nils-herrmann/clean_scirepeval huggingface dataset:
Hyperdimensional Data Analysis Using Parallel Coordinates 1

This paper presents the basic results for using the parallel coordinate representation as a high dimensional data analysis tool. Several alternatives are reviewed. The basic algorithm for parallel coordinates is laid out and a discussion of its properties as a projective transformation are shown. The several of the duality results are discussed along with their interpretations as data analysis tools. A discussion of permutations of the parallel coordinate axes is given and some examples are given. Some extensions of the parallel coordinate idea are given. The paper closes with a discussion of implementation and some of our experiences are relayed. 1This research was supported by the Air Force Office of Scientific Research under grant number AFOSR-870179, by the Army Research Office under contract number DAAL03-87-K-0087 and by the National Science Foundation under grant number DMS-8701931 . Hyperdimensional Data Analysis Using Parallel Coordinates
Graph-Based Metric Embedding for Next POI Recommendation

With the rapid prevalence of smart mobile devices and the dramatic proliferation of location-based social networks (LBSNs), point of interest (POI) recommendation has become an important means to help people discover attractive and interesting places. In this paper, we investigate the problem of next POI recommendation by considering the sequential influences of POIs, as a natural extension of the general POI recommendation, but it is more challenging than the general POI recommendation, due to that 1) users’ preferences are dynamic, and the next POI recommendation requires tracking the change of user preferences in a real-time manner; and 2) the prediction space is extremely large, with millions of distinct POIs as the next prediction target, which impedes the application of classical Markov chain models. In light of the above challenges, we propose a graph-based metric embedding model which converts POIs in a low dimensional metric and tracks the dynamics of user preferences in an efficient way. Besides, the knowledge of sequential patterns of users’ check-in behaviors can be exploited and encoded in the POI embedding, which avoid the time-consuming computation of the POI-POI transition matrix or even cube as the Markov chain-based recommender models have done. In other words, our proposed method effectively unifies dynamic user preferences and sequential influence via the POI embedding. Experiments on two real large-scale datasets demonstrate a significant improvement of our proposed models in terms of recommendation accuracy, compared with the state-of-the-art methods.
The Effect of Different Writing Tasks on Linguistic Style: A Case Study of the ROC Story Cloze Task

A writer’s style depends not just on personal traits but also on her intent and mental state. In this paper, we show how variants of the same writing task can lead to measurable differences in writing style. We present a case study based on the story cloze task (Mostafazadeh et al., 2016a), where annotators were assigned similar writing tasks with different constraints: (1) writing an entire story, (2) adding a story ending for a given story context, and (3) adding an incoherent ending to a story. We show that a simple linear classifier informed by stylistic features is able to successfully distinguish among the three cases, without even looking at the story context. In addition, combining our stylistic features with language model predictions reaches state of the art performance on the story cloze challenge. Our results demonstrate that different task framings can dramatically affect the way people write.
Quality Measures for ETL Processes

ETL processes play an increasingly important role for the support of modern business operations. These business processes are centred around artifacts with high variability and diverse lifecycles, which correspond to key business entities. The apparent complexity of these activities has been examined through the prism of Business Process Management, mainly focusing on functional requirements and performance optimization. However, the quality dimension has not yet been thoroughly investigated and there is a need for a more human-centric approach to bring them closer to business-users requirements. In this paper we take a first step towards this direction by defining a sound model for ETL process quality characteristics and quantitative measures for each characteristic, based on existing literature. Our model shows dependencies among quality characteristics and can provide the basis for subsequent analysis using Goal Modeling techniques.
Prostate Histopathology: Learning Tissue Component Histograms for Cancer Detection and Classification

Radical prostatectomy is performed on approximately 40% of men with organ-confined prostate cancer. Pathologic information obtained from the prostatectomy specimen provides important prognostic information and guides recommendations for adjuvant treatment. The current pathology protocol in most centers involves primarily qualitative assessment. In this paper, we describe and evaluate our system for automatic prostate cancer detection and grading on hematoxylin & eosin-stained tissue images. Our approach is intended to address the dual challenges of large data size and the need for high-level tissue information about the locations and grades of tumors. Our system uses two stages of AdaBoost-based classification. The first provides high-level tissue component labeling of a superpixel image partitioning. The second uses the tissue component labeling to provide a classification of cancer versus noncancer, and low-grade versus high-grade cancer. We evaluated our system using 991 sub-images extracted from digital pathology images of 50 whole-mount tissue sections from 15 prostatectomy patients. We measured accuracies of 90% and 85% for the cancer versus noncancer and high-grade versus low-grade classification tasks, respectively. This system represents a first step toward automated cancer quantification on prostate digital histopathology imaging, which could pave the way for more accurately informed postprostatectomy patient care.
Feature selection for fast speech emotion recognition

