nils-herrmann/clean_scirepeval huggingface dataset:
Embedding Label Structures for Fine-Grained Feature Representation

Recent algorithms in convolutional neural networks (CNN) considerably advance the fine-grained image classification, which aims to differentiate subtle differences among subordinate classes. However, previous studies have rarely focused on learning a fined-grained and structured feature representation that is able to locate similar images at different levels of relevance, e.g., discovering cars from the same make or the same model, both of which require high precision. In this paper, we propose two main contributions to tackle this problem. 1) A multitask learning framework is designed to effectively learn fine-grained feature representations by jointly optimizing both classification and similarity constraints. 2) To model the multi-level relevance, label structures such as hierarchy or shared attributes are seamlessly embedded into the framework by generalizing the triplet loss. Extensive and thorough experiments have been conducted on three finegrained datasets, i.e., the Stanford car, the Car-333, and the food datasets, which contain either hierarchical labels or shared attributes. Our proposed method has achieved very competitive performance, i.e., among state-of-the-art classification accuracy when not using parts. More importantly, it significantly outperforms previous fine-grained feature representations for image retrieval at different levels of relevance.
Automatic language identification using long short-term memory recurrent neural networks

This work explores the use of Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) for automatic language identification (LID). The use of RNNs is motivated by their better ability in modeling sequences with respect to feed forward networks used in previous works. We show that LSTM RNNs can effectively exploit temporal dependencies in acoustic data, learning relevant features for language discrimination purposes. The proposed approach is compared to baseline i-vector and feed forward Deep Neural Network (DNN) systems in the NIST Language Recognition Evaluation 2009 dataset. We show LSTM RNNs achieve better performance than our best DNN system with an order of magnitude fewer parameters. Further, the combination of the different systems leads to significant performance improvements (up to 28%).
Shill Bidding In Multi-Round Online Auctions

The online implementation of traditional business mechanisms raises many new issues not considered in classical economic models. This partially explains why online auctions have become the most successful but also the most controversial Internet businesses in the recent years. One emerging issue is that the lack of authentication over the Internet has encouraged shill bidding, the deliberate placing of bids on the seller’s behalf to artificially drive up the price of the seller’s auctioned item. Private-value English auctions with shill bidding can result in a higher expected seller profit than other auction formats , violating the classical revenue equivalence theory. This paper analyzes shill bidding in multi-round online English auctions and proves that there is no equilibrium without shill bidding. Taking into account the seller’s shills and relistings, bidders with valuations even higher than the reserve will either wait for the next round or shield their bids in the current round. Hence, it is inevitable to redesign online auctions to deal with the “shiller’s curse.”
Towards the Systematic Testing of Aspect-Oriented Programs

The code that provides solutions to key software requirements, such as security and fault-tolerance, tends to be spread throughout (or cross-cut) the program modules that implement the “primary functionality” of a software system. Aspect-oriented programming is an emerging programming paradigm that supports implementing such cross-cutting requirements into named program units called “aspects”. To construct a system as an aspect-oriented program (AOP), one develops code for primary functionality in traditional modules and code for cross-cutting functionality in aspect modules. Compiling and running an AOP requires that the aspect code be “woven” into the code. Although aspect-oriented programming supports the separation of concerns into named program units, explicit and implicit dependencies of both aspects and traditional modules will result in systems with new testing challenges, which include new sources for program faults. This paper introduces a candidate fault model, along with associated testing criteria, for AOPs based on interactions that are unique to AOPs. The paper also identifies key issues relevant to the systematic testing of AOPs.
A Joint Model of Intent Determination and Slot Filling for Spoken Language Understanding

Two major tasks in spoken language understanding (SLU) are intent determination (ID) and slot filling (SF). Recurrent neural networks (RNNs) have been proved effective in SF, while there is no prior work using RNNs in ID. Based on the idea that the intent and semantic slots of a sentence are correlative, we propose a joint model for both tasks. Gated recurrent unit (GRU) is used to learn the representation of each time step, by which the label of each slot is predicted. Meanwhile, a max-pooling layer is employed to capture global features of a sentence for intent classification. The representations are shared by two tasks and the model is trained by a united loss function. We conduct experiments on two datasets, and the experimental results demonstrate that our model outperforms the state-of-theart approaches on both tasks.
Cyber Threats Facing Autonomous and Connected Vehicles: Future Challenges

