nils-herrmann/clean_scirepeval huggingface dataset:
Multimodal human discourse: gesture and speech

Gesture and speech combine to form a rich basis for human conversational interaction. To exploit these modalities in HCI, we need to understand the interplay between them and the way in which they support communication. We propose a framework for the gesture research done to date, and present our work on the cross-modal cues for discourse segmentation in free-form gesticulation accompanying speech in natural conversation as a new paradigm for such multimodal interaction. The basis for this integration is the psycholinguistic concept of the coequal generation of gesture and speech from the same semantic intent. We present a detailed case study of a gesture and speech elicitation experiment in which a subject describes her living space to an interlocutor. We perform two independent sets of analyses on the video and audio data: video and audio analysis to extract segmentation cues, and expert transcription of the speech and gesture data by microanalyzing the videotape using a frame-accurate videoplayer to correlate the speech with the gestural entities. We compare the results of both analyses to identify the cues accessible in the gestural and audio data that correlate well with the expert psycholinguistic analysis. We show that "handedness" and the kind of symmetry in two-handed gestures provide effective supersegmental discourse cues.
Demystifying GPU microarchitecture through microbenchmarking

Graphics processors (GPU) offer the promise of more than an order of magnitude speedup over conventional processors for certain non-graphics computations. Because the GPU is often presented as a C-like abstraction (e.g., Nvidia's CUDA), little is known about the characteristics of the GPU's architecture beyond what the manufacturer has documented. This work develops a microbechmark suite and measures the CUDA-visible architectural characteristics of the Nvidia GT200 (GTX280) GPU. Various undisclosed characteristics of the processing elements and the memory hierarchies are measured. This analysis exposes undocumented features that impact program performance and correctness. These measurements can be useful for improving performance optimization, analysis, and modeling on this architecture and offer additional insight on the decisions made in developing this GPU.
Automatic Performance Diagnosis and Tuning in Oracle

Performance tuning in modern database systems requires a lot of expertise, is very time consuming and often misdirected. Tuning attempts often lack a methodology that has a holistic view of the database. The absence of historical diagnostic information to investigate performance issues at first occurrence exacerbates the whole tuning process often requiring that problems be reproduced before they can be correctly diagnosed. In this paper we describe how Oracle overcomes these challenges and provides a way to perform automatic performance diagnosis and tuning. We define a new measure called ‘Database Time’ that provides a common currency to gauge the performance impact of any resource or activity in the database. We explain how the Automatic Database Diagnostic Monitor (ADDM) automatically diagnoses the bottlenecks affecting the total database throughput and provides actionable recommendations to alleviate them. We also describe the types of performance measurements that are required to perform an ADDM analysis. Finally we show how ADDM plays a central role within Oracle 10g’s manageability framework to self-manage a database and provide a comprehensive tuning solution.
Social Networks, Personalized Advertising, and Privacy Controls

This paper investigates how internet users’ perception of control over their personal information affects how likely they are to click on online advertising on a social networking website. The paper uses data from a randomized field experiment that examined the effectiveness of personalizing ad text with user-posted personal information relative to generic text. The website gave users more control over their personally identifiable information in the middle of the field test. However, the website did not change how advertisers used data to target and personalize ads. Before the policy change, personalized ads did not perform particularly well. However, after this enhancement of perceived control over privacy, users were nearly twice as likely to click on personalized ads. Ads that targeted but did not use personalized text remained unchanged in effectiveness. The increase in effectiveness was larger for ads that used more unique private information to personalize their message and for target groups who were more likely to use opt-out privacy settings.
Multiparticipant chat analysis: A survey

a r t i c l e i n f o a b s t r a c t We survey research on the analysis of multiparticipant chat. Multiple research and applied communities (e.g., AI, educational, law enforcement, military) have interest in this topic. After introducing some context, we describe relevant problems and how these have been addressed using AI techniques. We also identify recent research trends and unresolved issues that could benefit from more attention.
Denial of Service via Algorithmic Complexity Attacks

We present a new class of low-bandwidth denial of service attacks that exploit algorithmic deficiencies in many common applications’ data structures. Frequently used data structures have “average-case” expected running time that’s far more efficient than the worst case. For example, both binary trees and hash tables can degenerate to linked lists with carefully chosen input. We show how an attacker can effectively compute such input, and we demonstrate attacks against the hash table implementations in two versions of Perl, the Squid web proxy, and the Bro intrusion detection system. Using bandwidth less than a typical dialup modem, we can bring a dedicated Bro server to its knees; after six minutes of carefully chosen packets, our Bro server was dropping as much as 71% of its traffic and consuming all of its CPU. We show how modern universal hashing techniques can yield performance comparable to commonplace hash functions while being provably secure against these attacks.
A family of test adequacy criteria for database-driven applications

