nils-herrmann/clean_scirepeval huggingface dataset:
A DCVSL Delay Cell for Fast Low Power Frequency Synthesis Applications

In this paper, a low-cost, power efficient and fast Differential Cascode Voltage-Switch-Logic (DCVSL) based delay cell (named DCVSL-R) is proposed. We use the DCVSL-R cell to implement high frequency and power-critical delay cells and flip-flops of ring oscillators and frequency dividers. When compared to TSPC, DCVSL circuits offer small input and clock capacitance and a symmetric differential loading for previous RF stages. When compared to CML, they offer low transistor count, no headroom limitation, rail-to-rail swing and no static current consumption. However, DCVSL circuits suffer from a large low-to-high propagation delay, which limits their speed and results in asymmetrical output waveforms. The proposed DCVSL-R circuit embodies the benefits of DCVSL while reducing the total propagation delay, achieving faster operation. DCVSL-R also generates symmetrical output waveforms which are critical for differential circuits. Another contribution of this work is a closed-form delay model that predicts the speed of DCVSL circuits with 8% worst case accuracy. We implement two ring-oscillator-based VCOs in 0.13 μm technology with DCVSL and DCVSL-R delay cells. Measurements show that the proposed DCVSL-R based VCO consumes 30% less power than the DCVSL VCO for the same oscillation frequency (2.4 GHz) and same phase noise (-113 dBc/Hz at 10 MHz). DCVSL-R circuits are also used to implement the high frequency dual modulus prescaler (DMP) of a 2.4 GHz frequency synthesizer in 0.18 μm technology. The DMP consumes only 0.8 mW at 2.48 GHz, a 40% reduction in power when compared to other reported DMPs with similar division ratios and operating frequencies. The RF buffer that drives the DMP consumes only 0.27 mW, demonstrating the lowest combined DMP and buffer power consumption among similar synthesizers in literature.
Asynchronous Distributed ADMM for Consensus Optimization

Distributed optimization algorithms are highly attractive for solving big data problems. In particular, many machine learning problems can be formulated as the global consensus optimization problem, which can then be solved in a distributed manner by the alternating direction method of multipliers (ADMM) algorithm. However, this suffers from the straggler problem as its updates have to be synchronized. In this paper, we propose an asynchronous ADMM algorithm by using two conditions to control the asynchrony: partial barrier and bounded delay. The proposed algorithm has a simple structure and good convergence guarantees (its convergence rate can be reduced to that of its synchronous counterpart). Experiments on different distributed ADMM applications show that asynchrony reduces the time on network waiting, and achieves faster convergence than its synchronous counterpart in terms of the wall clock time.
Learning to Play With Intrinsically-Motivated, Self-Aware Agents

Infants are experts at playing, with an amazing ability to generate novel structured behaviors in unstructured environments that lack clear extrinsic reward signals. We seek to mathematically formalize these abilities using a neural network that implements curiosity-driven intrinsic motivation. Using a simple but ecologically naturalistic simulated environment in which an agent can move and interact with objects it sees, we propose a “world-model” network that learns to predict the dynamic consequences of the agent’s actions. Simultaneously, we train a separate explicit “self-model” that allows the agent to track the error map of its worldmodel. It then uses the self-model to adversarially challenge the developing world-model. We demonstrate that this policy causes the agent to explore novel and informative interactions with its environment, leading to the generation of a spectrum of complex behaviors, including ego-motion prediction, object attention, and object gathering. Moreover, the world-model that the agent learns supports improved performance on object dynamics prediction, detection, localization and recognition tasks. Taken together, our results are initial steps toward creating flexible autonomous agents that self-supervise in realistic physical environments.
Instant inkjet circuits: lab-based inkjet printing to support rapid prototyping of UbiComp devices

This paper introduces a low cost, fast and accessible technology to support the rapid prototyping of functional electronic devices. Central to this approach of 'instant inkjet circuits' is the ability to print highly conductive traces and patterns onto flexible substrates such as paper and plastic films cheaply and quickly. In addition to providing an alternative to breadboarding and conventional printed circuits, we demonstrate how this technique readily supports large area sensors and high frequency applications such as antennas. Unlike existing methods for printing conductive patterns, conductivity emerges within a few seconds without the need for special equipment. We demonstrate that this technique is feasible using commodity inkjet printers and commercially available ink, for an initial investment of around US$300. Having presented this exciting new technology, we explain the tools and techniques we have found useful for the first time. Our main research contribution is to characterize the performance of instant inkjet circuits and illustrate a range of possibilities that are enabled by way of several example applications which we have built. We believe that this technology will be of immediate appeal to researchers in the ubiquitous computing domain, since it supports the fabrication of a variety of functional electronic device prototypes.
What's in a Name? Understanding the Interplay between Titles, Content, and Communities in Social Media

