nils-herrmann/clean_scirepeval huggingface dataset:
Multimodal feature fusion for robust event detection in web videos

Combining multiple low-level visual features is a proven and effective strategy for a range of computer vision tasks. However, limited attention has been paid to combining such features with information from other modalities, such as audio and videotext, for large scale analysis of web videos. In our work, we rigorously analyze and combine a large set of low-level features that capture appearance, color, motion, audio and audio-visual co-occurrence patterns in videos. We also evaluate the utility of high-level (i.e., semantic) visual information obtained from detecting scene, object, and action concepts. Further, we exploit multimodal information by analyzing available spoken and videotext content using state-of-the-art automatic speech recognition (ASR) and videotext recognition systems. We combine these diverse features using a two-step strategy employing multiple kernel learning (MKL) and late score level fusion methods. Based on the TRECVID MED 2011 evaluations for detecting 10 events in a large benchmark set of ~45000 videos, our system showed the best performance among the 19 international teams.
Omnidirectional Mobile Robot - Design and Implementation

Industrial and technical applications of mobile robots are continuously gaining in importance. They are already widely used for surveillance, inspection and transportation tasks. A further emerging market is that of mobile entertainment robots. One of the main requirements of an autonomous mobile robot is its ability to move through the operational space, avoiding obstacles and finding its way to the next location, in order to perform its task, capabilities known as localization and navigation. In order to know where to go, the robot must have accurate knowledge of its current location. It means, it should use a great variety of sensors, external references and algorithms. In order to move in tight areas and to avoid obstacles mobile robots should have good mobility and maneuverability. These capabilities mainly depend on the wheels design. Research is continuously going on in this field, to improve the autonomous navigation capability of mobile robotic systems. This chapter introduces an omnidirectional mobile robot for educational purposes. The robot has full omnidirectional motion capabilities, thanks to its special Mecanum wheels. The present chapter provides some information about conventional and special wheels designs, mechanical design aspects of the Mecanum wheel and also of the robot, kinematic models, as well as electronics and control strategies: remote control, line follow, autonomous strategy. Thanks to its motion capabilities and to its different control possibilities, the robot discussed in this chapter could be used as an interesting educational platform. This report is the result of a research conducted at the Robotics Laboratory of the Mechanical Engineering Faculty, “Gh. Asachi” Technical University of Iasi, Romania.
Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images

Analysis-by-synthesis has been a successful approach for many tasks in computer vision, such as 6D pose estimation of an object in an RGB-D image which is the topic of this work. The idea is to compare the observation with the output of a forward process, such as a rendered image of the object of interest in a particular pose. Due to occlusion or complicated sensor noise, it can be difficult to perform this comparison in a meaningful way. We propose an approach that "learns to compare", while taking these difficulties into account. This is done by describing the posterior density of a particular object pose with a convolutional neural network (CNN) that compares observed and rendered images. The network is trained with the maximum likelihood paradigm. We observe empirically that the CNN does not specialize to the geometry or appearance of specific objects. It can be used with objects of vastly different shapes and appearances, and in different backgrounds. Compared to state-of-the-art, we demonstrate a significant improvement on two different datasets which include a total of eleven objects, cluttered background, and heavy occlusion.
On formalizing social commitments in dialogue and argumentation models using temporal defeasible logic

In this paper, we take the view that any formalization of commitments has to come together with a formalization of time, events/actions and change. We enrich a suitable formalism for reasoning about time, event/action and change in order to represent and reason about commitments. We employ a three-valued based temporal first-order non-monotonic logic (TFONL) that allows an explicit representation of time and events/action. TFONL subsumes the action languages presented in the literature and takes into consideration the frame, qualification and ramification problems, and incorporates to a domain description the set of rules governing change. It can handle protocols for the different types of dialogues such as information seeking, inquiry and negotiation. We incorporate commitments into TFONL to obtain Com-TFONL. Com-TFONL allows an agent to reason about its commitments and about other agents’ behaviour during a dialogue. Thus, agents can employ social commitments to act on, argue with and reason about during interactions with other agents. Agents may use their reasoning and argumentative capabilities in order to determine the appropriate communicative acts during conversations. Furthermore, Com-TFONL allows for an integration of commitments and arguments which helps in capturing the public aspects of a conversation and the reasoning aspects required in coherent conversations.
RAP-CLA: A Reconfigurable Approximate Carry Look-Ahead Adder