Although a software application always executes within a particular environment, current testing methods have largely ignored these environmental factors. Many applications execute in an environment that contains a database. In this paper, we propose a family of test adequacy criteria that can be used to assess the quality of test suites for database-driven applications. Our test adequacy criteria use dataflow information that is associated with the entities in a relational database. Furthermore, we develop a unique representation of a database-driven application that facilitates the enumeration of database interaction associations. These associations can reflect an application's definition and use of database entities at multiple levels of granularity. The usage of a tool to calculate intraprocedural database interaction associations for two case study applications indicates that our adequacy criteria can be computed with an acceptable time and space overhead.
WiSLAM: Improving FootSLAM with WiFi

We address Simultaneous Localization and Mapping (SLAM) for pedestrians by means of WiFi signal strength measurements. In our system odometric data from foot mounted Inertial Measurements Units are fused with received signal strength (RSS) measurements of IEEE 802.11. To do this, we assign a probabilistic model to RSS measurements, and adopt the Bayesian framework on which FootSLAM and PlaceSLAM are based. Computational aspects are also accounted in order to provide a practical implementation of the algorithm. Simulative and experimental examples of WiSLAM are shown to underline the effectiveness of our proposal.
Skin-Color Modeling and Adaptation

This paper studies a statistical skin-color model and its adaptation. It is revealed that (1) human skin colors cluster in a small region in a color space; (2) the variance of a skin color cluster can be reduced by intensity normalization, and (3) under a certain lighting condition, a skin-color distribution can be characterized by a multivariate normal distribution in the normalized color space. We then propose an adaptive model to characterize human skin-color distributions for tracking human faces under di erent lighting conditions. The parameters of the model are adapted based on the maximum likelihood criterion. The model has been successfully applied to a real-time face tracker and other applications.
Online dating recommendations: matching markets and learning preferences

Recommendation systems for online dating have recently attracted much attention from the research community. In this paper we propose a two-side matching framework for online dating recommendations and design an Latent Dirichlet Allocation (LDA) model to learn the user preferences from the observed user messaging behavior and user profile features. Experimental results using data from a large online dating website shows that two-sided matching improves the rate of successful matches by as much as 45%. Finally, using simulated matching, we show that the LDA model can correctly capture user preferences.
Conversation Extraction in Dynamic Text Message Stream

Text message stream which is produced by Instant Messager and Internet Relay Chat poses interesting and challenging problems for information technologies. It is beneficial to extract the conversations in this kind of chatting message stream for information management and knowledge finding. However, the data in text message stream are usually very short and incomplete, and it requires efficiency to monitor thousands of continuous chat sessions. Many existing text mining methods encounter challenges. This paper focuses on the conversation extraction in dynamic text message stream. We design the dynamic representation for messages to combine the text content information and linguistic feature in message stream. A memory structure of reversed maximal similar relationship is developed for renewable assignments when grouping messages into conversations. We finally propose a double time window algorithm based on above methods to extract conversations in dynamic text message stream. Experiments on a real dataset shows that our method outperforms two baseline methods introduced in a recent related paper about 47% and 15% in terms of F measure
Probabilistic Reasoning via Deep Learning: Neural Association Models

In this paper, we propose a new deep learning approach, called neural association model (NAM), for probabilistic reasoning in artificial intelligence. We propose to use neural networks to model association between any two events in a domain. Neural networks take one event as input and compute a conditional probability of the other event to model how likely these two events are associated. The actual meaning of the conditional probabilities varies between applications and depends on how the models are trained. In this work, as two case studies, we have investigated two NAM structures, namely deep neural networks (DNN) and relationmodulated neural nets (RMNN), on several probabilistic reasoning tasks in AI, including recognizing textual entailment, triple classification in multirelational knowledge bases and common-sense reasoning. Experimental results on several popular data sets derived from WordNet, FreeBase and ConceptNet have all demonstrated that both DNN and RMNN perform equally well and they can significantly outperform the conventional methods available for these reasoning tasks. Moreover, compared with DNN, RMNN are superior in knowledge transfer, where a pre-trained model can be quickly extended to an unseen relation after observing only a few training samples.
SMM rootkits: a new breed of OS independent malware

