nils-herrmann/clean_scirepeval huggingface dataset:
Applications of Multilingual Text Retrieval

The recent enormous increase in the use of networked information access and on-line databases has led to more databases being available in languages other than English. The Center for Intelligent Information Retrieval (CIIR) at the University of Massachusetts is involved in a variety of industrial, government, and digital library applications which have a need for multilingual text retrieval. Most information retrieval research, however, has been evaluated using English databases and queries, and relatively little is known about how well advanced statistical techniques that incorporate ranking and term weighting perform in different languages. We describe our experience with a range of projects involving text retrieval in Spanish, Japanese and Chinese. The issues covered by these projects include document representation techniques such as morphology and segmentation, query formulation and expansion techniques, relevance feedback, and comparisons of retrieval effectiveness with English databases. The results indicate that advanced statistical techniques are effective in a wide range of languages, and that new languages can be incorporated with only moderate effort.
Host Fingerprinting and Tracking on the Web: Privacy and Security Implications

Many web services aim to track clients as a basis for analyzing their behavior and providing personalized services. Despite much debate regarding the collection of client information, there have been few quantitative studies that analyze the effectiveness of host-tracking and the associated privacy risks. In this paper, we perform a large-scale study to quantify the amount of information revealed by common host identifiers. We analyze month-long anonymized datasets collected by the Hotmail web-mail service and the Bing search engine, which include millions of hosts across the global IP address space. In this setting, we compare the use of multiple identifiers, including browser information, IP addresses, cookies, and user login IDs. We further demonstrate the privacy and security implications of host-tracking in two contexts. In the first, we study the causes of cookie churn in web services, and show that many returning users can still be tracked even if they clear cookies or utilize private browsing. In the second, we show that host-tracking can be leveraged to improve security. Specifically, by aggregating information across hosts, we uncover a stealthy malicious attack associated with over 75,000 bot accounts that forward cookies to distributed locations. This work was done while Ting-Fang was an intern at Microsoft Research. Martı́n Abadi is also affiliated with the University of California,
Environments and System Types of Virtual Reality Technology in STEM: a Survey

Virtual Reality (VR) technology has been used widely today in Science, Technology, Engineering and Mathematics (STEM) fields. The VR is emerging computer interface distinguished by high degrees of immersion, trustworthy, and interaction. The goal of VR is making the user believe, as much as possible, that he is within the computergenerated environment. The VR has become one of the important technologies to be discussed regarding its applications, usage, and its different types that can achieve huge benefits in the real world. This survey paper introduces detail information about VR systems and requirements to build correct VR environment. Moreover, this work presents a comparison between system types of VR. Then, it presents the tools and software used for building VR environments. After that, we epitomize a road of the map for selecting appropriate VR system according to the field of applications. Finally, we introduce the conclusion and future predictions to develop the VR systems. Virtual reality; 3D graphics; immersion; 3D images; navigation; multimedia
Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing

We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that allows programmers to perform in-memory computations on large clusters while retaining the fault tolerance of data flow models like MapReduce. RDDs are motivated by two types of applications that current data flow systems handle inefficiently: iterative algorithms, which are common in graph applications and machine learning, and interactive data mining tools. In both cases, keeping data in memory can improve performance by an order of magnitude. To achieve fault tolerance efficiently, RDDs provide a highly restricted form of shared memory: they are read-only datasets that can only be constructed through bulk operations on other RDDs. However, we show that RDDs are expressive enough to capture a wide class of computations, including MapReduce and specialized programming models for iterative jobs such as Pregel. Our implementation of RDDs can outperform Hadoop by 20× for iterative jobs and can be used interactively to search a 1 TB dataset with latencies of 5–7 seconds.
Aggregate Characterization of User Behavior in Twitter and Analysis of the Retweet Graph

Most previous analysis of Twitter user behavior has focused on individual information cascades and the social followers graph, in which the nodes for two users are connected if one follows the other. We instead study aggregate user behavior and the retweet graph with a focus on quantitative descriptions. We find that the lifetime tweet distribution is a type-II discrete Weibull stemming from a power law hazard function, that the tweet rate distribution, although asymptotically power law, exhibits a lognormal cutoff over finite sample intervals, and that the inter-tweet interval distribution is a power law with exponential cutoff. The retweet graph is small-world and scale-free, like the social graph, but less disassortative and has much stronger clustering. These differences are consistent with it better capturing the real-world social relationships of and trust between users than the social graph. Beyond just understanding and modeling human communication patterns and social networks, applications for alternative, decentralized microblogging systems---both predicting real-word performance and detecting spam---are discussed.
Capturing and analyzing low-level events from the code editor