In this brief, we propose a fast yet energy-efficient reconfigurable approximate carry look-ahead adder (RAP-CLA). This adder has the ability of switching between the approximate and exact operating modes making it suitable for both error-resilient and exact applications. The structure, which is more area and power efficient than state-of-the-art reconfigurable approximate adders, is achieved by some modifications to the conventional carry look ahead adder (CLA). The efficacy of the proposed RAP-CLA adder is evaluated by comparing its characteristics to those of two state-of-the-art reconfigurable approximate adders as well as the conventional (exact) CLA in a 15 nm FinFET technology. The results reveal that, in the approximate operating mode, the proposed 32-bit adder provides up to 55% and 28% delay and power reductions compared to those of the exact CLA, respectively, at the cost of up to 35.16% error rate. It also provides up to 49% and 19% lower delay and power consumption, respectively, compared to other approximate adders considered in this brief. Finally, the effectiveness of the proposed adder on two image processing applications of smoothing and sharpening is demonstrated.
CDx: a family of real-time Java benchmarks

Java is becoming a viable platform for hard real-time computing. There are production and research real-time Java VMs, as well as applications in both military and civil sector. Technological advances and increased adoption of Real-time Java contrast significantly with the lack of real-time benchmarks. The few benchmarks that exist are either low-level synthetic micro-benchmarks, or benchmarks used internally by companies, making it difficult to independently verify and repeat reported results.
 This paper presents the x (Collision Detector) benchmark suite, an open source application benchmark suite that targets different hard and soft real-time virtual machines. x is, at its core, a real-time benchmark with a single periodic task, which implements aircraft collision detection based on simulated radar frames. The benchmark can be configured to use different sets of real-time features and comes with a number of workloads. We describe the architecture of the benchmark and characterize the workload based on input parameters.
On generalized fat trees

11 e antroduce and analyze a neu' fanialy of multiprocessor anterconnection net ulorks. called generalized fat trees, whach Include a5 specaal cases the fa t trees us( d f o r the connecizoii machznc urchztecture CM-5, p i uned butterflaes, a n d 1 aravus othei f u t trees proposed zn the laterature 7'hc gcneralazpd fut trees provide U formal unifying concept l o dcsagn and analyzt a f a t tree based archztrcturc The rrteiided generalazed f a t tree network d Y ( ; F 7 ( h , m l , ,mh ,u i l . i u h ) of htight h has n,"=, rn, !eaj processors and the znner nodes serte onhy as swatches or router5. Each non-leaf node zn l e i i t 1 i has in, rhzldren and tach non-roo1 has 70 1 parc-nl nodes. T h i generalazed fat trees p r o v i d e reqularaty, s ymmet ry iecursave 5cdabalaiy, niaxiinal fault-tolerance, logurithtiiac dzameier bzsrclzon sculabzlrty, and pernizl sirripif a lqorz thm for fault folrriint seLf-routzng and broadcastang These n e t u w k s are a l s o v e l satzle, sance they ( a n rfficzently e7nbc d rings, i n t s h e s an0 t o n , trees. pyramad, a n d hypercubcs
Creating a Large Benchmark for Open Information Extraction

Open information extraction (Open IE) was presented as an unrestricted variant of traditional information extraction. It has been gaining substantial attention, manifested by a large number of automatic Open IE extractors and downstream applications. In spite of this broad attention, the Open IE task definition has been lacking – there are no formal guidelines and no large scale gold standard annotation. Subsequently, the various implementations of Open IE resorted to small scale posthoc evaluations, inhibiting an objective and reproducible cross-system comparison. In this work, we develop a methodology that leverages the recent QA-SRL annotation to create a first independent and large scale Open IE annotation,1 and use it to automatically compare the most prominent Open IE systems.
The reappearance of a forgotten disease in the oral cavity: syphilis.

Syphilis is a sexually transmitted disease (STD) produced by Treponema pallidum, which mainly affects humans and is able to invade practically any organ in the body. Its infection facilitates the transmission of other STDs. Since the end of the last decade, successive outbreaks of syphilis have been reported in most western European countries. Like other STDs, syphilis is a notifiable disease in the European Union. In Spain, epidemiological information is obtained nationwide via the country's system for recording notifiable diseases (Spanish acronym EDO) and the national microbiological information system (Spanish acronym SIM), which compiles information from a network of 46 sentinel laboratories in twelve Spanish regions. The STDs that are epidemiologically controlled are gonococcal infection, syphilis, and congenital syphilis. The incidence of each of these diseases is recorded weekly. The information compiled indicates an increase in the cases of syphilis and gonococcal infection in Spain in recent years. According to the EDO, in 1999, the number of cases of syphilis per 100,000 inhabitants was recorded to be 1.69, which has risen to 4.38 in 2007. In this article, we review the reappearance and the evolution of this infectious disease in eight European countries, and alert dentists to the importance of a) diagnosing sexually-transmitted diseases and b) notifying the centres that control them.
A Survey on Trajectory Data Mining: Techniques and Applications

