nils-herrmann/clean_scirepeval huggingface dataset:
No Fuss Distance Metric Learning Using Proxies

We address the problem of distance metric learning (DML), defined as learning a distance consistent with a notion of semantic similarity. Traditionally, for this problem supervision is expressed in the form of sets of points that follow an ordinal relationship – an anchor point x is similar to a set of positive points Y , and dissimilar to a set of negative points Z, and a loss defined over these distances is minimized. While the specifics of the optimization differ, in this work we collectively call this type of supervision Triplets and all methods that follow this pattern Triplet-Based methods. These methods are challenging to optimize. A main issue is the need for finding informative triplets, which is usually achieved by a variety of tricks such as increasing the batch size, hard or semi-hard triplet mining, etc. Even with these tricks, the convergence rate of such methods is slow. In this paper we propose to optimize the triplet loss on a different space of triplets, consisting of an anchor data point and similar and dissimilar proxy points which are learned as well. These proxies approximate the original data points, so that a triplet loss over the proxies is a tight upper bound of the original loss. This proxy-based loss is empirically better behaved. As a result, the proxy-loss improves on state-of-art results for three standard zero-shot learning datasets, by up to 15% points, while converging three times as fast as other triplet-based losses.
Overlapped community detection in complex networks

Extracting and understanding community structure in complex networks is one of the most intensively investigated problems in recent years. In this paper we propose a genetic based approach to discover overlapping communities. The algorithm optimizes a fitness function able to identify densely connected groups of nodes by employing it on the line graph corresponding to the graph modelling the network. The method generates a division of the network in a number of groups in an unsupervised way. This number is automatically determined by the optimal value of the fitness function. Experiments on synthetic and real life networks show the capability of the method to successfully detect the network structure.
Fully Probe-Corrected Near-Field Far-Field Transformation Employing Plane Wave Expansion and Diagonal Translation Operators

Near-field antenna measurements combined with a near-field far-field transformation are an established antenna characterization technique. The approach avoids far-field measurements and offers a wide area of post-processing possibilities including radiation pattern determination and diagnostic methods. In this paper, a near-field far-field transformation algorithm employing plane wave expansion is presented and applied to the case of spherical near-field measurements. Compared to existing algorithms, this approach exploits the benefits of diagonalized translation operators, known from fast multipole methods. Due to the plane wave based field representation, a probe correction, using directly the probe's far-field pattern can easily be integrated into the transformation. Hence, it is possible to perform a full probe correction for arbitrary field probes with almost no additional effort. In contrast to other plane wave techniques, like holographic projections, which are suitable for highly directive antennas, the presented approach is applicable for arbitrary radiating structures. Major advantages are low computational effort with respect to the coupling matrix elements owing to the use of diagonalized translation operators and the efficient correction of arbitrary field probes. Also, irregular measurement grids can be handled with little additional effort.
Body Centred Interaction in Immersive Virtual Environments

The technology to immerse people in computer generated worlds was proposed by Sutherland in 1965, and realised in 1968 with a head-mounted display that could present a user with a stereoscopic 3-dimensional view slaved to a sensing device tracking the user's head movements (Sutherland 1965; 1968). The views presented at that time were simple wire frame models. The advance of computer graphics knowledge and technology, itself tied to the enormous increase in processing power and decrease in cost, together with the development of relatively efficient and unobtrusive sensing devices, has led to the emergence of participatory immersive virtual environments, commonly referred to as "virtual reality" (VR) (Fisher 1982; Fisher et. al. 1986; Teitel 1990; see also SIGGRAPH Panel Proceedings 1989,1990). Ellis defines virtualisation as "the process by which a human viewer interprets a patterned sensory impression to be an extended object in an environment other than that in which it physically exists" (Ellis, 1991). In this definition the idea is taken from geometric optics, where the concept of a "virtual image" is precisely defined, and is well understood. In the context of virtual reality the "patterned sensory impressions" are generated to the human senses through visual, auditory, tactile and kinesthetic displays, though systems that effectively present information in all such sensory modalities do not exist at present. Ellis further distinguishes between a virtual space, image and environment. An example of the first is a flat surface on which an image is rendered. Perspective depth cues, texture gradients, occlusion, and other similar aspects of the image lead to an observer perceiving
HAMLeT: Hardware accelerated memory layout transform within 3D-stacked DRAM