Creating, placing, and presenting social media content is a difficult problem. In addition to the quality of the content itself, several factors such as the way the content is presented (the title), the community it is posted to, whether it has been seen before, and the time it is posted determine its success. There are also interesting interactions between these factors. For example, the language of the title should be targeted to the community where the content is submitted, yet it should also highlight the distinctive nature of the content. In this paper, we examine how these factors interact to determine the popularity of social media content. We do so by studying resubmissions, i.e., content that has been submitted multiple times, with multiple titles, to multiple different communities. Such data allows us to ‘tease apart’ the extent to which each factor influences the success of that content. The models we develop help us understand how to better target social media content: by using the right title, for the right community, at the right time.
Mosaicing New Views: The Crossed-Slits Projection

Weintroduceanewkindofmosaicing,where thepositionof the samplingstrip varies asa functionof the input camera location. The new images that are generated this way correspond to a new projection model defined by two slits, termed here the Crossed-Slits (X-Slits) projection. In this projection model, every 3D point is projected by a ray defined as the line that passes through that point and intersects the two slits. The intersection of the projection rays with the imaging surface defines the image. X-Slits mosaicing provides two benefits. First, the generated mosaics are closer to perspective images than traditional pushbroom mosaics. Second, by simple manipulationsof the strip sampling function,wecanchange the locationof oneof the virtual slits, providingavirtualwalkthroughof aX-slits camera; all this can be done without recovering any 3D geometry and without calibration. A number of examples where we translate the virtual camera and change its orientation are given; the examples demonstrate realistic changes in parallax, reflections, and occlusions.
A comparative assessment of answer quality on four question answering sites

Question answering (Q&A) sites, where communities of volunteers answer questions, may provide faster, cheaper, and better services than traditional institutions. However, like other Web 2.0 platforms, user-created content raises concerns about information quality. At the same time, Q&A sites may provide answers of different quality because they have different communities and technological platforms. This paper compares answer quality on four Q&A sites: Askville, WikiAnswers, Wikipedia Reference Desk, and Yahoo! Answers. Findings indicate that: 1) the use of similar collaborative processes on these sites results in a wide range of outcomes. Significant differences in answer accuracy, completeness, and verifiability were found; 2) answer multiplication does not always result in better information. Answer multiplication yields more complete and verifiable answers but does not result in higher accuracy levels; and 3) a Q&A site’s popularity does not correlate with its answer quality, on all three measures.
Pitch determination and voice quality analysis using Subharmonic-to-Harmonic Ratio

This paper presents an improvement of a previously proposed pitch determination algorithm (PDA). Particularly aiming at handling alternate cycles in speech signal, the algorithm estimates pitch through spectrum shifting on logarithmic frequency scale and calculating the Subharmonic-to-Harmonic Ratio (SHR). The evaluation results on two databases show that this algorithm performs considerably better than other PDAs compared. Application of SHR to voice quality analysis task is also presented. The implementation and evaluation routines are available from &#60;http://mel.speech.nwu.edu/sunxj/pda.htm>.
Phase-Encoded Speech Spectrograms

Spectrograms of speech and audio signals are time-frequency densities, and by construction, they are non-negative and do not have phase associated with them. Under certain conditions on the amount of overlap between consecutive frames and frequency sampling, it is possible to reconstruct the signal from the spectrogram. Deviating from this requirement, we develop a new technique to incorporate the phase of the signal in the spectrogram by satisfying what we call as the delta dominance condition, which in general is different from the well known minimum-phase condition. In fact, there are signals that are delta dominant but not minimum-phase and vice versa. The delta dominance condition can be satisfied in multiple ways, for example by placing a Kronecker impulse of the right amplitude or by choosing a suitable window function. A direct consequence of this novel way of constructing the spectrograms is that the phase of the signal is directly encoded or embedded in the spectrogram. We also develop a reconstruction methodology that takes such phase-encoded spectrograms and obtains the signal using the discrete Fourier transform (DFT). It is envisaged that the new class of phase-encoded spectrogram representations would find applications in various speech processing tasks such as analysis, synthesis, enhancement, and recognition.
Predicting Deeper into the Future of Semantic Segmentation

