nils-herrmann/clean_scirepeval huggingface dataset:
Discriminant kernel and regularization parameter learning via semidefinite programming

Regularized Kernel Discriminant Analysis (RKDA) performs linear discriminant analysis in the feature space via the kernel trick. The performance of RKDA depends on the selection of kernels. In this paper, we consider the problem of learning an optimal kernel over a convex set of kernels. We show that the kernel learning problem can be formulated as a semidefinite program (SDP) in the binary-class case. We further extend the SDP formulation to the multi-class case. It is based on a key result established in this paper, that is, the multi-class kernel learning problem can be decomposed into a set of binary-class kernel learning problems. In addition, we propose an approximation scheme to reduce the computational complexity of the multi-class SDP formulation. The performance of RKDA also depends on the value of the regularization parameter. We show that this value can be learned automatically in the framework. Experimental results on benchmark data sets demonstrate the efficacy of the proposed SDP formulations.
Bone graphs: Medial shape parsing and abstraction

1077-3142/$ see front matter 2011  A doi:10.1016/j.cviu.2010.12.011 ⇑ Corresponding author. E-mail address: dmac@cs.toronto.edu (D. Macrini) The recognition of 3-D objects from their silhouettes demands a shape representation which is stable with respect to minor changes in viewpoint and articulation. This can be achieved by parsing a silhouette into parts and relationships that do not change across similar object views. Medial descriptions, such as skeletons and shock graphs, provide part-based decompositions but suffer from instabilities. As a result, similar shapes may be represented by dissimilar part sets. We propose a novel shape parsing approach which is based on identifying and regularizing the ligature structure of a medial axis, leading to a bone graph, a medial abstraction which captures a more stable notion of an object’s parts. Our experiments show that it offers improved recognition and pose estimation performance in the presence of within-class deformation over the shock graph.
Interval Stabbing Problems in Small Integer Ranges

Given a set I of n intervals, a stabbing query consists of a point q and asks for all intervals in I that contain q. The Interval Stabbing Problem is to find a data structure that can handle stabbing queries efficiently. We propose a new, simple and optimal approach for different kinds of interval stabbing problems in a static setting where the query points and interval ends are in {1, . . . , O(n)}.  interval stabbing, interval intersection, rank space, static, discrete, point enclosure
Clustering intrusion detection alarms to support root cause analysis

It is a well-known problem that intrusion detection systems overload their human operators by triggering thousands of alarms per day. This paper presents a new approach for handling intrusion detection alarms more efficiently. Central to this approach is the notion that each alarm occurs for a reason, which is referred to as the alarm's root causes. This paper observes that a few dozens of rather persistent root causes generally account for over 90% of the alarms that an intrusion detection system triggers. Therefore, we argue that alarms should be handled by identifying and removing the most predominant and persistent root causes. To make this paradigm practicable, we propose a novel alarm-clustering method that supports the human analyst in identifying root causes. We present experiments with real-world intrusion detection alarms to show how alarm clustering helped us identify root causes. Moreover, we show that the alarm load decreases quite substantially if the identified root causes are eliminated so that they can no longer trigger alarms in the future.
ProfileDroid: multi-layer profiling of android applications

The Android platform lacks tools for assessing and monitoring apps in a systematic way. This lack of tools is particularly problematic when combined with the open nature of Google Play, the main app distribution channel. As our key contribution, we design and implement ProfileDroid, a comprehensive, multi-layer system for monitoring and profiling apps. Our approach is arguably the first to profile apps at four layers: (a) static, or app specification, (b) user interaction, (c) operating system, and (d) network. We evaluate 27 free and paid Android apps and make several observations: (a) we identify discrepancies between the app specification and app execution, (b) free versions of apps could end up costing more than their paid counterparts, due to an order of magnitude increase in traffic, (c) most network traffic is not encrypted, (d) apps communicate with many more sources than users might expect---as many as 13, and (e) we find that 22 out of 27 apps communicate with Google during execution. ProfileDroid is the first step towards a systematic approach for (a) generating cost-effective but comprehensive app profiles, and (b) identifying inconsistencies and surprising behaviors.
Aligning 3D models to RGB-D images of cluttered scenes

The goal of this work is to represent objects in an RGB-D scene with corresponding 3D models from a library. We approach this problem by first detecting and segmenting object instances in the scene and then using a convolutional neural network (CNN) to predict the pose of the object. This CNN is trained using pixel surface normals in images containing renderings of synthetic objects. When tested on real data, our method outperforms alternative algorithms trained on real data. We then use this coarse pose estimate along with the inferred pixel support to align a small number of prototypical models to the data, and place into the scene the model that fits best. We observe a 48% relative improvement in performance at the task of 3D detection over the current state-of-the-art [34], while being an order of magnitude faster.
Budget-Optimal Task Allocation for Reliable Crowdsourcing Systems

