nils-herrmann/clean_scirepeval huggingface dataset:
The MOPED framework: Object recognition and pose estimation for manipulation

We present MOPED, a framework for Multiple Object Pose Estimation and Detection that seamlessly integrates singleimage and multi-image object recognition and pose estimation in one optimized, robust, and scalable framework. We address two main challenges in computer vision for robotics: robust performance in complex scenes, and low latency for real-time operation. We achieve robust performance with Iterative Clustering Estimation (ICE), a novel algorithm that iteratively combines feature clustering with robust pose estimation. Feature clustering quickly partitions the scene and produces object hypotheses. The hypotheses are used to further refine the feature clusters, and the two steps iterate until convergence. ICE is easy to parallelize, and easily integrates singleand multi-camera object recognition and pose estimation. We also introduce a novel object hypothesis scoring function based on M-estimator theory, and a novel pose clustering algorithm that robustly handles recognition outliers. We achieve scalability and low latency with an improved feature matching algorithm for large databases, a GPU/CPU hybrid architecture that exploits parallelism at all levels, and an optimized resource scheduler. We provide extensive experimental results demonstrating state-of-the-art performance in terms of recognition, scalability, and latency in real-world robotic applications.
Using sentiment to detect bots on Twitter: Are humans more opinionated than bots?

In many Twitter applications, developers collect only a limited sample of tweets and a local portion of the Twitter network. Given such Twitter applications with limited data, how can we classify Twitter users as either bots or humans? We develop a collection of network-, linguistic-, and application-oriented variables that could be used as possible features, and identify specific features that distinguish well between humans and bots. In particular, by analyzing a large dataset relating to the 2014 Indian election, we show that a number of sentimentrelated factors are key to the identification of bots, significantly increasing the Area under the ROC Curve (AUROC). The same method may be used for other applications as well.
Splicing MPLS and OpenFlow Tunnels Based on SDN Paradigm

Software-defined networking has emerged as a promising solution for supporting dynamic network functions and intelligent applications through decoupling control plane from forwarding plane. OpenFlow is the first standardized open management interface of SDN architecture. But it is unrealistic to simply swaping out conventional networks for new infrastructure. How to integrate OpenFlow with existing networks is still a serious challenge. We propose a tunnel splicing mechanism for heterogeneous network with MPLS and OpenFlow routers. Two key mechanisms were suggested: first, abstract the underlying network devices into uniformed nodes in order to shield the details of various equipments, second, strip the manipulation of flow table and lable switch table from controller and fulfill it in an independent module. This new paradigm has been developed on Linux system and tests have been carried out in experiment networks. The emulation results proved its feasibility and efficiency.
Fast Exact Inference with a Factored Model for Natural Language Parsing

We presenta novel generati ve model for natural languagetree structuresin whichsemantic(lexical dependenc y) andsyntacticstructuresare scoredwith separatemodels.Thisfactorizationprovidesconceptual simplicity, straightforwardopportunitiesfor separatelyimproving the componentmodels,anda level of performancealreadycloseto thatof similar, non-factoredmodels.Most importantly, unlikeothermodernparsing models,thefactoredmodeladmitsanextremelyeffectiveA parsingalgorithm,which makesefficient,exactinferencefeasible.
Rationalizing Neural Predictions

Prediction without justification has limited applicability. As a remedy, we learn to extract pieces of input text as justifications – rationales – that are tailored to be short and coherent, yet sufficient for making the same prediction. Our approach combines two modular components, generator and encoder, which are trained to operate well together. The generator specifies a distribution over text fragments as candidate rationales and these are passed through the encoder for prediction. Rationales are never given during training. Instead, the model is regularized by desiderata for rationales. We evaluate the approach on multi-aspect sentiment analysis against manually annotated test cases. Our approach outperforms attention-based baseline by a significant margin. We also successfully illustrate the method on the question retrieval
A general approach to control a Positive Buck-Boost converter to achieve robustness against input voltage fluctuations and load changes

A positive buck-boost converter is a known DC- DC converter which may be controlled to act as buck or boost converter with same polarity of the input voltage. This converter has four switching states which include all the switching states of the above mentioned DC-DC converters. In addition there is one switching state which provides a degree of freedom for the positive buck-boost converter in comparison to the buck, boost, and inverting buck-boost converters. In other words the positive buck- boost converter shows a higher level of flexibility for its inductor current control compared to the other DC-DC converters. In this paper this extra degree of freedom is utilised to increase the robustness against input voltage fluctuations and load changes. To address this capacity of the positive buck-boost converter, two different control strategies are proposed which control the inductor current and output voltage against any fluctuations in input voltage and load changes. Mathematical analysis for dynamic and steady state conditions are presented in this paper and simulation results verify the proposed method.
Dyscalculia : Characteristics , Causes , and Treatments