Vehicles are currently being developed and sold with increasing levels of connectivity and automation. As with all networked computing devices, increased connectivity often results in a heightened risk of a cyber security attack. Furthermore, increased automation exacerbates any risk by increasing the opportunities for the adversary to implement a successful attack. In this paper, a large volume of publicly accessible literature is reviewed and compartmentalized based on the vulnerabilities identified and mitigation techniques developed. This review highlighted that the majority of studies are reactive and vulnerabilities are often discovered by friendly adversaries (white-hat hackers). Many gaps in the knowledge base were identified. Priority should be given to address these knowledge gaps to minimize future cyber security risks in the connected and autonomous vehicle sector.
A Framework for Partitioning and Execution of Data Stream Applications in Mobile Cloud Computing

The contribution of cloud computing and mobile computing technologies lead to the newly emerging mobile cloud computing paradigm. Three major approaches have been proposed for mobile cloud applications: 1) extending the access to cloud services to mobile devices; 2) enabling mobile devices to work collaboratively as cloud resource providers; 3) augmenting the execution of mobile applications on portable devices using cloud resources. In this paper, we focus on the third approach in supporting mobile data stream applications. More specifically, we study how to optimize the computation partitioning of a data stream application between mobile and cloud to achieve maximum speed/throughput in processing the streaming data.
 To the best of our knowledge, it is the first work to study the partitioning problem for mobile data stream applications, where the optimization is placed on achieving high throughput of processing the streaming data rather than minimizing the makespan of executions as in other applications. We first propose a framework to provide runtime support for the dynamic computation partitioning and execution of the application. Different from existing works, the framework not only allows the dynamic partitioning for a single user but also supports the sharing of computation instances among multiple users in the cloud to achieve efficient utilization of the underlying cloud resources. Meanwhile, the framework has better scalability because it is designed on the elastic cloud fabrics. Based on the framework, we design a genetic algorithm for optimal computation partition. Both numerical evaluation and real world experiment have been performed, and the results show that the partitioned application can achieve at least two times better performance in terms of throughput than the application without partitioning.
Usability evaluation of ontology editors

Ontology editors are software tools that allow the creation and maintenance of ontologies through a graphical user interface. As the Semantic Web effort grows, a larger community of users for this kind of tools is expected. New users include people not specifically skilled in the use of ontology formalisms. In consequence, the usability of ontology editors can be viewed as a key adoption precondition for Semantic Web technologies. In this paper, the usability evaluation of several representative ontology editors is described. This evaluation is carried out by combining a heuristic pre-assessment and a subsequent user-testing phase. The target population comprises people with no specific ontology-creation skills that have a general knowledge about domain modelling. The problems found point out that, for this kind of users, current editors are adequate for the creation and maintenance of simple ontologies, but also that there is room for improvement, especially in browsing mechanisms, help systems and visualization metaphors.
The Performance Cost of Shadow Stacks and Stack Canaries

Control flow defenses against ROP either use strict, expensive, but strong protection against redirected RET instructions with shadow stacks, or much faster but weaker protections without. In this work we study the inherent overheads of shadow stack schemes. We find that the overhead is roughly 10% for a traditional shadow stack. We then design a new scheme, the parallel shadow stack, and show that its performance cost is significantly less: 3.5%. Our measurements suggest it will not be easy to improve performance on current x86 processors further, due to inherent costs associated with RET and memory load/store instructions. We conclude with a discussion of the design decisions in our shadow stack instrumentation, and possible lighter-weight alternatives.
Sar-graphs: A language resource connecting linguistic knowledge with semantic relations from knowledge graphs

Recent years have seen a significant growth and increased usage of large-scale knowledge resources in both academic research and industry. We can distinguish two main types of knowledge resources: those that store factual information about entities in the form of semantic relations (e.g., Freebase), namely so-called knowledge graphs, and those that represent general linguistic knowledge (e.g., WordNet or UWN). In this article, we present a third type of knowledge resource which completes the picture by connecting the two first types. Instances of this resource are graphs of semantically-associated relations (sar-graphs), whose purpose is to link semantic relations from factual knowledge graphs with their linguistic representations in human language. We present a general method for constructing sar-graphs using a languageand relation-independent, distantly supervised approach which, apart from generic language processing tools, relies solely on the availability of a lexical semantic resource, providing sense information for words, as well as a knowledge base containing seed relation instances. Using these seeds, our method extracts, validates and merges relationspecific linguistic patterns from text to create sar-graphs. To cope with the noisily labeled data arising in a distantly supervised setting, we propose several automatic pattern confidence estimation strategies, and also show how manual supervision can be used to improve the quality of sar-graph instances. We demonstrate the applicability of our method by constructing sar-graphs for 25 semantic relations, of which we make a subset publicly available at http://sargraph.dfki.de. We believe sar-graphs will prove to be useful linguistic resources for a wide variety of natural language processing tasks, and in particular for information extraction and knowledge base population. We illustrate their usefulness with experiments in relation extraction and in computer assisted language learning.
Processor Architecture Design Using 3D Integration Technology