Rapid advance of location acquisition technologies boosts the generation of trajectory data, which track the traces of moving objects. A trajectory is typically represented by a sequence of timestamped geographical locations. A wide spectrum of applications can benefit from the trajectory data mining. Bringing unprecedented opportunities, large-scale trajectory data also pose great challenges. In this paper, we survey various applications of trajectory data mining, e.g., path discovery, location prediction, movement behavior analysis, and so on. Furthermore, this paper reviews an extensive collection of existing trajectory data mining techniques and discusses them in a framework of trajectory data mining. This framework and the survey can be used as a guideline for designing future trajectory data mining solutions.
View-Invariant Analysis of Cyclic Motion

This paper presents a general framework for image-based analysis of 3D repeating motions that addresses two limitations in the state of the art. First, the assumption that a motion be perfectly even from one cycle to the next is relaxed. Real repeating motions tend not to be perfectly even, i.e., the length of a cycle varies through time because of physically important changes in the scene. A generalization of period is defined for repeating motions that makes this temporal variation explicit. This representation, called the period trace, is compact and purely temporal, describing the evolution of an object or scene without reference to spatial quantities such as position or velocity. Second, the requirement that the observer be stationary is removed. Observer motion complicates image analysis because an object that undergoes a 3D repeating motion will generally not produce a repeating sequence of images. Using principles of affine invariance, we derive necessary and sufficient conditions for an image sequence to be the projection of a 3D repeating motion, accounting for changes in viewpoint and other camera parameters. Unlike previous work in visual invariance, however, our approach is applicable to objects and scenes whose motion is highly non-rigid. Experiments on real image sequences demonstrate how the approach may be used to detect several types of purely temporal motion features, relating to motion trends and irregularities. Applications to athletic and medical motion analysis are discussed.
Incident Ticket Analytics for IT Application Management Services

An important IT service outsourcing business is to resolve incidents related to IT infrastructures our clients contract our company to support. Incidents are recorded as structured and unstructured data in tickets, which contain various characteristics about the incidents including timestamps, description and resolution Analyzing such incident tickets becomes a critical task in managing the operations of the service in order to keep the operations within the agreed upon service level agreement. Ticket analytics is essential to identify anomalies and trends, as well as detect unusual patterns in the operations; such analysis is hard to do manually especially for large accounts with complex organization and scopes. This paper focuses on ticket analytics and some key statistical techniques applied in the analyses. Finally, we use real-data examples to demonstrate these techniques and discuss major challenges of ticket analyses.
Twitter Content-Based Spam Filtering

Twitter has become one of the most used social networks. And, as happens with every popular media, it is prone to misuse. In this context, spam in Twitter has emerged in the last years, becoming an important problem for the users. In the last years, several approaches have appeared that are able to determine whether an user is a spammer or not. However, these blacklisting systems cannot filter every spam message and a spammer may create another account and restart sending spam. In this paper, we propose a content-based approach to filter spam tweets. We have used the text in the tweet and machine learning and compression algorithms to filter those undesired tweets.
Generative modeling of repositories of health records for predictive tasks

Repositories of health records are collections of events with varying number and sparsity of occurrences within and among patients. Although a large number of predictive models have been proposed in the last decade, they are not yet able to simultaneously capture cross-attribute and temporal dependencies associated with these repositories. Two major streams of predictive models can be found. On one hand, deterministic models rely on compact subsets of discriminative events to anticipate medical conditions. On the other hand, generative models offer a more complete and noise-tolerant view based on the likelihood of the testing arrangements of events to discriminate a particular outcome. However, despite the relevance of generative predictive models, they are not easily extensible to deal with complex grids of events. In this work, we rely on the Markov assumption to propose new predictive models able to deal with cross-attribute and temporal dependencies. Experimental results hold evidence for the utility and superior accuracy of generative models to anticipate health conditions, such as the need for surgeries. Additionally, we show that the proposed generative models are able to decode temporal patterns of interest (from the learned lattices) with acceptable completeness and precision levels, and with superior efficiency for voluminous repositories.
An Overview and Comparison of Smooth Labeling Methods for Land-Cover Classification