The ability to predict and therefore to anticipate the future is an important attribute of intelligence. It is also of utmost importance in real-time systems, e.g. in robotics or autonomous driving, which depend on visual scene understanding for decision making. While prediction of the raw RGB pixel values in future video frames has been studied in previous work, here we introduce the novel task of predicting semantic segmentations of future frames. Given a sequence of video frames, our goal is to predict segmentation maps of not yet observed video frames that lie up to a second or further in the future. We develop an autoregressive convolutional neural network that learns to iteratively generate multiple frames. Our results on the Cityscapes dataset show that directly predicting future segmentations is substantially better than predicting and then segmenting future RGB frames. Prediction results up to half a second in the future are visually convincing and are much more accurate than those of a baseline based on warping semantic segmentations using optical flow.
Real-time data warehousing for business intelligence

Real-time ETL and data warehouse multidimensional modeling (DMM) of business operational data has become an important research issue in the area of real-time data warehousing (RTDW). In this study, some of the recently proposed real-time ETL technologies from the perspectives of data volumes, frequency, latency, and mode have been discussed. In addition, we highlight several advantages of using semi-structured DMM (i.e. XML) in RTDW instead of traditional structured DMM (i.e., relational). We compare the two DMMs on the basis of four characteristics: heterogeneous data integration, types of measures supported, aggregate query processing, and incremental maintenance. We implemented the RTDW framework for an example telecommunication organization. Our experimental analysis shows that if the delay comes from the incremental maintenance of DMM, no ETL technology (full-reloading or incremental-loading) can help in real-time business intelligence.
A unified sparse signal decomposition and reconstruction framework for elimination of muscle artifacts from ECG signal

Removal of muscle artifacts from the ECG signals is crucial for a reliable and accurate measurement of local features of ECG signals. In this paper, we present an automatic method for removal of muscle artifacts from ECG signals, based on four steps: decomposing ECG signal using sparse signal decomposition on mixed dictionaries; obtaining QRS complex signal; determining time-instants of R-peak; and removal of muscle artifacts from ECG signal. The noise reduction performance of the proposed method is tested and validated using ECG signals taken from a standard MIT-BIH Arrhythmia database. The reconstructed signals are assessed using both subjective quality assessment test and objective quality assessment metrics. Performance evaluation results show that the proposed method outperforms other existing ECG denoising methods inadequately removing the muscle artifacts without significantly distorting the morphologies of P-wave, QRS-complex and T-wave of the ECG signals.
Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity

Spike timing dependent plasticity (STDP) is a learning rule that modifies synaptic strength as a function of the relative timing of pre- and postsynaptic spikes. When a neuron is repeatedly presented with similar inputs, STDP is known to have the effect of concentrating high synaptic weights on afferents that systematically fire early, while postsynaptic spike latencies decrease. Here we use this learning rule in an asynchronous feedforward spiking neural network that mimics the ventral visual pathway and shows that when the network is presented with natural images, selectivity to intermediate-complexity visual features emerges. Those features, which correspond to prototypical patterns that are both salient and consistently present in the images, are highly informative and enable robust object recognition, as demonstrated on various classification tasks. Taken together, these results show that temporal codes may be a key to understanding the phenomenal processing speed achieved by the visual system and that STDP can lead to fast and selective responses.
The Affordance Template ROS package for robot task programming

This paper introduces the Affordance Template ROS package for quickly programming, adjusting, and executing robot applications in the ROS RViz environment. This package extends the capabilities of RViz interactive markers  by allowing an operator to specify multiple end-effector waypoint locations and grasp poses in object-centric coordinate frames and to adjust these waypoints in order to meet the run-time demands of the task (specifically, object scale and location). The Affordance Template package stores task specifications in a robot-agnostic JSON description format such that it is trivial to apply a template to a new robot. As such, the Affordance Template package provides a robot-generic ROS tool appropriate for building semi-autonomous, manipulation-based applications. Affordance Templates were developed by the NASA-JSC DARPA Robotics Challenge (DRC) team and have since successfully been deployed on multiple platforms including the NASA Valkyrie and Robonaut 2 humanoids, the University of Texas Dreamer robot and the Willow Garage PR2. In this paper, the specification and implementation of the affordance template package is introduced and demonstrated through examples for wheel (valve) turning, pick-and-place, and drill grasping, evincing its utility and flexibility for a wide variety of robot applications.
Review of underwater SLAM techniques