In this paper, we present FLUORITE, a publicly available event logging plug-in for Eclipse which captures all of the low-level events when using the Eclipse code editor. FLUORITE captures not only what types of events occurred in the code editor, but also more detailed information such as the inserted and deleted text and the specific parameters for each command. This enables the detection of many usage patterns that could otherwise not be recognized, such as "typo correction" that requires knowing that the entered text is immediately deleted and replaced. Moreover, the snapshots of each source code file that has been opened during the session can be completely reproduced using the collected information. We also provide analysis and visualization tools which report various statistics about usage patterns, and we provide the logs in an XML format so others can write their own analyzers. FLUORITE can be used for not only evaluating existing tools, but also for discovering issues that motivate new tools.
MiMaze, a 3D Multi-Player Game on the Internet

This paper describes the design, implementation, and evaluation of MiMaze, a distributed 3D multiplayer game on the Internet. The major contribution of this work is to provide the first experimental results for this new type of application. An original fully distributed communication architecture has been designed for MiMaze. It uses multicast communication system based on RTP/UDP/IP, and a distributed synchronization mechanisms to guarantee the consistency of the game, regardless of heterogeneous network delay. This paper provides evaluation results on the Mbone. The experimental evaluation shows that the major problem will not be with real-time interaction, but with scalability.
Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation

This paper analyzes recent literature in the search for trends in business intelligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in order to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelligence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining procedure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or denial. There is also a relevant interest in bankruptcy and fraud prediction. ∗Corresponding author (S. Moro). Email addresses: scmoro@gmail.com (Sérgio Miguel Carneiro Moro), pcortez@dsi.uminho.pt (Paulo Alexandre Ribeiro Cortez), paulo.rita@iscte.pt (Paulo Miguel Rasquinho Ferreira Rita)  2015 Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of articles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research.
Strategic human resource management ( SHRM ) practices and its effect on financial performance : evidence from some selected scheduled private commercial banks in Bangladesh

Formulation and execution of Strategic Human Resource Management (SHRM) practices and its effect on perceived financial performance of any organization is a burning issue in the globalized competitive business era. This study aims to find out the relationship between SHRM and financial performance to ensure the sustainability and competitive advantage of the selected scheduled private commercial banks in Bangladesh. The research has been conducted on managers as sample of some private commercial banks during the period of January to November 2012 to collect the primary data. To evaluate the financial performance researchers have used annual reports of 2011 and 2012, journals, web sites etc. as secondary source. Survey research findings indicate that strategic integration and development of HRM were practiced to a full extent in the sampled firms. From the cross sectional analysis, the financial performance indicators show that the capital adequacy was mostly at satisfactory level compared with eh industry average. The quality of assets loan varies from bank to bank but most of them are performing at the desired level. Management efficiency was out-performing the standard in most of the cases. The profitability indicators ratio was also better than the average of private commercial banks. The result presented in this study suggests practicing intensive SHRM so that improved financial performance can be asserted to sustain in the competitive environment.
A large-scale hierarchical multi-view RGB-D object dataset

Over the last decade, the availability of public image repositories and recognition benchmarks has enabled rapid progress in visual object category and instance detection. Today we are witnessing the birth of a new generation of sensing technologies capable of providing high quality synchronized videos of both color and depth, the RGB-D (Kinect-style) camera. With its advanced sensing capabilities and the potential for mass adoption, this technology represents an opportunity to dramatically increase robotic object recognition, manipulation, navigation, and interaction capabilities. In this paper, we introduce a large-scale, hierarchical multi-view object dataset collected using an RGB-D camera. The dataset contains 300 objects organized into 51 categories and has been made publicly available to the research community so as to enable rapid progress based on this promising technology. This paper describes the dataset collection procedure and introduces techniques for RGB-D based object recognition and detection, demonstrating that combining color and depth information substantially improves quality of results.
Many regression algorithms, one unified model: A review

