nils-herrmann/clean_scirepeval huggingface dataset:
A "semi-lazy" approach to probabilistic path prediction

Path prediction is useful in a wide range of applications. Most of the existing solutions, however, are based on eager learning methods where models and patterns are extracted from historical trajectories and then used for future prediction. Since such approaches are committed to a set of statistically significant models or patterns, problems can arise in dynamic environments where the underlying models change quickly or where the regions are not covered with statistically significant models or patterns.
 We propose a "semi-lazy" approach to path prediction that builds prediction models on the fly using dynamically selected reference trajectories. Such an approach has several advantages. First, the target trajectories to be predicted are known before the models are built, which allows us to construct models that are deemed relevant to the target trajectories. Second, unlike the lazy learning approaches, we use sophisticated learning algorithms to derive accurate prediction models with acceptable delay based on a small number of selected reference trajectories. Finally, our approach can be continuously self-correcting since we can dynamically re-construct new models if the predicted movements do not match the actual ones.
 Our prediction model can construct a probabilistic path whose probability of occurrence is larger than a threshold and which is furthest ahead in term of time. Users can control the confidence of the path prediction by setting a probability threshold. We conducted a comprehensive experimental study on real-world and synthetic datasets to show the effectiveness and efficiency of our approach.
PaperPhone: understanding the use of bend gestures in mobile devices with flexible electronic paper displays

Flexible displays potentially allow for interaction styles that resemble those used in paper documents. Bending the display, e.g., to page forward, shows particular promise as an interaction technique. In this paper, we present an evaluation of the effectiveness of various bend gestures in executing a set of tasks with a flexible display. We discuss a study in which users designed bend gestures for common computing actions deployed on a smartphone-inspired flexible E Ink prototype called PaperPhone. We collected a total of 87 bend gesture pairs from ten participants and their appropriateness over twenty actions in five applications. We identified six most frequently used bend gesture pairs out of 24 unique pairs. Results show users preferred bend gestures and bend gesture pairs that were conceptually simpler, e.g., along one axis, and less physically demanding. There was a strong agreement among participants to use the same three pairs in applications: (1) side of display, up/down (2) top corner, up/down (3) bottom corner, up/down. For actions with a strong directional cue, we found strong consensus on the polarity of the bend gestures (e.g., navigating left is performed with an upwards bend gesture, navigating right, downwards). This implies that bend gestures that take directional cues into account are likely more natural to users.
Combining self-supervised learning and imitation for vision-based rope manipulation

Manipulation of deformable objects, such as ropes and cloth, is an important but challenging problem in robotics. We present a learning-based system where a robot takes as input a sequence of images of a human manipulating a rope from an initial to goal configuration, and outputs a sequence of actions that can reproduce the human demonstration, using only monocular images as input. To perform this task, the robot learns a pixel-level inverse dynamics model of rope manipulation directly from images in a self-supervised manner, using about 60K interactions with the rope collected autonomously by the robot. The human demonstration provides a high-level plan of what to do and the low-level inverse model is used to execute the plan. We show that by combining the high and low-level plans, the robot can successfully manipulate a rope into a variety of target shapes using only a sequence of human-provided images for direction.
From image-level to pixel-level labeling with Convolutional Networks

We are interested in inferring object segmentation by leveraging only object class information, and by considering only minimal priors on the object segmentation task. This problem could be viewed as a kind of weakly supervised segmentation task, and naturally fits the Multiple Instance Learning (MIL) framework: every training image is known to have (or not) at least one pixel corresponding to the image class label, and the segmentation task can be rewritten as inferring the pixels belonging to the class of the object (given one image, and its object class). We propose a Convolutional Neural Network-based model, which is constrained during training to put more weight on pixels which are important for classifying the image. We show that at test time, the model has learned to discriminate the right pixels well enough, such that it performs very well on an existing segmentation benchmark, by adding only few smoothing priors. Our system is trained using a subset of the Imagenet dataset and the segmentation experiments are performed on the challenging Pascal VOC dataset (with no fine-tuning of the model on Pascal VOC). Our model beats the state of the art results in weakly supervised object segmentation task by a large margin. We also compare the performance of our model with state of the art fully-supervised segmentation approaches.
Sequence Matching for Suspicious Activity Detection in Anti-Money Laundering