SLAM (Simultaneous Localization and Mapping) for underwater vehicles is a challenging research topic due to the limitations of underwater localization sensors and error accumulation over long-term operations. Furthermore, acoustic sensors for mapping often provide noisy and distorted images or low-resolution ranging, while video images provide highly detailed images but are often limited due to turbidity and lighting. This paper presents a review of the approaches used in state-of-the-art SLAM techniques: Extended Kalman Filter SLAM (EKF-SLAM), FastSLAM, GraphSLAM and its application in underwater environments.
Behavior-Based Grade Prediction for MOOCs Via Time Series Neural Networks

We present a novel method for predicting the evolution of a student's grade in massive open online courses (MOOCs). Performance prediction is particularly challenging in MOOC settings due to per-student assessment response sparsity and the need for personalized models. Our method overcomes these challenges by incorporating another, richer form of data collected from each student—lecture video-watching clickstreams—into the machine learning feature set, and using that to train a time series neural network that learns from both prior performance and clickstream data. Through evaluation on two MOOC datasets, we find that our algorithm outperforms a baseline of average past performance by more than 60% on average, and a lasso regression baseline by more than 15%. Moreover, the gains are higher when the student has answered fewer questions, underscoring their ability to provide instructors with early detection of struggling and/or advanced students. We also show that despite these gains, when taken alone, none of the behavioral features are particularly correlated with performance, emphasizing the need to consider their combined effect and nonlinear predictors. Finally, we discuss how course instructors can use these predictive learning analytics to stage student interventions.
Data Mining Methods for Network Intrusion Detection

Network intrusion detection systems have become a standard component in security infrastructures. Unfortunately, current systems are poor at detecting novel attacks without an unacceptable level of false alarms. We propose that the solution to this problem is the application of an ensemble of data mining techniques which can be applied to network connection data in an offline environment, augmenting existing real-time sensors. In this paper, we expand on our motivation, particularly with regard to running in an offline environment, and our interest in multisensor and multimethod correlation. We then review existing systems, from commercial systems, to research based intrusion detection systems. Next we survey the state of the art in the area. Standard datasets and feature extraction turned out to be more important than we had initially anticipated, so each can be found under its own heading. Next, we review the actual data mining methods that have been proposed or implemented. We conclude by summarizing the open problems in this area and proposing a new research project to answer some of these open problems.
Traffic flow on networks : conservation laws models

The underlaying equations for the models we consider are hyperbolic systems of conservation laws in one dimension: ut + f(u)x = 0, where x ∈ R, u ∈ R and Df(u) is assumed to have real distinct eigenvalues. The main mathematical novelty is to describe the dynamics on a network, represented by a directed topological graph, instead of a real line. The more advanced results are available for the scalar case, i.e. n = 1.
Relational Bayesian Networks

A new method is developed to represent probabilistic relations on multiple random events. Where previously knowledge bases containing probabilistic rules were used for this purpose, here a probabilitydistributionover the relations is directly represented by a Bayesian network. By using a powerful way of specifying conditional probability distributions in these networks, the resulting formalism is more expressive than the previous ones. Particularly, it provides for constraints on equalities of events, and it allows to define complex, nested combination functions.
Large-Scale Query-by-Image Video Retrieval Using Bloom Filters

We consider the problem of using image queries to retrieve videos from a database. Our focus is on large-scale applications, where it is infeasible to index each database video frame independently. Our main contribution is a framework based on Bloom filters, which can be used to index long video segments, enabling efficient image-to-video comparisons. Using this framework, we investigate several retrieval architectures, by considering different types of aggregation and different functions to encode visual information – these play a crucial role in achieving high performance. Extensive experiments show that the proposed technique improves mean average precision by 24% on a public dataset, while being 4× faster, compared to the previous state-of-the-art.
Verified by Visa and MasterCard SecureCode: Or, How Not to Design Authentication

Banks worldwide are starting to authenticate online card transactions using the ‘3-D Secure’ protocol, which is branded as Verified by Visa and MasterCard SecureCode. This has been partly driven by the sharp increase in online fraud that followed the deployment of EMV smart cards for cardholder-present payments in Europe and elsewhere. 3-D Secure has so far escaped academic scrutiny; yet it might be a textbook example of how not to design an authentication protocol. It ignores good design principles and has significant vulnerabilities, some of which are already being exploited. Also, it provides a fascinating lesson in security economics. While other single sign-on schemes such as OpenID, InfoCard and Liberty came up with decent technology they got the economics wrong, and their schemes have not been adopted. 3-D Secure has lousy technology, but got the economics right (at least for banks and merchants); it now boasts hundreds of millions of accounts. We suggest a path towards more robust authentication that is technologically sound and where the economics would work for banks, merchants and customers – given a gentle regulatory nudge.