nils-herrmann/clean_scirepeval huggingface dataset:
The Corpus DIMEx100: transcription and evaluation

In this paper the transcription and evaluation of the corpus DIMEx100 for Mexican Spanish is presented. First we describe the corpus and explain the linguistic and computational motivation for its design and collection process; then, the phonetic antecedents and the alphabet adopted for the transcription task are presented; the corpus has been transcribed at three different granularity levels, which are also specified in detail. The corpus statistics for each transcription level are also presented. A set of phonetic rules describing phonetic context observed empirically in spontaneous conversation is also validated with the transcription. The corpus has been used for the construction of acoustic models and a phonetic dictionary for the construction of a speech recognition system. Initial performance results suggest that the data can be used to train good quality acoustic models.
Rethinking Sentiment Analysis in the News : from Theory to Practice and back

Most sentiment analysis work has been carried out on highly subjective text types where the target is clearly defined and unique across the text (movie or product reviews). However, when applying sentiment analysis to the news domain, it is necessary to clearly define the scope of the task, in a more specific manner than it has been done in the field so far. The main tasks we identified for news opinion mining are: definition of the target; separation of the good and bad news content from the good and bad sentiment expressed on the target; and analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge. It is furthermore important to distinguish three different possible views on newspaper articles – author, reader and text. These have to be addressed differently at the time of analysing sentiment, especially the case of author intention and reader interpretation, where specific profiles must be defined if the proper sentiment is to be extracted.
Robust and flexible power-proportional storage

Power-proportional cluster-based storage is an important component of an overall cloud computing infrastructure. With it, substantial subsets of nodes in the storage cluster can be turned off to save power during periods of low utilization. Rabbit is a distributed file system that arranges its data-layout to provide ideal power-proportionality down to very low minimum number of powered-up nodes (enough to store a primary replica of available datasets). Rabbit addresses the node failure rates of large-scale clusters with data layouts that minimize the number of nodes that must be powered-up if a primary fails. Rabbit also allows different datasets to use different subsets of nodes as a building block for interference avoidance when the infrastructure is shared by multiple tenants. Experiments with a Rabbit prototype demonstrate its power-proportionality, and simulation experiments demonstrate its properties at scale.
Sequential fraud detection for prepaid cards using hidden Markov model divergence

Stored-value cards, or prepaid cards, are increasingly popular. Like credit cards, their use is vulnerable to fraud, costing merchants and card processors millions of dollars. Prior techniques to automate fraud detection rely on a priori rules or specialized learned models associated with the customer. Mostly, these techniques do not consider fraud sequences or changing behavior, which can lead to false alarms. This study demonstrates how a transaction model can be dynamically created and updated, and fraud can be automatically detected for prepaid cards. A card processing company creates models of the store terminals rather than the customers, in part, because of the anonymous nature of prepaid cards. The technique automatically creates, updates, and compares hidden Markov models (HMM) of merchant terminals. We present fraud detection and experiments on real transactional data, showing the efficiency and effectiveness of the approach. In the fraud test cases, derived from known fraud cases, the technique has a good F-score. The technique can detect fraud in real-time for merchants, as card transactions are processed by a modern transaction processing system.
Which extractant should be used for the screening and isolation of antimicrobial components from plants?

Freeze dried and finely ground leaves of two plants with known antimicrobial activity, Anthocleista grandiflora and Combretum erythrophyllum were extracted with acetone, ethanol, methanol, methylenedichloride, methanol/chloroform/water and water at a 1 to 10 ratio in each case. The quantity and diversity of compounds extracted, number of inhibitors extracted, rate of extraction, toxicity in a bioassay, ease of removal of solvent and biological hazard were evaluated for each extractant. An arbitrary scoring system was developed to evaluate the above parameters for the different extractants. Acetone gave the best results with these plants with an arbitrary value of 102 followed by methanol/chloroform/water (81), methylene dichloride (79), methanol (71), ethanol (58) and water (47). Four five minute sequential extractions of very finely ground A. grandiflora shaking at a high rate extracted 97% of the total antimicrobial activity.
Two-Frame Motion Estimation Based on Polynomial Expansion

This paper presents a novel two-frame motion estimation algorithm. The first step is to approximate each neighborhood of both frames by quadratic polynomials, which can be done efficiently using the polynomial expansion transform. From observing how an exact polynomial transforms under translation a method to estimate displacement fields from the polynomial expansion coefficients is derived and after a series of refinements leads to a robust algorithm. Evaluation on the Yosemite sequence shows good results.
Audio-based Music Classification with a Pretrained Convolutional Network