Crowdsourcing systems, in which numerous tasks are electronically distributed to numerous “information piece-workers”, have emerged as an effective paradigm for human-powered solving of large scale problems in domains such as image classification, data entry, optical character recognition, recommendation, and proofreading. Because these low-paid workers can be unreliable, nearly all such systems must devise schemes to increase confidence in their answers, typically by assigning each task multiple times and combining the answers in an appropriate manner, e.g. majority voting. In this paper, we consider a general model of such crowdsourcing tasks and pose the problem of minimizing the total price (i.e., number of task assignments) that must be paid to achieve a target overall reliability. We give a new algorithm for deciding which tasks to assign to which workers and for inferring correct answers from the workers’ answers. We show that our algorithm, inspired by belief propagation and low-rank matrix approximation, significantly outperforms majority voting and, in fact, is optimal through comparison to an oracle that knows the reliability of every worker. Further, we compare our approach with a more general class of algorithms which can dynamically assign tasks. By adaptively deciding which questions to ask to the next arriving worker, one might hope to reduce uncertainty more efficiently. We show that, perhaps surprisingly, the minimum price necessary to achieve a target reliability scales in the same manner under both adaptive and non-adaptive scenarios. Hence, our non-adaptive approach is order-optimal under both scenarios. This strongly relies on the fact that workers are fleeting and can not be exploited. Therefore, architecturally, our results suggest that building a reliable worker-reputation system is essential to fully harnessing the potential of adaptive designs. ∗Computer Science and Artificial Intelligence Laboratory and Department of EECS at Massachusetts Institute of Technology.  karger@mit.edu †Department of Industrial and Enterprise Systems Engineering at University of Illinois at Urbana-Champaign. Email: swoh@illinois.edu ‡Laboratory for Information and Decision Systems and Department of EECS at Massachusetts Institute of Technology.  devavrat@mit.edu. This work was supported in parts by NSF EMT project, AFOSR Complex Networks project and Army Research Office under MURI Award 58153-MA-MUR. 1 ar X iv :1 11 0. 35 64 v4 [ cs .L G ] 2 6 M ar 2 01 3
Empirical Power Computation Using SAS ® for Schuirmann ’ s Two One-Sided Tests Procedure in Clinical Pharmacokinetic Drug-Drug Interaction Studies

Drug-drug interaction studies are becoming increasingly crucial in drug development. An FDA survey of recent new drug application submissions showed that 70% of drug-drug interaction studies were conducted using fixedsequence designs. Schuirmann’s Two One-sided Tests (TOST) procedure is the FDA preferred statistical method for evaluating drug-drug interaction. However, calculating the exact power of the TOST procedure generally requires sophisticated numerical integration. Monte Carlo simulation is a good alternative approach to the power calculation if a closed algerbraic formula is not available. This paper presents a SAS Macro to compute the empirical power of the TOST procedure under a fixed-sequence design. Empirical power curve plot and numerical table can be produced through this SAS macro.
Increasing the breakdown capability of superjunction power MOSFETs at the edge of the active region

When superjunction power MOSFETs operate near the rim of the safe operating area, avalanche breakdown can occur in the transition region between the active cell array and the edge termination. Numerical device simulations confirmed this and revealed local charge imbalances, created by irregularities in the superjunction doping pattern, as major cause. Based on the simulation results, we proposed optimized transitions of the superjunction doping pattern from the active cell array to the edge termination. Numerical device simulations as well as experiments demonstrated the enhanced breakdown capability of these transition regions.
Linear spatial pyramid matching using sparse coding for image classification

Recently SVMs using spatial pyramid matching (SPM) kernel have been highly successful in image classification. Despite its popularity, these nonlinear SVMs have a complexity O(n2 ~ n3) in training and O(n) in testing, where n is the training size, implying that it is nontrivial to scaleup the algorithms to handle more than thousands of training images. In this paper we develop an extension of the SPM method, by generalizing vector quantization to sparse coding followed by multi-scale spatial max pooling, and propose a linear SPM kernel based on SIFT sparse codes. This new approach remarkably reduces the complexity of SVMs to O(n) in training and a constant in testing. In a number of image categorization experiments, we find that, in terms of classification accuracy, the suggested linear SPM based on sparse coding of SIFT descriptors always significantly outperforms the linear SPM kernel on histograms, and is even better than the nonlinear SPM kernels, leading to state-of-the-art performance on several benchmarks by using a single type of descriptors.
Poster: Wireless Power Hotspot that Charges All of Your Devices