Developmental Dyscalculia (DD) is a learning disorder affecting the ability to acquire school-level arithmetic skills, affecting approximately 3-6% of individuals. Progress in understanding the root causes of DD and how best to treat it have been impeded by lack of widespread research and variation in characterizations of the disorder across studies. However, recent years have witnessed significant growth in the field, and a growing body of behavioral and neuroimaging evidence now points to an underlying deficit in the representation and processing of numerical magnitude information as a potential core deficit in DD. An additional product of the recent progress in understanding DD is the resurgence of a distinction between ‘primary’ and ‘secondary’ developmental dyscalculia. The first appears related to impaired development of brain mechanisms for processing numerical magnitude information, while the latter refers to mathematical deficits stemming from external factors such as poor teaching, low socio-economic status, and behavioral attention problems or domain-general cognitive deficits. Increased awareness of this distinction going forward, in combination with longitudinal empirical research, offers great potential for deepening our understanding of the disorder and developing effective educational interventions.
Energy-efficient rate-adaptive GPS-based positioning for smartphones

Many emerging smartphone applications require position information to provide location-based or context-aware services. In these applications, GPS is often preferred over its alternatives such as GSM/WiFi based positioning systems because it is known to be more accurate. However, GPS is extremely power hungry. Hence a common approach is to periodically duty-cycle GPS. However, GPS duty-cycling trades-off positioning accuracy for lower energy. A key requirement for such applications, then, is a positioning system that provides accurate position information while spending minimal energy.
 In this paper, we present RAPS, rate-adaptive positioning system for smartphone applications. It is based on the observation that GPS is generally less accurate in urban areas, so it suffices to turn on GPS only as often as necessary to achieve this accuracy. RAPS uses a collection of techniques to cleverly determine when to turn on GPS. It uses the location-time history of the user to estimate user velocity and adaptively turn on GPS only if the estimated uncertainty in position exceeds the accuracy threshold. It also efficiently estimates user movement using a duty-cycled accelerometer, and utilizes Bluetooth communication to reduce position uncertainty among neighboring devices. Finally, it employs celltower-RSS blacklisting to detect GPS unavailability (e.g., indoors) and avoid turning on GPS in these cases. We evaluate RAPS through real-world experiments using a prototype implementation on a modern smartphone and show that it can increase phone lifetimes by more than a factor of 3.8 over an approach where GPS is always on.
Survey on Feature Selection for Data Reduction

The storage capabilities and advanced in data collection has led to an information load and the size of databases increases in dimensions, not only in rows but also in columns. Data reduction (DR) plays a vital role as a data prepossessing techniques in the area of knowledge discovery from the huge collection of data. Feature selection (FS) is one of the well known data reduction techniques, which deals with the reduction of attributes from the original data without affecting the main information content. Based on the training data used for different applications of knowledge discovery, FS technique falls into supervised, unsupervised. In this paper an extensive survey on supervised FS technique describing the different searching approach, methods and application areas with an outline of a comparative study is covered.
A 3D-BPP approach for optimising stowage plans and terminal productivity

This paper addresses the problem of determining stowage plans for containers in a ship, that is the so-called master bay plan problem (MBPP). MBPP is NP-complete [Botter, R.C., Brinati, M.A., 1992. Stowage container planning: A model for getting an optimal solution. IFIP Transactions B (Applications in Technology) B-5, 217–229; Avriel, M., Penn, M., Shpirer, N., 2000. Container ship stowage problem: Complexity and connection to the colouring of circle graphs. Discrete Applied Mathematics 103, 271–279]. We present a heuristic method for solving MBPP based on its relation with the three-dimensional bin packing problem (3D-BPP), where items are containers and the only bin is the ship. We look for stowage plans that take into a proper account structural and operational constraints, related to both the containers and the ship, and maximise some important terminal performance indexes, such as the effective and mean net crane productivity. Our aim is to evaluate how stowage plans can influence the performance of the quay. A validation of the proposed approach with some test cases related to containership docks at the port of Genoa (Italy) is given. The results of real instances of the problem and the comparison with a validated heuristic for MBPP, show the effectiveness of the proposed approach in producing stowage plans that minimise the total loading time and allow an efficient use of the quay equipment.
Maximum entropy models for natural language ambiguity resolution