Developing effective suspicious activity detection methods has become an increasingly critical problem for governments and financial institutions in their efforts to fight money laundering. Previous anti-money laundering (AML) systems were mostly rule-based systems which suffered from low efficiency and could can be easily learned and evaded by money launders. Recently researchers have begun to use machine learning methods to solve the suspicious activity detection problem. However nearly all these methods focus on detecting suspicious activities on accounts or individual level. In this paper we propose a sequence matching based algorithm to identify suspicious sequences in transactions. Our method aims to pick out suspicious transaction sequences using two kinds of information as reference sequences: 1) individual account’s transaction history and 2) transaction information from other accounts in a peer group. By introducing the reference sequences, we can combat those who want to evade regulations by simply learning and adapting reporting criteria, and easily detect suspicious patterns. The initial results show that our approach is highly accurate.
The Supply Chain Management Processes

Page 13 Supply chain management is increasingly being recognized as the integration of key business processes across the supply chain. For example, Hammer argues that now that companies have implemented processes within the firm, they need to integrate them between firms: Streamlining cross-company processes is the next great frontier for reducing costs, enhancing quality, and speeding operations. It is where this decade’s productivity wars will be fought. The victors will be those companies that are able to take a new approach to business, working closely with partners to design and manage processes that extend across traditional corporate boundaries. They will be the ones that make the leap from efficiency to super efficiency . Monczka and Morgan also focus on the importance of process integration in supply chain management . The piece that seems to be missing from the literature is a comprehensive definition of the processes that constitute supply chain management. How can companies achieve supply chain integration if there is not a common understanding of the key business processes? It seems that in order to build links between supply chain members it is necessary for companies to implement a standard set of supply chain processes. Practitioners and educators need a common definition of supply chain management, and a shared understanding of the processes. We recommend the definition of supply chain management developed and used by The Global Supply Chain  Supply Chain Management is the integration of key business processes from end user through original suppliers that provides products, services, and information that add value for customers and other stakeholders . The Forum members identified eight key processes that need to be implemented within and across firms in the supply chain. To date, The Supply Chain Management Processes
A review of technologies for MVDC circuit breakers

Medium voltage direct current (MVDC) distribution networks have been considered for various applications, such as offshore wind farm collector systems, all-electric naval vessels, and aircraft. MVDC circuit breakers are a critical technology to directly manage faults in multi-terminal DC (MTDC) networks. However, DC current breaking is much more challenging than in AC systems because there is no natural zero-crossing of the current waveform to aid fault isolation. This paper reviews existing MVDC circuit breaker technologies and also discusses their advantages and disadvantages. This paper also introduces new topologies that can be applied in MVDC applications. The operation of several hybrid DC circuit breaker topologies with aided commutation is included. The paper illustrates that a hybrid DC circuit breaker with aided commutation can clear a fault within 2-5 msecs with low losses, this shows great potential for future MVDC applications. The implications for the practical design of commercial MVDC circuit breakers are also discussed.
The role of media violence in violent behavior.

Media violence poses a threat to public health inasmuch as it leads to an increase in real-world violence and aggression. Research shows that fictional television and film violence contribute to both a short-term and a long-term increase in aggression and violence in young viewers. Television news violence also contributes to increased violence, principally in the form of imitative suicides and acts of aggression. Video games are clearly capable of producing an increase in aggression and violence in the short term, although no long-term longitudinal studies capable of demonstrating long-term effects have been conducted. The relationship between media violence and real-world violence and aggression is moderated by the nature of the media content and characteristics of and social influences on the individual exposed to that content. Still, the average overall size of the effect is large enough to place it in the category of known threats to public health.
Context-aware citation recommendation

When you write papers, how many times do you want to make some citations at a place but you are not sure which papers to cite? Do you wish to have a recommendation system which can recommend a small number of good candidates for every place that you want to make some citations? In this paper, we present our initiative of building a context-aware citation recommendation system. High quality citation recommendation is challenging: not only should the citations recommended be relevant to the paper under composition, but also should match the local contexts of the places citations are made. Moreover, it is far from trivial to model how the topic of the whole paper and the contexts of the citation places should affect the selection and ranking of citations. To tackle the problem, we develop a context-aware approach. The core idea is to design a novel non-parametric probabilistic model which can measure the context-based relevance between a citation context and a document. Our approach can recommend citations for a context effectively. Moreover, it can recommend a set of citations for a paper with high quality. We implement a prototype system in CiteSeerX. An extensive empirical evaluation in the CiteSeerX digital library against many baselines demonstrates the effectiveness and the scalability of our approach.
Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images

