Quaero project. One of the aims of the speech processing activities in the Quaero project is to cover all official European languages. However, for some of the languages only very limited, if any, training resources are available via corpora agencies such as LDC and ELRA. The aim of this study was to show the way, taking Latvian as example, an STT system can be rapidly developed without any transcribed training data. Following the scheme proposed in this paper, the Latvian STT system was developed in about a month and obtained a word error rate of 20% on broadcast news and conversation data in the Quaero 2012 evaluation campaign.
An Enhanced Algorithm to Predict a Future Crime using Data Mining

Concern about national security has increased after the 26/11 Mumbai attack. In this paper we look at the use of missing value and clustering algorithm for a data mining approach to help predict the crimes patterns and fast up the process of solving crime. We will concentrate on MV algorithm and Apriori algorithm with some enhancements to aid in the process of filling the missing value and identification of crime patterns. We applied these techniques to real crime data. We also use semisupervised learning technique in this paper for knowledge discovery from the crime records and to help increase the predictive accuracy. General Terms Crime data mining, MV Algorithm, Apriori Algorithm
A Modular 1 mm$^{3}$ Die-Stacked Sensing Platform With Low Power I$^{2}$C Inter-Die Communication and Multi-Modal Energy Harvesting

A 1.0 mm3 general-purpose sensor node platform with heterogeneous multi-layer structure is proposed. The sensor platform benefits from modularity by allowing the addition/removal of IC layers. A new low power I2C interface is introduced for energy efficient inter-layer communication with compatibility to commercial I2C protocols. A self-adapting power management unit is proposed for efficient battery voltage down conversion for wide range of battery voltages and load current. The power management unit also adapts itself by monitoring energy harvesting conditions and harvesting sources and is capable of harvesting from solar, thermal and microbial fuel cells. An optical wakeup receiver is proposed for sensor node programming and synchronization with 228 pW standby power. The system also includes two processors, timer, temperature sensor, and low-power imager. Standby power of the system is 11 nW.
Dynamic task scheduling for linear algebra algorithms on distributed-memory multicore systems

This paper presents a dynamic task scheduling approach to executing dense linear algebra algorithms on multicore systems (either shared-memory or distributed-memory). We use a task-based library to replace the existing linear algebra subroutines such as PBLAS to transparently provide the same interface and computational function as the ScaLAPACK library. Linear algebra programs are written with the task-based library and executed by a dynamic runtime system. We mainly focus our runtime system design on the metric of performance scalability. We propose a distributed algorithm to solve data dependences without process cooperation. We have implemented the runtime system and applied it to three linear algebra algorithms: Cholesky, LU, and QR factorizations. Our experiments on both shared-memory machines (16, 32 cores) and distributed-memory machines (1024 cores) demonstrate that our runtime system is able to achieve good scalability. Furthermore, we provide analytical analysis to show why the tiled algorithms are scalable and the expected execution time.
Jointly Learning Word Representations and Composition Functions Using Predicate-Argument Structures

We introduce a novel compositional language model that works on PredicateArgument Structures (PASs). Our model jointly learns word representations and their composition functions using bagof-words and dependency-based contexts. Unlike previous word-sequencebased models, our PAS-based model composes arguments into predicates by using the category information from the PAS. This enables our model to capture longrange dependencies between words and to better handle constructs such as verbobject and subject-verb-object relations. We verify this experimentally using two phrase similarity datasets and achieve results comparable to or higher than the previous best results. Our system achieves these results without the need for pretrained word vectors and using a much smaller training corpus; despite this, for the subject-verb-object dataset our model improves upon the state of the art by as much as ∼10% in relative performance.
Assessing Customer Perceptions of Website Service Quality in Digital Marketing Environments

The e-commerce literature has rarely addressed the measurement of customer perceptions of website service quality in digital marketing environments. It is argued that the current SERVQUAL and IS-SERVQUAL instruments need to be refined and validated to fit the digital marketing environment, as they are targeted primarily towards either traditional retailing or information systems contexts. This article validates and refines a comprehensive model and instrument for measuring customer-perceived service quality of websites that market digital products and services. After a discussion of the conceptualization and operationalization of the service quality construct, the procedure used in modifying items, collecting data, and validating a multiple-item scale is described. Subsequently, evidence of reliability and validity on the basis of analyzing data from a quota sample of 260 adult respondents is presented. Implications for practice and research are then explored. Finally, this paper concludes by discussing limitations that could be addressed in future studies. The final EC-SERVQUAL instrument with good reliability and validity will be essential to the development and testing of e-business theories, and provide researchers with a common framework for explaining, justifying, and comparing differences across results.
Translating keyword commands into executable code

