nils-herrmann/clean_scirepeval huggingface dataset:
A quantitative analysis on microarchitectures of modern CPU-FPGA platforms

CPU-FPGA heterogeneous acceleration platforms have shown great potential for continued performance and energy efficiency improvement for modern data centers, and have captured great attention from both academia and industry. However, it is nontrivial for users to choose the right platform among various PCIe and QPI based CPU-FPGA platforms from different vendors. This paper aims to find out what microarchitectural characteristics affect the performance, and how. We conduct our quantitative comparison and in-depth analysis on two representative platforms: QPI-based Intel-Altera HARP with coherent shared memory, and PCIe-based Alpha Data board with private device memory. We provide multiple insights for both application developers and platform designers.
Topic Modeling Based Multi-modal Depression Detection

Major depressive disorder is a common mental disorder that affects almost 7% of the adult U.S. population. The 2017 Audio/Visual Emotion Challenge (AVEC) asks participants to build a model to predict depression levels based on the audio, video, and text of an interview ranging between 7-33 minutes. Since averaging features over the entire interview will lose most temporal information, how to discover, capture, and preserve useful temporal details for such a long interview are significant challenges. Therefore, we propose a novel topic modeling based approach to perform context-aware analysis of the recording. Our experiments show that the proposed approach outperforms context-unaware methods and the challenge baselines for all metrics.
Hardware-Based Trusted Computing Architectures for Isolation and Attestation

Attackers target many different types of computer systems in use today, exploiting software vulnerabilities to take over the device and make it act maliciously. Reports of numerous attacks have been published, against the constrained embedded devices of the Internet of Things, mobile devices like smartphones and tablets, high-performance desktop and server environments, as well as complex industrial control systems. Trusted computing architectures give users and remote parties like software vendors guarantees about the behaviour of the software they run, protecting them against software-level attackers. This paper defines the security properties offered by them, and presents detailed descriptions of twelve hardware-based attestation and isolation architectures from academia and industry. We compare all twelve designs with respect to the security properties and architectural features they offer. The presented architectures have been designed for a wide range of devices, supporting different security properties.
Mosaic: A low-cost mobile sensing system for urban air quality monitoring

Air quality monitoring has attracted a lot of attention from governments, academia and industry, especially for PM2.5 due to its significant impact on our respiratory systems. In this paper, we present the design, implementation, and evaluation of Mosaic, a low cost urban PM2.5 monitoring system based on mobile sensing. In Mosaic, a small number of air quality monitoring nodes are deployed on city buses to measure air quality. Current low-cost particle sensors based on light-scattering, however, are vulnerable to airflow disturbance on moving vehicles. In order to address this problem, we build our air quality monitoring nodes, Mosaic-Nodes, with a novel constructive airflow-disturbance design based on a carefully tuned airflow structure and a GPS-assisted filtering method. Further, the buses used for system deployment are selected by a novel algorithm which achieves both high coverage and low computation overhead. The collected sensor data is also used to calculate the PM2.5 of locations without direct measurements by an existing inference model. We apply the Mosaic system in a testing urban area which includes more than 70 point-of-interests. Results show that the Mosaic system can accurately obtain the urban air quality with high coverage and low cost.
Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings

We present a scalable Bayesian multi-label learning model based on learning lowdimensional label embeddings. Our model assumes that each label vector is generated as a weighted combination of a set of topics (each topic being a distribution over labels), where the combination weights (i.e., the embeddings) for each label vector are conditioned on the observed feature vector. This construction, coupled with a Bernoulli-Poisson link function for each label of the binary label vector, leads to a model with a computational cost that scales in the number of positive labels in the label matrix. This makes the model particularly appealing for real-world multi-label learning problems where the label matrix is usually very massive but highly sparse. Using a data-augmentation strategy leads to full local conjugacy in our model, facilitating simple and very efficient Gibbs sampling, as well as an Expectation Maximization algorithm for inference. Also, predicting the label vector at test time does not require doing an inference for the label embeddings and can be done in closed form. We report results on several benchmark data sets, comparing our model with various state-of-the art methods.
An analysis of security issues for cloud computing

Cloud Computing is a flexible, cost-effective, and proven delivery platform for providing business or consumer IT services over the Internet. However, cloud Computing presents an added level of risk because essential services are often outsourced to a third party, which makes it harder to maintain data security and privacy, support data and service availability, and demonstrate compliance. Cloud Computing leverages many technologies (SOA, virtualization, Web 2.0); it also inherits their security issues, which we discuss here, identifying the main vulnerabilities in this kind of systems and the most important threats found in the literature related to Cloud Computing and its environment as well as to identify and relate vulnerabilities and threats with possible solutions.
Discovering Causal Signals in Images