The emergence of hardware virtualization technology has led to the development of OS independent malware such as the Virtual Machine based rootkits (VMBRs). In this paper, we draw attention to a different but related threat that exists on many commodity systems in operation today: The System Management Mode based rootkit (SMBR). System Management Mode (SMM) is a relatively obscure mode on Intel processors used for low-level hardware control. It has its own private memory space and execution environment which is generally invisible to code running outside (e.g., the Operating System). Furthermore, SMM code is completely non-preemptible, lacks any concept of privilege level, and is immune to memory protection mechanisms. These features make it a potentially attractive home for stealthy rootkits. In this paper, we present our development of a proof of concept SMM rootkit. In it, we explore the potential of System Management Mode for malicious use by implementing a chipset level keylogger and a network backdoor capable of directly interacting with the network card to send logged keystrokes to a remote machine via UDP. The rootkit hides its memory footprint and requires no changes to the existing Operating System. It is compared and contrasted with VMBRs. Finally, techniques to defend against these threats are explored. By taking an offensive perspective we hope to help security researchers better understand the depth and scope of the problems posed by an emerging class of OS independent malware.
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)

Inevitably, reading is one of the requirements to be undergone. To improve the performance and quality, someone needs to have something new every day. It will suggest you to have more inspirations, then. However, the needs of inspirations will make you searching for some sources. Even from the other people experience, internet, and many books. Books and internet are the recommended media to help you improving your quality and performance.
Automatically Labeled Data Generation for Large Scale Event Extraction

Modern models of event extraction for tasks like ACE are based on supervised learning of events from small hand-labeled data. However, hand-labeled training data is expensive to produce, in low coverage of event types, and limited in size, which makes supervised methods hard to extract large scale of events for knowledge base population. To solve the data labeling problem, we propose to automatically label training data for event extraction via world knowledge and linguistic knowledge, which can detect key arguments and trigger words for each event type and employ them to label events in texts automatically. The experimental results show that the quality of our large scale automatically labeled data is competitive with elaborately human-labeled data. And our automatically labeled data can incorporate with human-labeled data, then improve the performance of models learned from these data.
Random Early Detection Gateways for Congestion Avoidance

This paper presents a scheme to implement congestion control at the gateway nodes. The superiority of such a scheme (congestion control at gateways) over end-to-end congestion control comes from the fact that, a gateway has a global view of current link utilization (can differentiate between queuing and propagation delays). Hence they can trigger congestion avoidance mechanisms when the delays are congestion induced while letting normal traffic flow to carry on if high latency is the main culprit for end-to-end delays. Also, congestion control at gateways could potentially be part of an architecture to enforce differential treatment to flows based on certain metrics (to maintain fairness between flows). The method proposed by the authors involves maintaining a max and a min threshold of permissible average queue size at the router. Once the queue grows beyond the high mark, the algorithm starts to drop packets. When the queue length is between the two thresholds the algorithm probabilistically marks packets to inform the sending hosts of impending congestion. The intent being, the sender would throttle its own traffic and hence a congestion would be avoided. The advantage of the algorithm over droptail strategy are a) it is able to react earlier than droptail strategy and hence would lead to potentially better throughputs for all the senders (droptail would only kickoff when the queue size is above the high mark), b) because it drops packets probabilistically it doesn’t lead to global synchronization of the backoffs at all the senders. c) also because of being probabilistic it is not biased against bursty traffic. However there are a few weak points of the work, a) the algorithm gives the desired performance only when all the senders implement their own congestion control strategy. In case a sender doesn’t implement congestion control algorithm it might end up getting unfair advantage over a congestion controlled source. For example, in a scenario when the average queue size is between the two thresholds (max, min), as the controlled source would throttle its sending rate on getting marked packets while the other source would keep on pumping packets. The authors do not do any simulations to model this scenario. b) The success of the algorithm depends of correct estimation of the max and min thresholds, which in turn depends on the traffic patterns observed over the links. While the traffic patterns at the core of the Internet is stable enough to accurately set the threshold values, the traffic at edge gateways (which are nearest to the senders and hence most effective traffic controllers) is generally bursty in nature. Hence, determination of the two thresholds is a fairly nontrivial problem.
Modeling and Analysis of Harmonic Stability in an AC Power-Electronics-Based Power System