In speech based emotion recognition, both acoustic features extraction and features classification are usually time consuming,which obstruct the system to be real time. In this paper, we proposea novel feature selection (FSalgorithm to filter out the low efficiency features towards fast speech emotion recognition.Firstly, each acoustic feature's discriminative ability, time consumption and redundancy are calculated. Then, we map the original feature space into a nonlinear one to select nonlinear features,which can exploit the underlying relationship among the original features. Thirdly, high discriminative nonlinear feature with low time consumption is initially preserved. Finally, a further selection is followed to obtain low redundant features based on these preserved features. The final selected nonlinear features are used in features' extraction and features' classification in our approach, we call them qualified features. The experimental results demonstrate that recognition time consumption can be dramatically reduced in not only the extraction phase but also the classification phase. Moreover, a competitive of recognition accuracy has been observed in the speech emotion recognition.
Optimal State Reference Computation With Constrained MTPA Criterion for PM Motor Drives

This research proposes a procedure that maps a PMSM torque request onto optimal state (current) references. Combining the procedure with a dynamic (current) controller yields a torque controller. The maximum torque per ampere (MTPA) criterion is used to minimize conduction and switching losses. This research extends the concept to field-weakening operation to obtain high efficiency at any machine speed. The resulting constrained MTPA criterion is formalized as an optimization problem. Since it is difficult to solve directly, the maximum and intersection torque subproblems are identified. An algorithm is obtained that maps a torque onto an optimal state reference, and it is sufficiently efficient for real-time implementation. This method is compatible with a variety of state (current) controllers with/without PWM, SPM and IPM machines with saliency and reverse saliency, and a variable dc-link voltage. The proposed procedure relies on a sufficiently accurate torque model that may not be provided using rated machine parameters. Thus, an approach to compute locally optimized machine parameters is proposed that takes magnetic saturation into account. The concept is developed on a software-in-the-loop platform and evaluated on an experimental test bench.
Modelling football match results and the efficiency of fixed-odds betting

An ordered probit regression model estimated using 15 years’ data is used to model English league football match results. As well as past match results data, the significance of the match for end-ofseason league outcomes; the involvement of the teams in cup competition; the geographical distance between the two teams’ home towns; and the average attendances of the two teams all contribute to the model’s performance. The model is used to test the weak-form efficiency of prices in the fixedodds betting market, and betting strategies with a positive expected return are identified.
Gaussian Prediction Based Attention for Online End-to-End Speech Recognition

Recently end-to-end speech recognition has obtained much attention. One of the popular models to achieve end-to-end speech recognition is attention based encoder-decoder model, which usually generating output sequences iteratively by attending the whole representations of the input sequences. However, predicting outputs until receiving the whole input sequence is not practical for online or low time latency speech recognition. In this paper, we present a simple but effective attention mechanism which can make the encoder-decoder model generate outputs without attending the entire input sequence and can apply to online speech recognition. At each prediction step, the attention is assumed to be a time-moving gaussian window with variable size and can be predicted by using previous input and output information instead of the content based computation on the whole input sequence. To further improve the online performance of the model, we employ deep convolutional neural networks as encoder. Experiments show that the gaussian prediction based attention works well and under the help of deep convolutional neural networks the online model achieves 19.5% phoneme error rate in TIMIT ASR task.
An Auditory-Based Feature Extraction Algorithm for Robust Speaker Identification Under Mismatched Conditions

An auditory-based feature extraction algorithm is presented. We name the new features as cochlear filter cepstral coefficients (CFCCs) which are defined based on a recently developed auditory transform (AT) plus a set of modules to emulate the signal processing functions in the cochlea. The CFCC features are applied to a speaker identification task to address the acoustic mismatch problem between training and testing environments. Usually, the performance of acoustic models trained in clean speech drops significantly when tested in noisy speech. The CFCC features have shown strong robustness in this kind of situation. In our experiments, the CFCC features consistently perform better than the baseline MFCC features under all three mismatched testing conditions-white noise, car noise, and babble noise. For example, in clean conditions, both MFCC and CFCC features perform similarly, over 96%, but when the signal-to-noise ratio (SNR) of the input signal is 6 dB, the accuracy of the MFCC features drops to 41.2%, while the CFCC features still achieve an accuracy of 88.3%. The proposed CFCC features also compare favorably to perceptual linear predictive (PLP) and RASTA-PLP features. The CFCC features consistently perform much better than PLP. Under white noise, the CFCC features are significantly better than RASTA-PLP, while under car and babble noise, the CFCC features provide similar performances to RASTA-PLP.
A new approach to cross-modal multimedia retrieval