This paper establishes the existence of observable footprints that reveal the causal dispositions of the object categories appearing in collections of images. We achieve this goal in two steps. First, we take a learning approach to observational causal discovery, and build a classifier that achieves state-of-the-art performance on finding the causal direction between pairs of random variables, given samples from their joint distribution. Second, we use our causal direction classifier to effectively distinguish between features of objects and features of their contexts in collections of static images. Our experiments demonstrate the existence of a relation between the direction of causality and the difference between objects and their contexts, and by the same token, the existence of observable signals that reveal the causal dispositions of objects.
Exploring the design space of robots: Children's perspectives

Children’s perceptions and evaluations of different robot designs are an important unexplored area within robotics research considering that many robots are specifically designed for children. To examine children’s feelings and attitudes towards robots, a large sample of children (N = 159) evaluated 40 robot images by completing a questionnaire for each image, which enquired about robot appearance, robot personality dimensions and robot emotions. Results showed that depending on a robot’s appearance children clearly distinguished robots in terms of their intentions (i.e. friendly vs. unfriendly), their capability to understand, and their emotional expression. Results of a principal components analysis of the children’s ratings of the robots’ personality attributes revealed two dimensions labelled ‘Behavioural Intention’ and ‘Emotional Expression’. Robots were classified according to their scores on these two dimensions and a content analysis of their appearance was conducted in an attempt to identify salient features of different robot personalities. Children judged human-like robots as aggressive, but human–machine robots as friendly. Results on children’s perceptions of the robots’ behavioural intentions provided tentative empirical support for the Uncanny Valley, hypothesized by (Mori, M., 1970), reflecting a situation where robots are very human-like, but still distinguishable from humans, evoking a feeling of discomfort or repulsion. The paper concludes with a discussion of design implications for robots, and the use of robots in educational contexts. ! 2006 Elsevier B.V. All rights reserved.
Estimation on Location, Velocity, and Acceleration With High Precision for Collision Avoidance

An approach is proposed to estimate the location, velocity, and acceleration of a target vehicle to avoid a possible collision. Radial distance, velocity, and acceleration are extracted from the hybrid linear frequency modulation (LFM)/frequency-shift keying (FSK) echoed signals and then processed using the Kalman filter and the trilateration process. This approach proves to converge fast with good accuracy. Two other approaches, i.e., an extended Kalman filter (EKF) and a two-stage Kalman filter (TSKF), are used as benchmarks for comparison. Several scenarios of vehicle movement are also presented to demonstrate the effectiveness of this approach.
Mental imagery in the motor context

The working hypothesis of the paper is that motor images are endowed with the same properties as those of the (corresponding) motor representations, and therefore have the same functional relationship to the imagined or represented movement and the same causal role in the generation of this movement. The fact that the timing of simulated movements follows the same constraints as that of actually executed movements is consistent with this hypothesis. Accordingly, many neural mechanisms are activated during motor imagery, as revealed by a sharp increase in tendinous reflexes in the limb imagined to move, and by vegetative changes which correlate with the level of mental effort. At the cortical level, a specific pattern of activation, that closely resembles that of action execution, is observed in areas devoted to motor control. This activation might be the substrate for the effects of mental training. A hierarchical model of the organization of action is proposed: this model implies a short-term memory storage of a 'copy' of the various representational steps. These memories are erased when an action corresponding to the represented goal takes place. By contrast, if the action is incompletely or not executed, the whole system remains activated, and the content of the representation is rehearsed. This mechanism would be the substrate for conscious access to this content during motor imagery and mental training.
SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning

Synthesizing SQL queries from natural language is a long-standing open problem and has been attracting considerable interest recently. Toward solving the problem, the de facto approach is to employ a sequence-to-sequence-style model. Such an approach will necessarily require the SQL queries to be serialized. Since the same SQL query may have multiple equivalent serializations, training a sequenceto-sequence-style model is sensitive to the choice from one of them. This phenomenon is documented as the “order-matters” problem. Existing state-of-the-art approaches rely on reinforcement learning to reward the decoder when it generates any of the equivalent serializations. However, we observe that the improvement from reinforcement learning is limited. In this paper, we propose a novel approach, i.e., SQLNet, to fundamentally solve this problem by avoiding the sequence-to-sequence structure when the order does not matter. In particular, we employ a sketch-based approach where the sketch contains a dependency graph so that one prediction can be done by taking into consideration only the previous predictions that it depends on. In addition, we propose a sequence-to-set model as well as the column attention mechanism to synthesize the query based on the sketch. By combining all these novel techniques, we show that SQLNet can outperform the prior art by 9% to 13% on the WikiSQL task.
An operational and performance overview of the IRIDIUM low earth orbit satellite system