Memory layout transformations via data reorganization are very common operations, which occur as a part of the computation or as a performance optimization in data-intensive applications. These operations require inefficient memory access patterns and roundtrip data movement through the memory hierarchy, failing to utilize the performance and energy-efficiency potentials of the memory subsystem. This paper proposes a high-bandwidth and energy-efficient hardware accelerated memory layout transform (HAMLeT) system integrated within a 3D-stacked DRAM. HAMLeT uses a low-overhead hardware that exploits the existing infrastructure in the logic layer of 3D-stacked DRAMs, and does not require any changes to the DRAM layers, yet it can fully exploit the locality and parallelism within the stack by implementing efficient layout transform algorithms. We analyze matrix layout transform operations (such as matrix transpose, matrix blocking and 3D matrix rotation) and demonstrate that HAMLeT can achieve close to peak system utilization, offering up to an order of magnitude performance improvement compared to the CPU and GPU memory subsystems which does not employ HAMLeT.
Predicting the Stream of Consciousness from Activity in Human Visual Cortex

Can the rapid stream of conscious experience be predicted from brain activity alone? Recently, spatial patterns of activity in visual cortex have been successfully used to predict feature-specific stimulus representations for both visible and invisible stimuli. However, because these studies examined only the prediction of static and unchanging perceptual states during extended periods of stimulation, it remains unclear whether activity in early visual cortex can also predict the rapidly and spontaneously changing stream of consciousness. Here, we used binocular rivalry to induce frequent spontaneous and stochastic changes in conscious experience without any corresponding changes in sensory stimulation, while measuring brain activity with fMRI. Using information that was present in the multivariate pattern of responses to stimulus features, we could accurately predict, and therefore track, participants' conscious experience from the fMRI signal alone while it underwent many spontaneous changes. Prediction in primary visual cortex primarily reflected eye-based signals, whereas prediction in higher areas reflected the color of the percept. Furthermore, accurate prediction during binocular rivalry could be established with signals recorded during stable monocular viewing, showing that prediction generalized across viewing conditions and did not require or rely on motor responses. It is therefore possible to predict the dynamically changing time course of subjective experience with only brain activity.
Accelerating SPARQL queries by exploiting hash-based locality and adaptive partitioning

State-of-the-art distributed RDF systems partition data across multiple computer nodes (workers). Some systems perform cheap hash partitioning, which may result in expensive query evaluation. Others try to minimize inter-node communication, which requires an expensive data preprocessing phase, leading to a high startup cost. Apriori knowledge of the query workload has also been used to create partitions, which, however, are static and do not adapt to workload changes. In this paper, we propose AdPart, a distributed RDF system, which addresses the shortcomings of previous work. First, AdPart applies lightweight partitioning on the initial data, which distributes triples by hashing on their subjects; this renders its startup overhead low. At the same time, the locality-aware query optimizer of AdPart takes full advantage of the partitioning to (1) support the fully parallel processing of join patterns on subjects and (2) minimize data communication for general queries by applying hash distribution of intermediate results instead of broadcasting, wherever possible. Second, AdPart monitors the data access patterns and dynamically redistributes and replicates the instances of the most frequent ones among workers. As a result, the communication cost for future queries is drastically reduced or even eliminated. To control replication, AdPart implements an eviction policy for the redistributed patterns. Our experiments with synthetic and real data verify that AdPart: (1) starts faster than all existing systems; (2) processes thousands of queries before other systems become online; and (3) gracefully adapts to the query load, being able to evaluate queries on billion-scale RDF data in subseconds.
Feedforward Sequential Memory Networks: A New Structure to Learn Long-term Dependency

In this paper, we propose a novel neural network structure, namely feedforward sequential memory networks (FSMN), to model long-term dependency in time series without using recurrent feedback. The proposed FSMN is a standard fully-connected feedforward neural network equipped with some learnable memory blocks in its hidden layers. The memory blocks use a tapped-delay line structure to encode the long context information into a fixed-size representation as short-term memory mechanism. We have evaluated the proposed FSMNs in several standard benchmark tasks, including speech recognition and language modelling. Experimental results have shown FSMNs significantly outperform the conventional recurrent neural networks (RNN), including LSTMs, in modeling sequential signals like speech or language. Moreover, FSMNs can be learned much more reliably and faster than RNNs or LSTMs due to the inherent non-recurrent model structure.
Dissociation of cognitive and emotional empathy in adults with Asperger syndrome using the Multifaceted Empathy Test (MET).