Each year, consumers carry an increasing number of gadgets on their person: mobile phones, tablets, smartwatches, etc. As a result, users must remember to recharge each device, every day. Wireless charging promises to free users from this burden, allowing devices to remain permanently unplugged. Today's wireless charging, however, is either limited to a single device, or is highly cumbersome, requiring the user to remove all of her wearable and handheld gadgets and place them on a charging pad. This paper introduces MultiSpot, a new wireless charging technology that can charge multiple devices, even as the user is wearing them or carrying them in her pocket. A MultiSpot charger acts as an access point for wireless power. When a user enters the vicinity of the MultiSpot charger, all of her gadgets start to charge automatically. We have prototyped MultiSpot and evaluated it using off-the-shelf mobile phones, smartwatches, and tablets. Our results show that MultiSpot can charge 6 devices at distances of up to 50cm.
Quantifying and Visualizing Attribute Interactions

Interactions are the prototypical examples of unexpected and irreducible patterns in data that encompass several attributes. We attempt to unify the divergent definitions and conceptions of interactions present in the literature, and propose two separate conceptions of what interactions are: if a group of attributes interacts observationally, we cannot understand their relationship without looking at all the attributes simultaneously; if a group of attributes interacts representationally, we cannot create a model that would not include conditions involving all the attributes. Rather than test for interactions, we attempt to quantify them, and visually present the most important interactions of the data. Our quantification is based on how well we can approximate a joint probability distribution without admitting that there are interactions. Judging from these illustrations, most machine learning procedures are not treating interactions appropriately.
Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System

Designing the dialogue policy of a spoken dialogue system involves many nontrivial choices. This paper presents a reinforcement learning approach for automatically optimizing a dialogue policy, which addresses the technical challenges in applying reinforcement learning to a working dialogue system with human users. We report on the design, construction and empirical evaluation of NJFun, an experimental spoken dialogue system that provides users with access to information about fun things to do in New Jersey. Our results show that by optimizing its performance via reinforcement learning, NJFun measurably improves system performance.
Real Behavior in Virtual Environments: Psychology Experiments in a Simple Virtual-Reality Paradigm Using Video Games

The purpose of this research was to illustrate the broad usefulness of simple video-game-based virtual environments (VEs) for psychological research on real-world behavior. To this end, this research explored several high-level social phenomena in a simple, inexpensive computer-game environment: the reduced likelihood of helping under time pressure and the bystander effect, which is reduced helping in the presence of bystanders. In the first experiment, participants had to find the exit in a virtual labyrinth under either high or low time pressure. They encountered rooms with and without virtual bystanders, and in each room, a virtual person requested assistance. Participants helped significantly less frequently under time pressure but the presence/absence of a small number of bystanders did not significantly moderate helping. The second experiment increased the number of virtual bystanders, and participants were instructed to imagine that these were real people. Participants helped significantly less in rooms with large numbers of bystanders compared to rooms with no bystanders, thus demonstrating a bystander effect. These results indicate that even sophisticated high-level social behaviors can be observed and experimentally manipulated in simple VEs, thus implying the broad usefulness of this paradigm in psychological research as a good compromise between experimental control and ecological validity.
The Internet of Things: A Review of Enabled Technologies and Future Challenges

The Internet of Things (IoT) is an emerging classical model, envisioned as a system of billions of small interconnected devices for posing the state-of-the-art findings to real-world glitches. Over the last decade, there has been an increasing research concentration in the IoT as an essential design of the constant convergence between human behaviors and their images on Information Technology. With the development of technologies, the IoT drives the deployment of across-the-board and self-organizing wireless networks. The IoT model is progressing toward the notion of a cyber-physical world, where things can be originated, driven, intermixed, and modernized to facilitate the emergence of any feasible association. This paper provides a summary of the existing IoT research that underlines enabling technologies, such as fog computing, wireless sensor networks, data mining, context awareness, real-time analytics, virtual reality, and cellular communications. Also, we present the lessons learned after acquiring a thorough representation of the subject. Thus, by identifying numerous open research challenges, it is presumed to drag more consideration into this novel paradigm.
Biobanks: transnational, European and global networks.