Today, both the military and commercial sectors are placing an increased emphasis on global communications. This has prompted the development of several low earth orbit satellite systems that promise worldwide connectivity and real-time voice communications. This article provides a tutorial overview of the IRIDIUM low earth orbit satellite system and performance results obtained via simulation. First, it presents an overview of key IRIDIUM design parameters and features. Then, it examines the issues associated with routing in a dynamic network topology, focusing on network management and routing algorithm selection. Finally, it presents the results of the simulation and demonstrates that the IRIDIUM system is a robust system capable of meeting published specifications.
Advances in opportunistic radio technologies for TVWS

Cognitive radio has been an active research area in wireless communications over the past 10 years. TV Digital Switch Over resulted in new regulatory regimes, which offer the first large-scale opportunity for cognitive radio and networks. This article considers the most recent regulatory rules for TV White Space opportunistic usage, and proposes technologies to operate in these bands. It addresses techniques to assess channel vacancy by the cognitive radio, focusing on the two incumbent systems of the TV bands, namely TV stations and wireless microphones. Spectrum-sensing performance is discussed under TV White Space regulation parameters. Then, modulation schemes for the opportunistic radio are discussed, showing the limitations of classical multi-carrier techniques and the advantages of filter bank modulations. In particular, the low adjacent band leakage of filter bank is addressed, and its benefit for spectrum pooling is stressed as a means to offer broadband access through channel aggregation.
MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization

Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of magnetic resonance (MR) images. Unfortunately, MR images always contain a significant amount of noise caused by operator performance, equipment, and the environment, which can lead to serious inaccuracies with segmentation. A robust segmentation technique based on an extension to the traditional fuzzy c-means (FCM) clustering algorithm is proposed in this paper. A neighborhood attraction, which is dependent on the relative location and features of neighboring pixels, is shown to improve the segmentation performance dramatically. The degree of attraction is optimized by a neural-network model. Simulated and real brain MR images with different noise levels are segmented to demonstrate the superiority of the proposed technique compared to other FCM-based methods. This segmentation method is a key component of an MR image-based classification system for brain tumors, currently being developed.
A Wideband PET Inkjet-Printed Antenna for UHF RFID

A new antenna, designed on a polyethylene terephthalate (PET) substrate and implemented by inkjet printing using a conductive ink, is proposed as a passive tag antenna for UHF radio frequency identification (RFID). The operating bandwidth of the proposed antenna is very large since it encompasses all worldwide UHF RFID bands and extends well beyond at both edges. Moreover, it has a very simple geometry, can be easily tuned to feed many of the commercial RFID chips, and is very robust with respect to realization tolerances. The antenna has been designed using a general-purpose 3-D computer-aided design (CAD), CST Microwave Studio, and measured results are in very good agreement with simulations. The proposed passive RFID tag meets both the objectives of low-cost and size reduction.
An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition

Achieving balance between convergence and diversity is a key issue in evolutionary multiobjective optimization. Most existing methodologies, which have demonstrated their niche on various practical problems involving two and three objectives, face significant challenges in many-objective optimization. This paper suggests a unified paradigm, which combines dominance- and decomposition-based approaches, for many-objective optimization. Our major purpose is to exploit the merits of both dominance- and decomposition-based approaches to balance the convergence and diversity of the evolutionary process. The performance of our proposed method is validated and compared with four state-of-the-art algorithms on a number of unconstrained benchmark problems with up to 15 objectives. Empirical results fully demonstrate the superiority of our proposed method on all considered test instances. In addition, we extend this method to solve constrained problems having a large number of objectives. Compared to two other recently proposed constrained optimizers, our proposed method shows highly competitive performance on all the constrained optimization problems.
An 11-Mb/s 2.1-mW Synchronous Superregenerative Receiver at 2.4 GHz