Empathy is a multidimensional construct consisting of cognitive (inferring mental states) and emotional (empathic concern) components. Despite a paucity of research, individuals on the autism spectrum are generally believed to lack empathy. In the current study we used a new, photo-based measure, the Multifaceted Empathy Test (MET), to assess empathy multidimensionally in a group of 17 individuals with Asperger syndrome (AS) and 18 well-matched controls. Results suggested that while individuals with AS are impaired in cognitive empathy, they do not differ from controls in emotional empathy. Level of general emotional arousability and socially desirable answer tendencies did not differ between groups. Internal consistency of the MET's scales ranged from .71 to .92, and convergent and divergent validity were highly satisfactory.
High-Voltage Modular Switched Capacitor Pulsed Power Generator

Utilization of power electronics converters in pulsed power applications introduced a new series of reliable, long life, and cost-effective pulse generators. However, these converters suffer from the limited power ratings of semiconductor switches, which necessitate introduction of a new family of modular topologies. This paper proposes a modular power electronics converter based on voltage multiplier as a high voltage pulsed power generator. This modular circuit is able to generate flexible high output voltage from low input voltage sources. Circuit topology and operational principles of proposed topology are verified via experimental and simulation results as well as theoretical analysis.
TrimBot2020: an outdoor robot for automatic gardening

Robots are increasingly present in modern industry and also in everyday life. Their applications range from health-related situations, for assistance to elderly people or in surgical operations, to automatic and driver-less vehicles (on wheels or flying) or for driving assistance. Recently, an interest towards robotics applied in agriculture and gardening has arisen, with applications to automatic seeding and cropping or to plant disease control, etc. Autonomous lawn mowers are succesful market applications of gardening robotics. In this paper, we present a novel robot that is developed within the TrimBot2020 project, funded by the EU H2020 program. The project aims at prototyping the first outdoor robot for automatic bush trimming and rose pruning.
A cluster-based directional routing protocol in VANET

Vehicular Ad-hoc Network (VANET) is a new application of Mobile Ad-hoc Network (MANET) in the field of Inter-vehicle communication. As the high mobility of vehicles, some traditional MANET routing protocols may not fit the VANET. In this paper, we propose a cluster-based directional routing protocol (CBDRP) for highway scenarios, in which the header of a cluster selects another header according to the moving direction of vehicle to forward packets. Simulation results shows the CBDRP can solve the problem of link stability in VANET, realizing reliable and rapid data transmission.
Ten Pairs to Tag - Multilingual POS Tagging via Coarse Mapping between Embeddings

In the absence of annotations in the target language, multilingual models typically draw on extensive parallel resources. In this paper, we demonstrate that accurate multilingual partof-speech (POS) tagging can be done with just a few (e.g., ten) word translation pairs. We use the translation pairs to establish a coarse linear isometric (orthonormal) mapping between monolingual embeddings. This enables the supervised source model expressed in terms of embeddings to be used directly on the target language. We further refine the model in an unsupervised manner by initializing and regularizing it to be close to the direct transfer model. Averaged across six languages, our model yields a 37.5% absolute improvement over the monolingual prototypedriven method (Haghighi and Klein, 2006) when using a comparable amount of supervision. Moreover, to highlight key linguistic characteristics of the generated tags, we use them to predict typological properties of languages, obtaining a 50% error reduction relative to the prototype
Effects of stress throughout the lifespan on the brain, behaviour and cognition

Chronic exposure to stress hormones, whether it occurs during the prenatal period, infancy, childhood, adolescence, adulthood or aging, has an impact on brain structures involved in cognition and mental health. However, the specific effects on the brain, behaviour and cognition emerge as a function of the timing and the duration of the exposure, and some also depend on the interaction between gene effects and previous exposure to environmental adversity. Advances in animal and human studies have made it possible to synthesize these findings, and in this Review a model is developed to explain why different disorders emerge in individuals exposed to stress at different times in their lives.
Analytical study of security aspects in 6LoWPAN networks

The Internet of Things (IoT) is a flagship project that aims to connect objects to the Internet to extend their use. For that, it was needed to find a solution to combine between the IEEE 802.15.4 protocol for Low Power Wireless Personal Area Networks (LoWPANs) and IPv6 network protocol that its large address space will allow million devices to integrate the internet. The development of 6LoWPAN technology was an appropriate solution to deal with this challenge and enable the IoT concept becoming a reality. But this was only the beginning of several challenges and problems like the case of how to secure this new type of networks, especially since it includes two major protocols so the combination of their problems too, over and above new problems specific to that network. In this paper, we analyze the security challenges in 6LoWPAN, we studied the various countermeasures to address these needs, their advantages and disadvantages, and we offer some recommendations to achieve a reliable security scheme for a powerful 6LoWPAN networks.
Automatic Protocol Format Reverse Engineering through Context-Aware Monitored Execution