An elementary piece of our prior knowledge about images of the physical world is that they are spatially smooth, in the sense that neighboring pixels are more likely to belong to the same object (class) than to different ones. The smoothness assumption becomes more important as sensor resolutions keep increasing, both because the radiometric variability within classes increases and because remote sensing is employed in more heterogeneous areas (e.g., cities), where shadow and shading effects, a multitude of materials, etc., degrade the measurement data, and prior knowledge plays a greater role. This paper gives a systematic overview of image classification methods, which impose a smoothness prior on the labels. Both local filtering-type approaches and global random field models developed in other fields of image processing are reviewed, and two new methods are proposed. Then follows a detailed experimental comparison and analysis of the presented methods, using two different aerial data sets from urban areas with known ground truth. A main message of the paper is that when classifying data of high spatial resolution, smoothness greatly improves the accuracy of the result-in our experiments up to 33%. A further finding is that global random field models outperform local filtering methods and should be more widely adopted for remote sensing. Finally, the evaluation confirms that all methods already oversmooth when most effective, pointing out that there is a need to include more and more complex prior information into the classification process.
A dynamic programming algorithm for haplotype block partitioning.

We develop a dynamic programming algorithm for haplotype block partitioning to minimize the number of representative single nucleotide polymorphisms (SNPs) required to account for most of the common haplotypes in each block. Any measure of haplotype quality can be used in the algorithm and of course the measure should depend on the specific application. The dynamic programming algorithm is applied to analyze the chromosome 21 haplotype data of Patil et al.
Learning model-free robot control by a Monte Carlo EM algorithm

We address the problem of learning robot control by model-free reinforcement learning (RL). We adopt the probabilistic model of Vlassis and Toussaint (2009) for model-free RL, and we propose a Monte Carlo EM algorithm (MCEM) for control learning that searches directly in the space of controller parameters using information obtained from randomly generated robot trajectories. MCEM is related to, and generalizes, the PoWER algorithm of Kober and Peters (2009). In the finite-horizon case MCEM reduces precisely to PoWER, but MCEM can also handle the discounted infinite-horizon case. An interesting result is that the infinite-horizon case can be viewed as a ‘randomized’ version of the finite-horizon case, in the sense that the length of each sampled trajectory is a random draw from an appropriately constructed geometric distribution. We provide some preliminary experiments demonstrating the effects of fixed (PoWER) vs randomized (MCEM) horizon length in two simulated and one real robot control tasks.
Blockchain and Smart Contracts for Insurance: Is the Technology Mature Enough?

Blockchain is receiving increasing attention from academy and industry, since it is considered a breakthrough technology that could bring huge benefits to many different sectors. In 2017, Gartner positioned blockchain close to the peak of inflated expectations, acknowledging the enthusiasm for this technology that is now largely discussed by media. In this scenario, the risk to adopt it in the wake of enthusiasm, without objectively judging its actual added value is rather high. Insurance is one the sectors that, among others, started to carefully investigate the possibilities of blockchain. For this specific sector, however, the hype cycle shows that the technology is still in the innovation trigger phase, meaning that the spectrum of possible applications has not been fully explored yet. Insurers, as with many other companies not necessarily active only in the financial sector, are currently requested to make a hard decision, that is, whether to adopt blockchain or not, and they will only know if they were right in 3–5 years. The objective of this paper is to support actors involved in this decision process by illustrating what a blockchain is, analyzing its advantages and disadvantages, as well as discussing several use cases taken from the insurance sector, which could easily be extended to other domains.
Mooshak: a Web-based multi-site programming contest system

This paper presents a new Web-based system, Mooshak, to handle programming contests. The system acts as a full contest manager as well as an automatic judge for programming contests. Mooshak innovates in a number of aspects: it has a scalable architecture that can be used from small single server contests to complex multi-site contests with simultaneous public online contests and redundancy; it has a robust data management system favoring simple procedures for storing, replicating, backing up data and failure recovery using persistent objects; it has automatic judging capabilities to assist human judges in the evaluation of programs; it has built-in safety measures to prevent users from interfering with the normal progress of contests. Mooshak is an open system implemented on the Linux operating system using the Apache HTTP server and the Tcl scripting language. This paper starts by describing the main features of the system and its architecture with reference to the automated judging, data management based on the replication of persistent objects over a network. Finally, we describe our experience using this system for managing two official programming contests.
Control of a doubly fed induction generator in a wind turbine during grid fault ride-through

This paper analyzes the ability of a doubly fed induction generator (DFIG) in a wind turbine to ride through a grid fault and the limitations to its performance. The fundamental difficulty for the DFIG in ride-through is the electromotive force (EMF) induced in the machine rotor during the fault, which depends on the dc and negative sequence components in the stator-flux linkage and the rotor speed. The investigation develops a control method to increase the probability of successful grid fault ride-through, given the current and voltage capabilities of the rotor-side converter. A time-domain computer simulation model is developed and laboratory experiments are conducted to verify the model and a control method is proposed. Case studies are then performed on a representatively sized system to define the feasibility regions of successful ride-through for different types of grid faults