The problem of joint modeling the text and image components of multimedia documents is studied. The text component is represented as a sample from a hidden topic model, learned with latent Dirichlet allocation, and images are represented as bags of visual (SIFT) features. Two hypotheses are investigated: that 1) there is a benefit to explicitly modeling correlations between the two components, and 2) this modeling is more effective in feature spaces with higher levels of abstraction. Correlations between the two components are learned with canonical correlation analysis. Abstraction is achieved by representing text and images at a more general, semantic level. The two hypotheses are studied in the context of the task of cross-modal document retrieval. This includes retrieving the text that most closely matches a query image, or retrieving the images that most closely match a query text. It is shown that accounting for cross-modal correlations and semantic abstraction both improve retrieval accuracy. The cross-modal model is also shown to outperform state-of-the-art image retrieval systems on a unimodal retrieval task.
A deep convolutional neural network based on nested residue number system

A pre-trained deep convolutional neural network (DCNN) is the feed-forward computation perspective which is widely used for the embedded vision systems. In the DCNN, the 2D convolutional operation occupies more than 90% of the computation time. Since the 2D convolutional operation performs massive multiply-accumulation (MAC) operations, conventional realizations could not implement a fully parallel DCNN. The RNS decomposes an integer into a tuple of L integers by residues of moduli set. Since no pair of modulus have a common factor with any other, the conventional RNS decomposes the MAC unit into circuits with different sizes. It means that the RNS could not utilize resources of an FPGA with uniform size. In this paper, we propose the nested RNS (NRNS), which recursively decompose the RNS. It can decompose the MAC unit into circuits with small sizes. In the DCNN using the NRNS, a 48-bit MAC unit is decomposed into 4-bit ones realized by look-up tables of the FPGA. In the system, we also use binary to NRNS converters and NRNS to binary converters. The binary to NRNS converter is realized by on-chip BRAMs, while the NRNS to binary one is realized by DSP blocks and BRAMs. Thus, a balanced usage of FPGA resources leads to a high clock frequency with less hardware. The ImageNet DCNN using the NRNS is implemented on a Xilinx Virtex VC707 evaluation board. As for the performance per area GOPS (Giga operations per second) per a slice, the proposed one is 5.86 times better than the existing best realization.
Foldable Printed Circuit Boards on Paper Substrates

This paper describes several low-cost methods for fabricating flexible electronic circuits on paper. The circuits comprise i) metallic wires (e.g., tin or zinc) that are deposited on the substrate by evaporation, sputtering, or airbrushing, and ii) discrete surface-mountable electronic components that are fastened with conductive adhesive directly to the wires. These electronic circuits—like conventional printed circuit boards—can be produced with electronic components that connect on both sides of the substrate. Unlike printed circuit boards made from fiberglass, ceramics, or polyimides, however, paper can be folded and creased (repeatedly), shaped to form threedimensional structures, trimmed using scissors, used to wick fluids (e.g., for microfluidic applications) and disposed of by incineration. Paper-based electronic circuits are thin and lightweight; they should be useful for applications in consumer electronics and packaging, for disposable systems for uses in the military and homeland security, for applications in medical sensing or low-cost portable diagnostics, for paper-based microelectromechanical systems, and for applications involving textiles.
Factors for Successful e-Government Adoption : a Conceptual Framework

Canada has been the world’s leader in e-Government maturity for the last five years. The global average for government website usage by citizens is about 30%. In Canada, this statistic is over 51%. The vast majority of Canadians visit government websites to obtain information, rather than interacting or transacting with the government. It seems that the rate of adoption of e-Government has globally fallen below expectations although some countries are doing better than others. Clearly, a better understanding of why and how citizens use government websites, and their general dispositions towards e-Government is an important research issue. This paper initiates discussion of this issue by proposing a conceptual model of e-Government adoption that places users as the focal point for e-Government adoption strategy.
Knowledge Adaptation: Teaching to Adapt