Regression is the process of learning relationships between inputs and continuous outputs from example data, which enables predictions for novel inputs. The history of regression is closely related to the history of artificial neural networks since the seminal work of Rosenblatt (1958). The aims of this paper are to provide an overview of many regression algorithms, and to demonstrate how the function representation whose parameters they regress fall into two classes: a weighted sum of basis functions, or a mixture of linear models. Furthermore, we show that the former is a special case of the latter. Our ambition is thus to provide a deep understanding of the relationship between these algorithms, that, despite being derived from very different principles, use a function representation that can be captured within one unified model. Finally, step-by-step derivations of the algorithms from first principles and visualizations of their inner workings allow this article to be used as a tutorial for those new to regression.
Prosody-Based Automatic Segmentation of Speech into Sentences and Topics

A crucial step in processing speech audio data for informati onextraction, topicdetection, or browsing/playback is to segment the input into sentence and topic units. Speech segm entation is challenging, since the cues typically present for segmenting text (headers, paragraphs, punctuation) ar e absent in spoken language. We investigate the use of prosody (informationgleaned from the timing and melody of s peech) for these tasks. Using decision tree and hidden Markov modeling techniques, we combine prosodic cues with w ord-based approaches, and evaluate performance on two speech corpora, Broadcast News and Switchboard. Resu lts show that the prosodic model alone performs on par with, or better than, word-based statistical languag e models—for both true and automatically recognized words in news speech. The prosodic model achieves comparabl e performance with significantly less training data, and requires no hand-labeling of prosodic events. Across ta sks nd corpora, we obtain a significant improvement over word-only models using a probabilistic combination of prosodic and lexical information. Inspection reveals that the prosodic models capture language-independent bou dary indicators described in the literature. Finally, cue usage is task and corpus dependent. For example, pause and pi tch features are highly informative for segmenting news speech, whereas pause, duration and word-based cues do minate for natural conversation.
Highlighting interventions and user differences: informing adaptive information visualization support

There is increasing evidence that the effectiveness of information visualization techniques can be impacted by the particular needs and abilities of each user. This suggests that it is important to investigate information visualization systems that can dynamically adapt to each user. In this paper, we address the question of how to adapt. In particular, we present a study to evaluate a variety of visual prompts, called "interventions", that can be performed on a visualization to help users process it. Our results show that some of the tested interventions perform better than a condition in which no intervention is provided, both in terms of task performance as well as subjective user ratings. We also discuss findings on how intervention effectiveness is influenced by individual differences and task complexity.
vSlicer: latency-aware virtual machine scheduling via differentiated-frequency CPU slicing

Recent advances in virtualization technologies have made it feasible to host multiple virtual machines (VMs) in the same physical host and even the same CPU core, with fair share of the physical resources among the VMs. However, as more VMs share the same core/CPU, the CPU access latency experienced by each VM increases substantially, which translates into longer I/O processing latency perceived by I/O-bound applications. To mitigate such impact while retaining the benefit of CPU sharing, we introduce a new class of VMs called latency-sensitive VMs (LSVMs), which achieve better performance for I/O-bound applications while maintaining the same resource share (and thus cost) as other CPU-sharing VMs. LSVMs are enabled by vSlicer, a hypervisor-level technique that schedules each LSVM more frequently but with a smaller micro time slice. vSlicer enables more timely processing of I/O events by LSVMs, without violating the CPU share fairness among all sharing VMs. Our evaluation of a vSlicer prototype in Xen shows that vSlicer substantially reduces network packet round-trip times and jitter and improves application-level performance. For example, vSlicer doubles both the connection rate and request processing throughput of an Apache web server; reduces a VoIP server's upstream jitter by 62%; and shortens the execution times of Intel MPI benchmark programs by half or more.
Handbook of Constraint Programming