Biobanks contain biological samples and associated information that are essential raw materials for advancement of biotechnology, human health, and research and development in life sciences. Population-based and disease-oriented biobanks are major biobank formats to establish the disease relevance of human genes and provide opportunities to elucidate their interaction with environment and lifestyle. The developments in personalized medicine require molecular definition of new disease subentities and biomarkers for identification of relevant patient subgroups for drug development. These emerging demands can only be met if biobanks cooperate at the transnational or even global scale. Establishment of common standards and strategies to cope with the heterogeneous legal and ethical landscape in different countries are seen as major challenges for biobank networks. The Central Research Infrastructure for Molecular Pathology (CRIP), the concept for a pan-European Biobanking and Biomolecular Resources Research Infrastructure (BBMRI), and the Organization for Economic Co-operation and Development (OECD) global Biological Resources Centres network are examples for transnational, European and global biobank networks that are described in this article.
Analytic Provenance for Sensemaking: A Research Agenda

Sensemaking is a process of finding meaning from information that often involves activities such as information foraging and hypothesis generation. It can be valuable to maintain a history of the data and reasoning involved. This history, commonly known as provenance information, can be a resource for "reflection-in-action"' during analysis, supporting collaboration between analysts, and can help trace data quality and uncertainty through the analysis process. Currently, there is limited work on utilizing analytic provenance, which captures the interactive data exploration and human reasoning process, to support sensemaking. This article presents and extends the research challenges discussed in a IEEE VIS 2014 workshop on this topic to provide an agenda for sensemaking analytic provenance.
Multi-core data analytics SoC with a flexible 1.76 Gbit/s AES-XTS cryptographic accelerator in 65 nm CMOS

Embedded systems for Internet-of-Things applications present new challenges to system design. From a hardware design perspective, energy efficiency is paramount, as most of devices have a limited power supply due to size considerations. Transmitting data away from the node remains a very power hungry operation, and the only viable solution to this problem is to reduce the amount of data by performing pre-processing which again requires additional computational power. Hence modern embedded devices need to strike a fine balance between the power needed for acquisition/processing and communication. In many scenarios, small IoT devices will be deployed widely making them vulnerable to malicious attacks. Thus, for practical applications, these devices also need to fit the necessary resources to provide adequate security services.
 We present a cryptographic hardware accelerator capable of supporting multiple encryption and decryption modes for different cryptographic algorithms (AES, Keccak) in an energy efficient multi-core cluster optimized for embedded digital signal processing applications implemented in 65 nm CMOS technology. We show that it is possible to have the necessary computation power to perform cryptographic services in addition to state of the art processing in a power budget that is compatible with IoT devices in a mature 65 nm CMOS technology. When running at 0.8 V the SoC with the cryptographic accelerator can be clocked at 84 MHz running AES-XTS at more than 250 Mbits/s consuming a total of 27 mW, which is a 100 × gain in energy and 496 × gain in operation speed over an optimized software implementation running on a single 32 bit OpenRISC core.
Graph-based semi-supervised learning for relational networks

We address the problem of semi-supervised learning in relational networks, networks in which nodes are entities and links are the relationships or interactions between them. Typically this problem is confounded with the problem of graph-based semi-supervised learning (GSSL), because both problems represent the data as a graph and predict the missing class labels of nodes. However, not all graphs are created equally. In GSSL a graph is constructed, often from independent data, based on similarity. As such, edges tend to connect instances with the same class label. Relational networks, however, can be more heterogeneous and edges do not always indicate similarity. For instance, instead of links being more likely to connect nodes with the same class label, they may occur more frequently between nodes with different class labels (link-heterogeneity). Or nodes with the same class label do not necessarily have the same type of connectivity across the whole network (class-heterogeneity), e.g. in a network of sexual interactions we may observe links between opposite genders in some parts of the graph and links between the same genders in others. Performing classification in networks with different types of heterogeneity is a hard problem that is made harder still by the fact we do not know a-priori the type or level of heterogeneity. In this work we present two scalable approaches for graph-based semi-supervised learning for the more general case of relational networks. We demonstrate these approaches on synthetic and real-world networks that display different link patterns within and between classes. Compared to state-of-the-art baseline approaches, ours give better classification performance and do so without prior knowledge of how classes interact. In particular, our two-step label propagation algorithm gives consistently good accuracy and precision, while also being highly efficient and can perform classification in networks of over 1.6 million nodes and 30 million edges in around 12 seconds.
LogP: Towards a Realistic Model of Parallel Computation

A vast body of theoretical research has focused either on overly simplistic models of parallel computation, notably the PRAM, or overly specific models that have few representatives in the real world. Both kinds of models encourage exploitation of formal loopholes, rather than rewarding development of techniques that yield performance across a range of current and future parallel machines. This paper offers a new parallel machine model, called LogP, that reflects the critical technology trends underlying parallel computers. it is intended to serve as a basis for developing fast, portable parallel algorithms and to offer guidelines to machine designers. Such a model must strike a balance between detail and simplicity in order to reveal important bottlenecks without making analysis of interesting problems intractable. The model is based on four parameters that specify abstractly the computing bandwidth, the communication bandwidth, the communication delay, and the efficiency of coupling communication and computation. Portable parallel algorithms typically adapt to the machine configuration, in terms of these parameters. The utility of the model is demonstrated through examples that are implemented on the CM-5.