Modern applications provide interfaces for scripting, but many users do not know how to write script commands. However, many users are familiar with the idea of entering keywords into a web search engine. Hence, if a user is familiar with the vocabulary of an application domain, we anticipate that they could write a set of keywords expressing a command in that domain. For instance, in the web browsing domain, a user might enter <B>click search button</B>. We call expressions of this form keyword commands, and we present a novel approach for translating keyword commands directly into executable code. Our prototype of this system in the web browsing domain translates <B>click search button</B> into the Chickenfoot code <B>click(findButton("search"))</B>. This code is then executed in the context of a web browser to carry out the effect. We also present an implementation of this system in the domain of Microsoft Word. A user study revealed that subjects could use keyword commands to successfully complete 90% of the web browsing tasks in our study without instructions or training. Conversely, we would expect users to complete close to 0% of the tasks if they had to guess the underlying JavaScript commands with no instructions or training.
Memory Forensics for QQ from a Live System

Our paper details the techniques to collect sensitive information of the QQ client, which is the most popular instant messaging (IM) in China. We have managed to acquire the contact list, the QQ account, the chats records, the QQ discussion group, the display names and the contents of network notepad. They are of great interest to the examiners. Besides, as the techniques we use to search for process are able to reveal terminated and hidden processes, we are very likely to find sensitive information as long as somebody has logged in the QQ client. What’s more, we propose the method of reconstructing the process space by integrating paging file into memory dump file. We have reconstructed the process space of the QQ client in this way and managed to narrow down the scale of sensitive information about QQ.
Accurate and Interpretable Bayesian MARS for Traffic Flow Prediction

Current research on traffic flow prediction mainly concentrates on generating accurate prediction results based on intelligent or combined algorithms but ignores the interpretability of the prediction model. In practice, however, the interpretability of the model is equally important for traffic managers to realize which road segment in the road network will affect the future traffic state of the target segment in a specific time interval and when such an influence is expected to happen. In this paper, an interpretable and adaptable spatiotemporal Bayesian multivariate adaptive-regression splines (ST-BMARS) model is developed to predict short-term freeway traffic flow accurately. The parameters in the model are estimated in the way of Bayesian inference, and the optimal models are obtained using a Markov chain Monte Carlo (MCMC) simulation. In order to investigate the spatial relationship of the freeway traffic flow, all of the road segments on the freeway are taken into account for the traffic prediction of the target road segment. In our experiments, actual traffic data collected from a series of observation stations along freeway Interstate 205 in Portland, OR, USA, are used to evaluate the performance of the model. Experimental results indicate that the proposed interpretable ST-BMARS model is robust and can generate superior prediction accuracy in contrast with the temporal MARS model, the parametric model autoregressive integrated moving averaging (ARIMA), the state-of-the-art seasonal ARIMA model, and the kernel method support vector regression.
Finding Low-rank Solutions to Matrix Problems, Efficiently and Provably

A rank-r matrix X ∈ Rm×n can be written as a product UV >, where U ∈ Rm×r and V ∈ Rn×r. One could exploit this observation in optimization: e.g., consider the minimization of a convex function f(X) over rank-r matrices, where the scaffold of rank-r matrices is modeled via the factorization in U and V variables. Such heuristic has been widely used before for specific problem instances, where the solution sought is (approximately) low-rank. Though such parameterization reduces the number of variables and is more efficient in computational speed and memory requirement (of particular interest is the case r min{m,n}), it comes at a cost: f(UV >) becomes a non-convex function w.r.t. U and V . In this paper, we study such parameterization in optimization of generic convex f and focus on first-order, gradient descent algorithmic solutions. We propose an algorithm we call the Bi-Factored Gradient Descent (BFGD) algorithm, an efficient first-order method that operates on the U, V factors. We show that when f is smooth, BFGD has local sublinear convergence, and linear convergence when f is both smooth and strongly convex. Moreover, for several key applications, we provide simple and efficient initialization schemes that provide approximate solutions good enough for the above convergence results to hold.