Recently the ‘Million Song Dataset’, containing audio features and metadata for one million songs, was made available. In this paper, we build a convolutional network that is then trained to perform artist recognition, genre recognition and key detection. The network is tailored to summarize the audio features over musically significant timescales. It is infeasible to train the network on all available data in a supervised fashion, so we use unsupervised pretraining to be able to harness the entire dataset: we train a convolutional deep belief network on all data, and then use the learnt parameters to initialize a convolutional multilayer perceptron with the same architecture. The MLP is then trained on a labeled subset of the data for each task. We also train the same MLP with randomly initialized weights. We find that our convolutional approach improves accuracy for the genre recognition and artist recognition tasks. Unsupervised pretraining improves convergence speed in all cases. For artist recognition it improves accuracy as well.
Application of Morphological Bit Planes in Retinal Blood Vessel Extraction

The appearance of the retinal blood vessels is an important diagnostic indicator of various clinical disorders of the eye and the body. Retinal blood vessels have been shown to provide evidence in terms of change in diameter, branching angles, or tortuosity, as a result of ophthalmic disease. This paper reports the development for an automated method for segmentation of blood vessels in retinal images. A unique combination of methods for retinal blood vessel skeleton detection and multidirectional morphological bit plane slicing is presented to extract the blood vessels from the color retinal images. The skeleton of main vessels is extracted by the application of directional differential operators and then evaluation of combination of derivative signs and average derivative values. Mathematical morphology has been materialized as a proficient technique for quantifying the retinal vasculature in ocular fundus images. A multidirectional top-hat operator with rotating structuring elements is used to emphasize the vessels in a particular direction, and information is extracted using bit plane slicing. An iterative region growing method is applied to integrate the main skeleton and the images resulting from bit plane slicing of vessel direction-dependent morphological filters. The approach is tested on two publicly available databases DRIVE and STARE. Average accuracy achieved by the proposed method is 0.9423 for both the databases with significant values of sensitivity and specificity also; the algorithm outperforms the second human observer in terms of precision of segmented vessel tree.
Behavioral detection of malware on mobile handsets

A novel behavioral detection framework is proposed to detect mobile worms, viruses and Trojans, instead of the signature-based solutions currently available for use in mobile devices. First, we propose an efficient representation of malware behaviors based on a key observation that the logical ordering of an application's actions over time often reveals the malicious intent even when each action alone may appear harmless. Then, we generate a database of malicious behavior signatures by studying more than 25 distinct families of mobile viruses and worms targeting the Symbian OS - the most widely-deployed handset OS - and their variants. Next, we propose a two-stage mapping technique that constructs these signatures at run-time from the monitored system events and API calls in Symbian OS. We discriminate the malicious behavior of malware from the normal behavior of applications by training a classifier based on Support Vector Machines (SVMs). Our evaluation on both simulated and real-world malware samples indicates that behavioral detection can identify current mobile viruses and worms with more than 96% accuracy. We also find that the time and resource overheads of constructing the behavior signatures from low-level API calls are acceptably low for their deployment in mobile devices.
Scalable stacking and learning for building deep architectures

Deep Neural Networks (DNNs) have shown remarkable success in pattern recognition tasks. However, parallelizing DNN training across computers has been difficult. We present the Deep Stacking Network (DSN), which overcomes the problem of parallelizing learning algorithms for deep architectures. The DSN provides a method of stacking simple processing modules in buiding deep architectures, with a convex learning problem in each module. Additional fine tuning further improves the DSN, while introducing minor non-convexity. Full learning in the DSN is batch-mode, making it amenable to parallel training over many machines and thus be scalable over the potentially huge size of the training data. Experimental results on both the MNIST (image) and TIMIT (speech) classification tasks demonstrate that the DSN learning algorithm developed in this work is not only parallelizable in implementation but it also attains higher classification accuracy than the DNN.
An Empirical Test of a Clinical Metacognitive Model of Rumination and Depression

Rumination has attracted increasing theoretical and empirical interest in the past 15 years. Previous research has demonstrated significant relationships between rumination, depression, and metacognition. Two studies were conducted to further investigate these relationships and test the fit of a clinical metacognitive model of rumination and depression in samples of both depressed and nondepressed participants. In these studies, we collected cross-sectional data of rumination, depression, and metacognition. The relationships among variables were examined by testing the fit of structural equation models. In the study on depressed participants, a good model fit was obtained consistent with predictions. There were similarities and differences between the depressed and nondepressed samples in terms of relationships among metacognition, rumination, and depression. In each case, theoretically consistent paths between positive metacognitive beliefs, rumination, negative metacognitive beliefs, and depression were evident. The conceptual and clinical implications of these data are discussed.
Automated surgical skill assessment in RMIS training