This paper presents a low-voltage low-power high-speed superregenerative receiver operating in the 2.4-GHz industrial-scientific-medical band. The receiver uses an architecture in which, thanks to the presence of a phase-locked loop, the quench oscillator is operated synchronously with the received data at a quench frequency equal to the data rate. This mode of operation has several benefits. Firstly, the traditional problem of poor selectivity in this type of receiver is to a large extent overcome. Secondly, considerably higher data rates can be achieved than with classical receivers. Thirdly, the bit envelope can be matched to the superregenerative oscillator, which improves sensitivity. The receiver includes an RF front end optimized to support high quench frequencies at low supply voltages, responding to todays increasing demand for high speed and low power consumption. The prototype implemented is very simple and achieves a data rate of 11 Mb/s with a current consumption of 1.75 mA at a supply voltage of 1.2 V - an excellent tradeoff between cost, performance, and power consumption.
Metaphors We Think With: The Role of Metaphor in Reasoning

The way we talk about complex and abstract ideas is suffused with metaphor. In five experiments, we explore how these metaphors influence the way that we reason about complex issues and forage for further information about them. We find that even the subtlest instantiation of a metaphor (via a single word) can have a powerful influence over how people attempt to solve social problems like crime and how they gather information to make "well-informed" decisions. Interestingly, we find that the influence of the metaphorical framing effect is covert: people do not recognize metaphors as influential in their decisions; instead they point to more "substantive" (often numerical) information as the motivation for their problem-solving decision. Metaphors in language appear to instantiate frame-consistent knowledge structures and invite structurally consistent inferences. Far from being mere rhetorical flourishes, metaphors have profound influences on how we conceptualize and act with respect to important societal issues. We find that exposure to even a single metaphor can induce substantial differences in opinion about how to solve social problems: differences that are larger, for example, than pre-existing differences in opinion between Democrats and Republicans.
Keeping a Good Attitude: A Quaternion-Based Orientation Filter for IMUs and MARGs

Orientation estimation using low cost sensors is an important task for Micro Aerial Vehicles (MAVs) in order to obtain a good feedback for the attitude controller. The challenges come from the low accuracy and noisy data of the MicroElectroMechanical System (MEMS) technology, which is the basis of modern, miniaturized inertial sensors. In this article, we describe a novel approach to obtain an estimation of the orientation in quaternion form from the observations of gravity and magnetic field. Our approach provides a quaternion estimation as the algebraic solution of a system from inertial/magnetic observations. We separate the problems of finding the "tilt" quaternion and the heading quaternion in two sub-parts of our system. This procedure is the key for avoiding the impact of the magnetic disturbances on the roll and pitch components of the orientation when the sensor is surrounded by unwanted magnetic flux. We demonstrate the validity of our method first analytically and then empirically using simulated data. We propose a novel complementary filter for MAVs that fuses together gyroscope data with accelerometer and magnetic field readings. The correction part of the filter is based on the method described above and works for both IMU (Inertial Measurement Unit) and MARG (Magnetic, Angular Rate, and Gravity) sensors. We evaluate the effectiveness of the filter and show that it significantly outperforms other common methods, using publicly available datasets with ground-truth data recorded during a real flight experiment of a micro quadrotor helicopter.
Pairwise Global Alignment of Protein Interaction Networks by Matching Neighborhood Topology

We describe an algorithm, IsoRank, for global alignment of two protein-protein interaction (PPI) networks. IsoRank aims to maximize the overall match between the two networks; in contrast, much of previous work has focused on the local alignment problem— identifying many possible alignments, each corresponding to a local region of similarity. IsoRank is guided by the intuition that a protein should be matched with a protein in the other network if and only if the neighbors of the two proteins can also be well matched. We encode this intuition as an eigenvalue problem, in a manner analogous to Google’s PageRank method. We use IsoRank to compute the first known global alignment between the S. cerevisiae and D. melanogaster PPI networks. The common subgraph has 1420 edges and describes conserved functional components between the two species. Comparisons of our results with those of a well-known algorithm for local network alignment indicate that the globally optimized alignment resolves ambiguity introduced by multiple local alignments. Finally, we interpret the results of global alignment to identify functional orthologs between yeast and fly; our functional ortholog prediction method is much simpler than a recently proposed approach and yet provides results that are more comprehensive.
Neural Network Bottleneck Features for Language Identifica tion

This paper presents the application of Neural Network Bottleneck (BN) features in Language Identification (LID). BN f eatures are generally used for Large Vocabulary Speech Recogn ition in conjunction with conventional acoustic features, s uch as MFCC or PLP. We compare the BN features to several common types of acoustic features used in the state-of-the-art LID systems. The test set is from DARPA RATS (Robust Automatic Transcription of Speech) program, which seeks to advance state-of-the-art detection capabilities on audio from hig hly degraded radio communication channels. On this type of noisy data, we show that in average, the BN features provide a 45% relative improvement in theCavgor Equal Error Rate (EER) metrics across several test duration conditions, with resp ect to our single best acoustic features.