Syntax We distinguish between two different kinds of constraints: built-in (pre-defined) constraints which are solved by a built-in constraint solver, and CHR (user-defined) constraintswhich are defined by the rules in a CHR program. Built-in const rain s include syntactic equality=, true, andfalse. This distinction allows to embed and utilize existing constraint solvers as well as side-effect-free host langua ge statements. Built-in constraint solvers are considered as black-boxes in whose behavior is t rusted and that do not need to be modified or inspected. The solvers for the built-in constr ain s can be written in CHR itself, giving rise to a hierarchy of solvers [87]. A CHR programis a finite set of rules. There are three kinds of rules: Simplification rule: Name@ H ⇔ C B Propagation rule: Name@ H ⇒ C B Simpagation rule: Name@ H \H ′ ⇔ C B Nameis an optional, unique identifier of a rule, theadH , H ′ is a non-empty conjunction of CHR constraints, the guardC is a conjunction of built-in constraints, and the bodyB is a goal. Agoal is a conjunction of built-in and CHR constraints. A trivial g uard expression “ true” can be omitted from a rule. Simpagation rules abbreviate simplification rules of the fo rm H ∧H ′ ⇔ C H ∧B, so there is no further need to discuss them separately. Operational Semantics At runtime, a CHR program is provided with an initial state an d will be executed until either no more rules are applicable or a contradiction occur s. The operational semantics of CHR is given by a transition sys tem . LetP be a CHR program. We define the transition relation 7→ by two computation steps (transitions), one for each kind of CHR rule. Statesare goals, i.e., conjunctions of built-in and CHR constraints. States are also called (constraint) stores . In the figure, all upper case letters are meta-variables that stand for conjunctions of cons trai ts. The constraint theory CT defines the semantics of the built-in constraints. Gbi denotes the built-in constraints of G. 2Integrating deduction and abduction, bottom-up and top-do wn execution, forward and backward chaining, tabulation and integrity constraints. 26 1. Constraints in Procedural and Concurrent Languages
Polarization imaging reflectometry in the wild

We present a novel approach for on-site acquisition of surface reflectance for planar, spatially varying, isotropic samples in uncontrolled outdoor environments. Our method exploits the naturally occurring linear polarization of incident and reflected illumination for this purpose. By rotating a linear polarizing filter in front of a camera at three different orientations, we measure the polarization reflected off the sample and combine this information with multi-view analysis and inverse rendering in order to recover per-pixel, high resolution reflectance and surface normal maps. Specifically, we employ polarization imaging from two near orthogonal views close to the Brewster angle of incidence in order to maximize polarization cues for surface reflectance estimation. To the best of our knowledge, our method is the first to successfully extract a complete set of reflectance parameters with passive capture in completely uncontrolled outdoor settings. To this end, we analyze our approach under the general, but previously unstudied, case of incident partial linear polarization (due to the sky) in order to identify the strengths and weaknesses of the method under various outdoor conditions. We provide practical guidelines for on-site acquisition based on our analysis, and demonstrate high quality results with an entry level DSLR as well as a mobile phone.
Some challenges in forensic fingerprint classification and interpretation

The usage of fingerprints is no longer limited to crime investigation. And automated fingerprint identification systems have been developed for a variety of applications. However, in law enforcement agency manual categorization and analysis are still an indispensible part of fingerprint operation mainly for the following two reasons: (1) Automated fingerprint classification and identification systems are far from perfection. They can help to expedite the identification process, but cannot completely replace human fingerprint experts in terms of identification accuracy; (2) Fingerprint classification can help speed up the automated searching process as fingerprint databases become overwhelmingly large. In this paper, we review existing classification systems, including Henry, Vucetich, Battley, and NCIC classification systems, and look into the challenges of classifying certain ambiguous fingerprints and interpreting latent prints. By reviewing literature and a representative case of fingerprint misidentification, we analyze the objective and subjective factors contributing to erroneous and inconsistent identification.
Background subtraction: Experiments and improvements for ViBe

Motion detection plays an important role in most video based applications. One of the many possible ways to detect motion consists in background subtraction. This paper discusses experiments led for a particular background subtraction technique called ViBe. This technique models the background with a set of samples for each pixel and compares new frames, pixel by pixel, to determine if a pixel belongs to the background or to the foreground. In its original version, the scope of ViBe is limited to background modeling. In this paper, we introduce a series of modifications that alter the working of ViBe, like the inhibition of propagation around internal borders or the distinction between the updating and segmentation masks, or process the output, for example by some operations on the connected components. Experimental results obtained for video sequences provided on the workshop site validate the improvements of the proposed modifications.