MAXIMUM ENTROPY MODELS FOR NATURAL LANGUAGE AMBIGUITY RESOLUTION Adwait Ratnaparkhi Supervisor Professor Mitch Marcus This thesis demonstrates that several important kinds of natural language ambiguities can be resolved to state of the art accuracies using a single statistical modeling technique based on the principle of maximum entropy We discuss the problems of sentence boundary detection part of speech tagging prepo sitional phrase attachment natural language parsing and text categorization under the maximum entropy framework In practice we have found that maximum entropy models o er the following advantages State of the art Accuracy The probability models for all of the tasks discussed per form at or near state of the art accuracies or outperform competing learning algo rithms when trained and tested under similar conditions Methods which outperform those presented here require much more supervision in the form of additional human involvement or additional supporting resources Knowledge Poor Features The facts used to model the data or features are linguis tically very simple or knowledge poor but yet succeed in approximating complex linguistic relationships Reusable Software Technology The mathematics of the maximum entropy framework
Causal Interventions for Fairness

Most approaches in algorithmic fairness constrain machine learning methods so the resulting predictions satisfy one of several intuitive notions of fairness. While this may help private companies comply with non-discrimination laws or avoid negative publicity, we believe it is often too little, too late. By the time the training data is collected, individuals in disadvantaged groups have already suffered from discrimination and lost opportunities due to factors out of their control. In the present work we focus instead on interventions such as a new public policy, and in particular, how to maximize their positive effects while improving the fairness of the overall system. We use causal methods to model the effects of interventions, allowing for potential interference–each individual’s outcome may depend on who else receives the intervention. We demonstrate this with an example of allocating a budget of teaching resources using a dataset of schools in New York City.
Divide and correct: using clusters to grade short answers at scale

In comparison to multiple choice or other recognition-oriented forms of assessment, short answer questions have been shown to offer greater value for both students and teachers; for students they can improve retention of knowledge, while for teachers they provide more insight into student understanding. Unfortunately, the same open-ended nature which makes them so valuable also makes them more difficult to grade at scale. To address this, we propose a cluster-based interface that allows teachers to read, grade, and provide feedback on large groups of answers at once. We evaluated this interface against an unclustered baseline in a within-subjects study with 25 teachers, and found that the clustered interface allows teachers to grade substantially faster, to give more feedback to students, and to develop a high-level view of students' understanding and misconceptions.
Centrality in Social Networks Conceptual Clarification

The intuitive background for measures of structural centrality in social networks is reviewed aPzd existing measures are evaluated in terms of their consistency with intuitions and their interpretability. Three distinct intuitive conceptions of centrality are uncovered and existing measures are refined to embody these conceptions. Three measures are developed for each concept, one absolute and one relative measure of the ~entra~~t~~ of ~os~tio~ls in a network, and one relenting the degree of centralization of the entire network. The implications of these measures for the experimental study of small groups is examined.
Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification

We propose Adaptive Recursive Neural Network (AdaRNN) for target-dependent Twitter sentiment classification. AdaRNN adaptively propagates the sentiments of words to target depending on the context and syntactic relationships between them. It consists of more than one composition functions, and we model the adaptive sentiment propagations as distributions over these composition functions. The experimental studies illustrate that AdaRNN improves the baseline methods. Furthermore, we introduce a manually annotated dataset for target-dependent Twitter sentiment analysis.
Semantic Object Maps for robotic housework - representation, acquisition and use

In this article we investigate the representation and acquisition of Semantic Objects Maps (SOMs) that can serve as information resources for autonomous service robots performing everyday manipulation tasks in kitchen environments. These maps provide the robot with information about its operation environment that enable it to perform fetch and place tasks more efficiently and reliably. To this end, the semantic object maps can answer queries such as the following ones: “What do parts of the kitchen look like?”, “How can a container be opened and closed?”, “Where do objects of daily use belong?”, “What is inside of cupboards/drawers?”, etc. The semantic object maps presented in this article, which we call SOM+, extend the first generation of SOMs presented by Rusu et al.  in that the representation of SOM+ is designed more thoroughly and that SOM+ also include knowledge about the appearance and articulation of furniture objects. Also, the acquisition methods for SOM+ substantially advance those developed in  in that SOM+ are acquired autonomously and with low-cost (Kinect) instead of very accurate (laser-based) 3D sensors. In addition, perception methods are more general and are demonstrated to work in different kitchen environments.
Image Similarity Using Mutual Information of Regions

Mutual information (MI) has emerged in recent years as an effective similarity measure for comparing images. One drawback of MI, however, is that it is calculated on a pixel by pixel basis, meaning that it takes into account only the relationships between corresponding individual pixels and not those of each pixel’s respective neighborhood. As a result, much of the spatial information inherent in images is not utilized. In this paper, we propose a novel extension to MI called regional mutual information (RMI). This extension efficiently takes neighborhood regions of corresponding pixels into account. We demonstrate the usefulness of RMI by applying it to a real-world problem in the medical domain— intensity-based 2D-3D registration of X-ray projection images (2D) to a CT image (3D). Using a gold-standard spine image data set, we show that RMI is a more robust similarity meaure for image registration than MI.
Corporate Governance and Corporate Social Responsibility Synergies and Interrelationships