This paper addresses the harmonic stability caused by the interactions among the wideband control of power converters and passive components in an ac power-electronics-based power system. The impedance-based analytical approach is employed and expanded to a meshed and balanced three-phase network which is dominated by multiple current- and voltage-controlled inverters with LCL- and LC-filters. A method of deriving the impedance ratios for the different inverters is proposed by means of the nodal admittance matrix. Thus, the contribution of each inverter to the harmonic stability of the power system can be readily predicted through Nyquist diagrams. Time-domain simulations and experimental tests on a three-inverter-based power system are presented. The results validate the effectiveness of the theoretical approach.
Combination of texture and shape features to detect pulmonary abnormalities in digital chest X-rays

To improve detection of pulmonary and pleural abnormalities caused by pneumonia or tuberculosis (TB) in digital chest X-rays (CXRs). A method was developed and tested by combining shape and texture features to classify CXRs into two categories: TB and non-TB cases. Based on observation that radiologist interpretation is typically comparative: between left and right lung fields, the algorithm uses shape features to describe the overall geometrical characteristics of the lung fields and texture features to represent image characteristics inside them. Our algorithm was evaluated on two different datasets containing tuberculosis and pneumonia cases. Using our proposed algorithm, we were able to increase the overall performance, measured as area under the (ROC) curve (AUC) by 2.4 % over our previous work.
SoftLearn: A Process Mining Platform for the Discovery of Learning Paths

One of the most challenging issues in learning analytics is the development of techniques and tools that facilitate the evaluation of the learning activities carried out by learners. In this paper, we faced this issue through a process mining-based platform, called Soft Learn, that is able to discover complete, precise and simple learning paths from event logs. This platform has a graphical interface that allows teachers to better understand the real learning paths undertaken by learners.
Which is more important in Internet shopping, perceived price or trust?

1567-4223/$ see front matter 2011  E-mail addresses: kimhw@yonsei.ac.kr (H.-W. K (Y. Xu), sumeetgupta@ssitm.ac.in (S. Gupta). 1 Tel. 21 2501 1198 +91 788 2291621. 3 There are two types of products: search product experience product (i.e., high touch product) (Klein 19 those where there is great variation is product qua luxurious items, and apparels. Search products are th quality does not vary across stores. For example, bo quality and therefore their quality is standard do not v store. Price and trust are considered to be two important factors that influence customer purchasing decisions in Internet shopping. This paper examines the relative influence they have on online purchasing decisions for both potential and repeat customers. The knowledge of their relative impacts and changes in their relative roles over customer transaction experience is useful in developing customized sales strategies to target different groups of customers. The results of this study revealed that perceived trust exerted a stronger effect than perceived price on purchase intentions for both potential and repeat customers of an online store. The results also revealed that perceived price exerted a stronger influence on purchase decisions of repeat customers as compared to that of potential customers. Perceived trust exerted a stronger influence on purchase decisions of potential customers as compared to that of repeat customers.
Learning to Optimize Via Posterior Sampling

This paper considers the use of a simple posterior sampling algorithm to balance between exploration and exploitation when learning to optimize actions such as in multi-armed bandit problems. The algorithm, also known as Thompson Sampling, offers significant advantages over the popular upper confidence bound (UCB) approach, and can be applied to problems with finite or infinite action spaces and complicated relationships among action rewards. We make two theoretical contributions. The first establishes a connection between posterior sampling and UCB algorithms. This result lets us convert regret bounds developed for UCB algorithms into Bayes risk bounds for posterior sampling. Our second theoretical contribution is a Bayes risk bound for posterior sampling that applies broadly and can be specialized to many model classes. This bound depends on a new notion we refer to as the margin dimension, which measures the degree of dependence among action rewards. Compared to UCB algorithm Bayes risk bounds for specific model classes, our general bound matches the best available for linear models and is stronger than the best available for generalized linear models. Further, our analysis provides insight into performance advantages of posterior sampling, which are highlighted through simulation results that demonstrate performance surpassing recently proposed UCB algorithms.
Visual Link Retrieval in a Database of Paintings

This paper examines how far state-of-the-art machine vision algorithms can be used to retrieve common visual patterns shared by series of paintings. The research of such visual patterns, central to Art History Research, is challenging because of the diversity of similarity criteria that could relevantly demonstrate genealogical links. We design a methodology and a tool to annotate efficiently clusters of similar paintings and test various algorithms in a retrieval task. We show that pretrained convolutional neural network can perform better for this task than other machine vision methods aimed at photograph analysis. We also show that retrieval performance can be significantly improved by fine-tuning a network specifically for this task.