The emerging three-dimensional (3D) chip architectures, with their intrinsic capability of reducing the wire length, is one of the promising solutions to mitigate the interconnect problem in modern microprocessor designs. 3D memory stacking also enables much higher memory bandwidth for future chip-multiprocessor design, mitigating the ``memory wall" problem. In addition, heterogenous integration enabled by 3D technology can also result in innovation designs for future microprocessors. This paper serves as a survey of various approaches to design future 3D microprocessors, leveraging the benefits of fast latency, higher bandwidth, and heterogeneous integration capability that are offered by 3D technology.
Low-power operation using self-timed circuits and adaptive scaling of the supply voltage

AbstrucfRecent research has demonstrated that for certain types of applications like sampled audio systems, self-timed circuits can achieve very low power consumption, because unused circuit parts automatically turn into a stand-by mode. Additional savings may be obtained by combining the self-timed circuits with a mechanism that adaptively adjusts the supply voltage to the smallest possible, while maintaining the performance requirements. This paper describes such a mechanism, analyzes the possible power savings, and presents a demonstrator chip that has been fabricated and tested. The idea of voltage scaling has been used previously in synchronous circuits, and the contributions of the present paper are: 1) the combination of supply scaling and self-timed circuitry which has some unique advantages, and 2) the thorough analysis of the power savings that are possible using this technique.
Fast Best-Effort Search on Graphs with Multiple Attributes

We address the problem of search on graphs with multiple nodal attributes. We call such graphs weighted attribute graphs (WAGs). Nodes of a WAG exhibit multiple attributes with varying, non-negative weights. WAGs are ubiquitous in real-world applications. For example, in a co-authorship WAG, each author is a node; each attribute corresponds to a particular topic (e.g., databases, data mining, and machine learning); and the amount of expertise in a particular topic is represented by a non-negative weight on that attribute. A typical search in this setting specifies both connectivity between nodes and constraints on weights of nodal attributes. For example, a user's search may be: find three coauthors (i.e., a triangle) where each author's expertise is greater than 50 percent in at least one topic area (i.e., attribute). We propose a ranking function which unifies ranking between the graph structure and attribute weights of nodes. We prove that the problem of retrieving the optimal answer for graph search on WAGs is NP-complete. Moreover, we propose a fast and effective top-k graph search algorithm for WAGs. In an extensive experimental study with multiple real-world graphs, our proposed algorithm exhibits significant speed-up over competing approaches. On average, our proposed method is more than 7χ faster in query processing than the best competitor.
Passive exoskeletons for assisting limb movement.

This article presents the state of the art in passive devices for enhancing limb movement in people with neuromuscular disabilities. Both upper- and lower-limb projects and devices are described. Special emphasis is placed on a passive functional upper-limb orthosis called the Wilmington Robotic Exoskeleton (WREX). The development and testing of the WREX with children with limited arm strength are described. The exoskeleton has two links and 4 degrees of freedom. It uses linear elastic elements that balance the effects of gravity in three dimensions. The experiences of five children with arthrogryposis who used the WREX are described.
Room-temperature ultraviolet nanowire nanolasers.

Room-temperature ultraviolet lasing in semiconductor nanowire arrays has been demonstrated. The self-organized, <0001> oriented zinc oxide nanowires grown on sapphire substrates were synthesized with a simple vapor transport and condensation process. These wide band-gap semiconductor nanowires form natural laser cavities with diameters varying from 20 to 150 nanometers and lengths up to 10 micrometers. Under optical excitation, surface-emitting lasing action was observed at 385 nanometers, with an emission linewidth less than 0.3 nanometer. The chemical flexibility and the one-dimensionality of the nanowires make them ideal miniaturized laser light sources. These short-wavelength nanolasers could have myriad applications, including optical computing, information storage, and microanalysis.
Application-aware traffic scheduling for workload offloading in mobile clouds