Manuscript Type: Empirical Research Questions/Issue: This paper seeks to explore the interrelationships between corporate governance (CG) and corporate social responsibility (CSR): first, theoretically, by reviewing the literature and surveying various postulations on offer; second, empirically, by investigating the conception and interpretation of this relationship in the context of a sample of firms operating in Lebanon. Accordingly, the paper seeks to highlight the increasing cross-connects or interfaces between CG and CSR, capitalizing on fresh insights from a developing country perspective.  A qualitative interpretive research methodology was adopted, drawing on in-depth interviews with the top managers of eight corporations operating in Lebanon, with the findings suggesting that the majority of managers conceive of CG as a necessary pillar for sustainable CSR. These findings are significant and interesting, implying that recent preoccupation with CG in developing countries is starting to be counterbalanced by some interest/attention to CSR, with growing appreciation of their interdependencies and the need to move beyond CG conformance toward voluntary CSR performance.  This study makes two important contributions. First, it suggests that there is a salient two-way relationship and increasing overlap between CG and CSR. While much previous literature has researched CG and CSR independently, this paper makes the case for considering them jointly and systematically. Second, the paper outlines a number of theoretical propositions that can serve as the basis for future research on the topic, particularly in developing countries, given that the data and theoretical propositions are both derived from and tailored to developing country contexts.  This study can potentially alert managers to the increasing overlap between the CG and CSR agendas and the need to exert diligent systematic efforts on both fronts. CG and CSR share more in common than previously assumed, and this needs to be accounted for by practitioners. The research can also alert policy makers in developing countries to the need to increase the vigilance and capacity of the regulatory and judicial systems in the context of CG reform and to increase institutional pressures, particularly of the coercive and normative variety to enhance CSR adoption.
Addressing the Data Sparsity Issue in Neural AMR Parsing

Neural attention models have achieved great success in different NLP tasks. However, they have not fulfilled their promise on the AMR parsing task due to the data sparsity issue. In this paper, we describe a sequence-to-sequence model for AMR parsing and present different ways to tackle the data sparsity problem. We show that our methods achieve significant improvement over a baseline neural attention model and our results are also competitive against state-of-the-art systems that do not use extra linguistic resources.
Cyber-Physical Systems in the SmartGrid

Radical changes are expected to occur in the next years in the electricity domain and the grid itself which has been almost unchanged the last 100 years. Value is created when interactions exist and this is the main thrust of the emerging SmartGrid which will heavily rely on IT technologies at several layers for monitoring and control. The basic building blocks are the existing efforts in the domain of the Internet of Things and Internet of Services, that come together with cooperation as the key enabler. The SmartGrid is a complex ecosystem of heterogeneous (cooperating) entities that interact in order to provide the envisioned functionality. Advanced business services will take advantage of the near real-time information flows among all participants. In order to realize the SmartGrid promise we will have to heavily depend on Cyber-Physical Systems (CPS) that will be able to monitor, share and manage information and actions on the business as well as the real world. CPS is seen as an integral part of the SmartGrid, hence several open issues will need to be effectively addressed.
From basic network principles to neural architecture: emergence of orientation columns.

Orientation-selective cells--cells that are selectively responsive to bars and edges at particular orientations--are a salient feature of the architecture of mammalian visual cortex. In the previous paper of this series, I showed that such cells emerge spontaneously during the development of a simple multilayered network having local but initially random feedforward connections that mature, one layer at a time, according to a simple development rule (of Hebb type). In this paper, I show that, in the presence of lateral connections between developing orientation cells, these cells self-organize into banded patterns of cells of similar orientation. These patterns are similar to the "orientation columns" found in mammalian visual cortex. No orientation preference is specified to the system at any stage, none of the basic developmental rules is specific to visual processing, and the results emerge even in the absence of visual input to the system (as has been observed in macaque monkey).
Femtocaching and device-to-device collaboration: A new architecture for wireless video distribution

We present a new architecture to handle the ongoing explosive increase in the demand for video content in wireless networks. It is based on distributed caching of the content in femtobasestations with small or non-existing backhaul capacity but with considerable storage space, called helper nodes. We also consider using the wireless terminals themselves as caching helpers, which can distribute video through device-todevice communications. This approach allows an improvement in the video throughput without deployment of any additional infrastructure. The new architecture can improve video throughput by one to two orders-of-magnitude.