We address a central problem of neuroanatomy, namely, the automatic segmentation of neuronal structures depicted in stacks of electron microscopy (EM) images. This is necessary to efficiently map 3D brain structure and connectivity. To segment biological neuron membranes, we use a special type of deep artificial neural network as a pixel classifier. The label of each pixel (membrane or nonmembrane) is predicted from raw pixel values in a square window centered on it. The input layer maps each window pixel to a neuron. It is followed by a succession of convolutional and max-pooling layers which preserve 2D information and extract features with increasing levels of abstraction. The output layer produces a calibrated probability for each class. The classifier is trained by plain gradient descent on a 512 × 512 × 30 stack with known ground truth, and tested on a stack of the same size (ground truth unknown to the authors) by the organizers of the ISBI 2012 EM Segmentation Challenge. Even without problem-specific postprocessing, our approach outperforms competing techniques by a large margin in all three considered metrics, i.e. rand error, warping error and pixel error. For pixel error, our approach is the only one outperforming a second human observer.
Evaluation of eye gaze interaction

Eye gaze interaction can provide a convenient and natural addition to user-computer dialogues. We have previously reported on our interaction techniques using eye gaze . While our techniques seemed useful in demonstration, we now investigate their strengths and weaknesses in a controlled setting. In this paper, we present two experiments that compare an interaction technique we developed for object selection based on a where a person is looking with the most commonly used selection method using a mouse. We find that our eye gaze interaction technique is faster than selection with a mouse. The results show that our algorithm, which makes use of knowledge about how the eyes behave, preserves the natural quickness of the eye. Eye gaze interaction is a reasonable addition to computer interaction and is convenient in situations where it is important to use the hands for other tasks. It is particularly beneficial for the larger screen workspaces and virtual environments of the future, and it will become increasingly practical as eye tracker technology matures.
Radio Transmitter Fingerprinting: A Steady State Frequency Domain Approach

We present a novel technique for radio transmitter identification based on frequency domain characteristics. Our technique detects the unique features imbued in a signal as it passes through a transmit chain. We are the first to propose the use of discriminatory classifiers based on steady state spectral features. In laboratory experiments, we achieve 97% accuracy at 30 dB SNR and 66% accuracy at OdB SNR based on eight identical universal software radio peripherals (USRP) transmitters. Our technique can be implemented using today's low cost high-volume receivers and requires no manual performance tuning.
A Comparison of Bus Architectures for Safety-Critical Embedded Systems

Avionics and control systems for aircraft use distributed, fault-tolerant computer systems to provide safety-critical functions such as flight and e gine control. These systems are becomingmodular, meaning that they are based on standardized architectures and components, andintegrated, meaning that some of the components are shared by different functions—of possibly different criticality levels. The modular architectures that support these functions mus t provide mechanisms for coordinating the distributed components that provide a sin gle function (e.g., distributing sensor readings and actuator commands appropriately, and a ssisting replicated components to perform the function in a fault-tolerant manner), while p rotecting functions from faults in each other. Such an architecture must tolerate hardware f aults in its own components and must provide very strong guarantees on the correctness and r eliability of its own mechanisms and services. One of the essential services provided by this kind of modula r architecture is communication of information from one distributed component to ano ther, so a (physical or logical) communication bus is one of its principal components, and th e protocols used for control and communication on the bus are among its principal mechani sms. Consequently, these architectures are often referred to as buses(or databuses ), although this term understates their complexity, sophistication, and criticality. The capabilities once found in aircraft buses are becoming a vailable in buses aimed at the automobile market, where the economies of scale ensure l ow prices. The low price of the automobile buses then renders them attractive to certai n aircraft applications—provided they can achieve the safety required. In this report, I describe and compare the architectures of t w avionics and two automobile buses in the interest of deducing principles common t o all of them, the main differences in their design choices, and the tradeoffs made. The av ionics buses considered are the Honeywell SAFEbus (the backplane data bus used in the Boe ing 777 Airplane Information Management System) and the NASA SPIDER (an architectur being developed as a demonstrator for certification under the new DO-254 guideli nes); the automobile buses considered are the TTTech Time-Triggered Architecture (TTA), recently adopted by Audi for automobile applications, and by Honeywell for avionics and ircraft control functions, and FlexRay, which is being developed by a consortium of BMW, Dai mlerChrysler, Motorola, and Philips. I consider these buses from the perspective of their fault hy potheses, mechanisms, services, and assurance.
Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs

Estimating the confidence for a link is a critical task for Knowledge Graph construction. Link prediction, or predicting the likelihood of a link in a knowledge graph based on prior state is a key research direction within this area. We propose a Latent Feature Embedding based link recommendation model for prediction task and utilize Bayesian Personalized Ranking based optimization technique for learning models for each predicate. Experimental results on largescale knowledge bases such as YAGO2 show that our approach achieves substantially higher performance than several state-of-art approaches. Furthermore, we also study the performance of the link prediction algorithm in terms of topological properties of the Knowledge Graph and present a linear regression model to reason about its expected level
Exploring Word Embedding for Drug Name Recognition

This paper describes a machine learningbased approach that uses word embedding features to recognize drug names from biomedical texts. As a starting point, we developed a baseline system based on Conditional Random Field (CRF) trained with standard features used in current Named Entity Recognition (NER) systems. Then, the system was extended to incorporate new features, such as word vectors and word clusters generated by the Word2Vec tool and a lexicon feature from the DINTO ontology. We trained the Word2vec tool over two different corpus: Wikipedia and MedLine. Our main goal is to study the effectiveness of using word embeddings as features to improve performance on our baseline system, as well as to analyze whether the DINTO ontology could be a valuable complementary data source integrated in a machine learning NER system. To evaluate our approach and compare it with previous work, we conducted a series of experiments on the dataset of SemEval-2013 Task 9.1 Drug Name Recognition.
The second-generation 600V RC-IGBT with optimized FWD

A Reverse Conducting IGBT (RC-IGBT) is a promising device to reduce a size and cost of the power module thanks to the integration of IGBT and FWD into a single chip. However, it is difficult to achieve well-balanced performance between IGBT and FWD. Indeed, the total inverter loss of the conventional RC-IGBT was not so small as the individual IGBT and FWD pair. To minimize the loss, the most important key is the improvement of reverse recovery characteristics of FWD. We carefully extracted five effective parameters to improve the FWD characteristics, and investigated the impact of these parameters by using simulation and experiments. Finally, optimizing these parameters, we succeeded in fabricating the second-generation 600V class RC-IGBT with a smaller FWD loss than the first-generation RC-IGBT.
Video Event Understanding Using Natural Language Descriptions

Human action and role recognition play an important part in complex event understanding. State-of-the-art methods learn action and role models from detailed spatio temporal annotations, which requires extensive human effort. In this work, we propose a method to learn such models based on natural language descriptions of the training videos, which are easier to collect and scale with the number of actions and roles. There are two challenges with using this form of weak supervision: First, these descriptions only provide a high-level summary and often do not directly mention the actions and roles occurring in a video. Second, natural language descriptions do not provide spatio temporal annotations of actions and roles. To tackle these challenges, we introduce a topic-based semantic relatedness (SR) measure between a video description and an action and role label, and incorporate it into a posterior regularization objective. Our event recognition system based on these action and role models matches the state-of-the-art method on the TRECVID-MED11 event kit, despite weaker supervision.
Narcissism on Facebook : Self-promotional and anti-social behavior

0191-8869/$ see front matter 2011  298 1622; fax: +1 309 298 2369. E-mail address: cj-carpenter2@wiu.edu A survey (N = 292) was conducted that measured self-promoting Facebook behaviors (e.g. posting status updates and photos of oneself, updating profile information) and several anti-social behaviors (e.g. seeking social support more than one provides it, getting angry when people do not comment on one’s status updates, retaliating against negative comments). The grandiose exhibitionism subscale of the narcissistic personality inventory was hypothesized to predict the self-promoting behaviors. The entitlement/exploitativeness subscale was hypothesized to predict the anti-social behaviors. Results were largely consistent with the hypothesis for the self-promoting behaviors but mixed concerning the anti-social behaviors. Trait self-esteem was also related in the opposite manner as the Narcissism scales to some Facebook behaviors.