Mobile Cloud Computing (MCC) bridges the gap between limited capabilities of mobile devices and the increasing complexity of mobile applications, by offloading the computational workloads from local devices to the cloud. Current research supports workload offloading through appropriate application partitioning and remote method execution, but generally ignores the impact of wireless network characteristics on such offloading. Wireless data transmissions incurred by remote method execution consume a large amount of additional energy during transmission intervals when the network interface stays in the high-power state, and deferring these transmissions increases the response delay of mobile applications. In this paper, we adaptively balance the tradeoff between energy efficiency and responsiveness of mobile applications by developing application-aware wireless transmission scheduling algorithms. We take both causality and run-time dynamics of application method executions into account when deferring wireless transmissions, so as to minimize the wireless energy cost and satisfy the application delay constraint with respect to the practical system contexts. Systematic evaluations show that our scheme significantly improves the energy efficiency of workload offloading over realistic smartphone applications.
Towards a software-defined Network Operating System for the IoT

The heterogeneity characterizing the Internet of Things (IoT) landscape can be addressed by leveraging network operating systems (NOS)s. Current NOS solutions have been developed for traditional infrastructured networks: they do not take the specific features of fundamental IoT components such as wireless sensor and actuator networks into account. Accordingly, in this paper an innovative integrated network operating system for the IoT which is obtained as the evolution of the Open Network Operating System (ONOS) is proposed. This has been appropriately extended to enhance the recently proposed SDN-WISE platform for supporting SDN in wireless sensor networks and let it interact with standard OpenFlow (OF) switches. The proposed integrated system has been prototyped.
Column Networks for Collective Classification

Relational learning deals with data that are characterized by relational structures. An important task is collective classification, which is to jointly classify networked objects. While it holds a great promise to produce a better accuracy than noncollective classifiers, collective classification is computational challenging and has not leveraged on the recent breakthroughs of deep learning. We present Column Network (CLN), a novel deep learning model for collective classification in multirelational domains. CLN has many desirable theoretical properties: (i) it encodes multi-relations between any two instances; (ii) it is deep and compact, allowing complex functions to be approximated at the network level with a small set of free parameters; (iii) local and relational features are learned simultaneously; (iv) longrange, higher-order dependencies between instances are supported naturally; and (v) crucially, learning and inference are efficient, linear in the size of the network and the number of relations. We evaluate CLN on multiple real-world applications: (a) delay prediction in software projects, (b) PubMed Diabetes publication classification and (c) film genre classification. In all applications, CLN demonstrates a higher accuracy than state-of-the-art rivals.
The Teaching Dimension of Linear Learners

Teaching dimension is a learning theoretic quantity that specifies the minimum training set size to teach a target model to a learner. Previous studies on teaching dimension focused on version-space learners which maintain all hypotheses consistent with the training data, and cannot be applied to modern machine learners which select a specific hypothesis via optimization. This paper presents the first known teaching dimension for ridge regression, support vector machines, and logistic regression. We also exhibit optimal training sets that match these teaching dimensions. Our approach generalizes to other linear learners.
Network monitoring using traffic dispersion graphs (tdgs)

Monitoring network traffic and detecting unwanted applications has become a challenging problem, since many applications obfuscate their traffic using unregistered port numbers or payload encryption. Apart from some notable exceptions, most traffic monitoring tools use two types of approaches: (a) keeping traffic statistics such as packet sizes and interarrivals, flow counts, byte volumes, etc., or (b) analyzing packet content. In this paper, we propose the use of Traffic Dispersion Graphs (TDGs) as a way to monitor, analyze, and visualize network traffic. TDGs model the social behavior of hosts ("who talks to whom"), where the edges can be defined to represent different interactions (e.g. the exchange of a certain number or type of packets). With the introduction of TDGs, we are able to harness a wealth of tools and graph modeling techniques from a diverse set of disciplines.
Architectural support for hypervisor-secure virtualization

Virtualization has become a standard part of many computer systems. A key part of virtualization is the all-powerful hypervisor which manages the physical platform and can access all of its resources, including memory assigned to the guest virtual machines (VMs). Continuing releases of bug reports and exploits in the virtualization software show that defending the hypervisor against attacks is very difficult. In this work, we present hypervisor-secure virtualization - a new research direction with the goal of protecting the guest VMs from an untrusted hypervisor. We also present the HyperWall architecture which achieves hypervisor-secure virtualization, using hardware to provide the protections. HyperWall allows a hypervisor to freely manage the memory, processor cores and other resources of a platform. Yet once VMs are created, our new Confidentiality and Integrity Protection (CIP) tables protect the memory of the guest VMs from accesses by the hypervisor or by DMA, depending on the customer's specification. If a hypervisor does become compromised, e.g. by an attack from a malicious VM, it cannot be used in turn to attack other VMs. The protections are enabled through minimal modifications to the microprocessor and memory management units. Whereas much of the previous work concentrates on protecting the hypervisor from attacks by guest VMs, we tackle the problem of protecting the guest VMs from the hypervisor.