Domain adaptation is crucial in many real-world applications where the distribution of the training data differs from the distribution of the test data. Previous Deep Learning-based approaches to domain adaptation need to be trained jointly on source and target domain data and are therefore unappealing in scenarios where models need to be adapted to a large number of domains or where a domain is evolving, e.g. spam detection where attackers continuously change their tactics. To fill this gap, we propose Knowledge Adaptation, an extension of Knowledge Distillation (Bucilua et al., 2006; Hinton et al., 2015) to the domain adaptation scenario. We show how a student model achieves state-of-the-art results on unsupervised domain adaptation from multiple sources on a standard sentiment analysis benchmark by taking into account the domain-specific expertise of multiple teachers and the similarities between their domains. When learning from a single teacher, using domain similarity to gauge trustworthiness is inadequate. To this end, we propose a simple metric that correlates well with the teacher’s accuracy in the target domain. We demonstrate that incorporating high-confidence examples selected by this metric enables the student model to achieve state-of-the-art performance in the single-source scenario.
Learning Visual Feature Spaces for Robotic Manipulation with Deep Spatial Autoencoders

Reinforcement learning provides a powerful and flexible framework for automated acquisition of robotic motion skills. However, applying reinforcement learning requires a sufficiently detailed representation of the state, including the configuration of task-relevant objects. We present an approach that automates state-space construction by learning a state representation directly from camera images. Our method uses a deep spatial autoencoder to acquire a set of feature points that describe the environment for the current task, such as the positions of objects, and then learns a motion skill with these feature points using an efficient reinforcement learning method based on local linear models. The resulting controller reacts continuously to the learned feature points, allowing the robot to dynamically manipulate objects in the world with closed-loop control. We demonstrate our method with a PR2 robot on tasks that include pushing a free-standing toy block, picking up a bag of rice using a spatula, and hanging a loop of rope on a hook at various positions. In each task, our method automatically learns to track task-relevant objects and manipulate their configuration with the robot’s arm.
Towards driving autonomously: Autonomous cruise control in urban environments

For automatic driving, vehicles must be able to recognize their environment and take control of the vehicle. The vehicle must perceive relevant objects, which includes other traffic participants as well as infrastructure information, assess the situation and generate appropriate actions. This work is a first step of integrating previous works on environment perception and situation analysis toward automatic driving strategies. We present a method for automatic cruise control of vehicles in urban environments. The longitudinal velocity is influenced by the speed limit, the curvature of the lane, the state of the next traffic light and the most relevant target on the current lane. The necessary acceleration is computed in respect to the information which is estimated by an instrumented vehicle.
Design and evaluation of a hardware/software FPGA-based system for fast image processing

We evaluate the performance of a hardware/software architecture designed to perform a wide range of fast image processing tasks. The system ar chitecture is based on hardware featuring a Field Programmable Gate Array (FPGA) co-processor and a h ost computer. A LabVIEW TM host application controlling a frame grabber and an industrial camer a is used to capture and exchange video data with t he hardware co-processor via a high speed USB2.0 chann el, implemented with a standard macrocell. The FPGA accelerator is based on a Altera Cyclone II ch ip and is designed as a system-on-a-programmablechip (SOPC) with the help of an embedded Nios II so ftware processor. The SOPC system integrates the CPU, external and on chip memory, the communication channel and typical image filters appropriate for the evaluation of the system performance. Measured tran sfer rates over the communication channel and processing times for the implemented hardware/softw are logic are presented for various frame sizes. A comparison with other solutions is given and a rang e of applications is also discussed.
Modeling the closed-current loop of PWM boost DC-DC converters operating in CCM with peak current-mode control

This paper derives the transfer function from error voltage to duty cycle, which captures the quasi-digital behavior of the closed-current loop for pulsewidth modulated (PWM) dc-dc converters operating in continuous-conduction mode (CCM) using peak current-mode (PCM) control, the current-loop gain, the transfer function from control voltage to duty cycle (closed-current loop transfer function), and presents experimental verification. The sample-and-hold effect, or quasi-digital (discrete) behavior in the current loop with constant-frequency PCM in PWM dc-dc converters is described in a manner consistent with the physical behavior of the circuit. Using control theory, a transfer function from the error voltage to the duty cycle that captures the quasi-digital behavior is derived. This transfer function has a pole that can be in either the left-half plane or right-half plane, and captures the sample-and-hold effect accurately, enabling the characterization of the current-loop gain and closed-current loop for PWM dc-dc converters with PCM. The theoretical and experimental response results were in excellent agreement, confirming the validity of the transfer functions derived. The closed-current loop characterization can be used for the design of a controller for the outer voltage loop.