Protocol reverse engineering has often been a manual process that is considered time-consuming, tedious and error-prone. To address this limitation, a number of solutions have recently been proposed to allow for automatic protocol reverse engineering. Unfortunately, they are either limited in extracting protocol fields due to lack of program semantics in network traces or primitive in only revealing the flat structure of protocol format. In this paper, we present a system called AutoFormat that aims at not only extracting protocol fields with high accuracy, but also revealing the inherently “non-flat”, hierarchical structures of protocol messages. AutoFormat is based on the key insight that different protocol fields in the same message are typically handled in different execution contexts (e.g., the runtime call stack). As such, by monitoring the program execution, we can collect the execution context information for every message byte (annotated with its offset in the entire message) and cluster them to derive the protocol format. We have evaluated our system with more than 30 protocol messages from seven protocols, including two text-based protocols (HTTP and SIP), three binary-based protocols (DHCP, RIP, and OSPF), one hybrid protocol (CIFS/SMB), as well as one unknown protocol used by a real-world malware. Our results show that AutoFormat can not only identify individual message fields automatically and with high accuracy (an average 93.4% match ratio compared with Wireshark), but also unveil the structure of the protocol format by revealing possible relations (e.g., sequential, parallel, and hierarchical) among the message fields. Part of this research has been supported by the National Science Foundation under grants CNS-0716376 and CNS-0716444. The bulk of this work was performed when the first author was visiting George Mason University in Summer 2007.
The WikEd Error Corpus: A Corpus of Corrective Wikipedia Edits and Its Application to Grammatical Error Correction

This paper introduces the freely available WikEd Error Corpus. We describe the data mining process from Wikipedia revision histories, corpus content and format. The corpus consists of more than 12 million sentences with a total of 14 million edits of various types. As one possible application, we show that WikEd can be successfully adapted to improve a strong baseline in a task of grammatical error correction for English-as-a-Second-Language (ESL) learners’ writings by 2.63%. Used together with an ESL error corpus, a composed system gains 1.64% when compared to the ESL-trained system.
Higher Order Conditional Random Fields in Deep Neural Networks

We address the problem of semantic segmentation using deep learning. Most segmentation systems include a Conditional Random Field (CRF) to produce a structured output that is consistent with the image’s visual features. Recent deep learning approaches have incorporated CRFs into Convolutional Neural Networks (CNNs), with some even training the CRF end-to-end with the rest of the network. However, these approaches have not employed higher order potentials, which have previously been shown to significantly improve segmentation performance. In this paper, we demonstrate that two types of higher order potential, based on object detections and superpixels, can be included in a CRF embedded within a deep network. We design these higher order potentials to allow inference with the differentiable mean field algorithm. As a result, all the parameters of our richer CRF model can be learned end-to-end with our pixelwise CNN classifier. We achieve state-of-the-art segmentation performance on the PASCAL VOC benchmark with these trainable higher order potentials.
Semantic data mining: A survey of ontology-based approaches

Semantic Data Mining refers to the data mining tasks that systematically incorporate domain knowledge, especially formal semantics, into the process. In the past, many research efforts have attested the benefits of incorporating domain knowledge in data mining. At the same time, the proliferation of knowledge engineering has enriched the family of domain knowledge, especially formal semantics and Semantic Web ontologies. Ontology is an explicit specification of conceptualization and a formal way to define the semantics of knowledge and data. The formal structure of ontology makes it a nature way to encode domain knowledge for the data mining use. In this survey paper, we introduce general concepts of semantic data mining. We investigate why ontology has the potential to help semantic data mining and how formal semantics in ontologies can be incorporated into the data mining process. We provide detail discussions for the advances and state of art of ontology-based approaches and an introduction of approaches that are based on other form of knowledge representations.
Design of a New High-Gain Circularly Polarized Antenna for Inmarsat Communications

A novel circularly polarized (CP) antenna with 1357-1997 MHz impedance bandwidth, 8 dB high gain, 3-dB axial-ratio bandwidth (ARBW) of 12.9% and ±45° half-power beamwidth (HPBW) is proposed. Compared to other reported Inmarsat antennas, the proposed antenna possesses much higher gain and larger CP bandwidth and is more suitable for high-latitude satellite communication systems.
Temporal and Spatial Clustering for a Parking Prediction Service

It has been estimated that in urban scenarios up to 30% of the traffic is due to vehicles looking for a free parking space. Thanks to recent technological evolutions, it is now possible to have at least a partial coverage of real-time data of parking space availability, and some preliminary mobile services are able to guide drivers towards free parking spaces. Nevertheless, the integration of this data within car navigators is challenging, mainly because (I) current In-Vehicle Telematic systems are not connected, and (II) they have strong limitations in terms of storage capabilities. To overcome these issues, in this paper we present a back-end based approach to learn historical models of parking availability per street. These compact models can then be easily stored on the map in the vehicle. In particular, we investigate the trade-off between the granularity level of the detailed spatial and temporal representation of parking space availability vs. The achievable prediction accuracy, using different spatio-temporal clustering strategies. The proposed solution is evaluated using five months of parking availability data, publicly available from the project Spark, based in San Francisco. Results show that clustering can reduce the needed storage up to 99%, still having an accuracy of around 70% in the predictions.