Manual feedback in basic robot-assisted minimally invasive surgery (RMIS) training can consume a significant amount of time from expert surgeons’ schedule and is prone to subjectivity. In this paper, we explore the usage of different holistic features for automated skill assessment using only robot kinematic data and propose a weighted feature fusion technique for improving score prediction performance. Moreover, we also propose a method for generating ‘task highlights’ which can give surgeons a more directed feedback regarding which segments had the most effect on the final skill score. We perform our experiments on the publicly available JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS) and evaluate four different types of holistic features from robot kinematic data—sequential motion texture (SMT), discrete Fourier transform (DFT), discrete cosine transform (DCT) and approximate entropy (ApEn). The features are then used for skill classification and exact skill score prediction. Along with using these features individually, we also evaluate the performance using our proposed weighted combination technique. The task highlights are produced using DCT features. Our results demonstrate that these holistic features outperform all previous Hidden Markov Model (HMM)-based state-of-the-art methods for skill classification on the JIGSAWS dataset. Also, our proposed feature fusion strategy significantly improves performance for skill score predictions achieving up to 0.61 average spearman correlation coefficient. Moreover, we provide an analysis on how the proposed task highlights can relate to different surgical gestures within a task. Holistic features capturing global information from robot kinematic data can successfully be used for evaluating surgeon skill in basic surgical tasks on the da Vinci robot. Using the framework presented can potentially allow for real-time score feedback in RMIS training and help surgical trainees have more focused training.
FPGA-based real-time optical-flow system

We describe a pipelined optical-flow processing system that works as a virtual motion sensor. It is based on a field-programmable gate array (FPGA) device enabling the easy change of configuring parameters to adapt the sensor to different speeds, light conditions and other environmental factors. We refer to it as a "virtual sensor" because it consists of a conventional camera as front-end supported by an FPGA processing device, which embeds the frame grabber, optical-flow algorithm implementation, output module, and some configuration and storage circuitry. To the best of our knowledge, this is the first study that presents a fully stand-alone optical-flow processing system to include measurements of the platform performance in terms of accuracy and speed.
Clustering Big Spatiotemporal-Interval Data

We propose a model for clustering data with spatiotemporal intervals. This model is used to effectively evaluate clusters of spatiotemporal interval data. A new energy function is used to measure similarity and balance between clusters in spatial and temporal dimensions. We employ as a case study a large collection of parking data from a real CBD area. The proposed model is applied to existing traditional algorithms to address spatiotemporal interval data clustering problem. Results from traditional clustering algorithms are compared and analysed using the proposed energy function.
Real-Time Wireless Sensor Network for Landslide Detection

Wireless sensor networks are one of the emerging areas which have equipped scientists with the capability of developing real-time monitoring systems. This paper discusses the development of a wireless sensor network(WSN) to detect landslides, which includes the design, development and implementation of a WSN for real time monitoring, the development of the algorithms needed that will enable efficient data collection and data aggregation, and the network requirements of the deployed landslide detection system. The actual deployment of the testbed is in the Idukki district of the Southern state of Kerala, India, a region known for its heavy rainfall, steep slopes, and frequent landslides.
Survey on Blockchain Technologies and Related Services FY2015 Report

1 Background and Objective of the Survey Compared with conventional centralized systems, blockchain technologies used for transactions of value records, such as bitcoins, structurally have the characteristics that (i) enable the creation of a system that substantially ensures no downtime (ii) make falsification extremely hard, and (iii) realize inexpensive system. Blockchain technologies are expected to be utilized in diverse fields including IoT. Japanese companies just started technology verification independently, and there is a risk that the initiative might be taken by foreign companies in blockchain technologies, which are highly likely to serve as the next-generation platform for all industrial fields in the future. From such point of view, this survey was conducted for the purpose of  comparing and analyzing details of numbers of blockchains and advantages/challenges therein;  ascertaining promising fields in which the technology should be utilized;  ascertaining the impact of the technology on society and the economy; and  developing policy guidelines for encouraging industries to utilize the technology in the future. This report compiles the results of interviews with domestic and overseas companies involving blockchain technology and experts. The content of this report is mostly based on data as of the end of February 2016. As specifications of blockchains and the status of services being provided change by the minute, it is recommended to check the latest conditions when intending to utilize any related technologies in business, etc. Terms and abbreviations used in this report are defined as follows. Terms Explanations BTC Abbreviation used as a currency unit of bitcoins FinTech A coined term combining Finance and Technology; Technologies and initiatives to create new services and businesses by utilizing ICT in the financial business Virtual currency / Cryptocurrency Bitcoins or other information whose value is recognized only on the Internet Exchange Services to exchange virtual currency, such as bitcoins, with another virtual currency or with legal currency, such as Japanese yen or US dollars; Some exchange offers services for contracts for difference, such as foreign exchange margin transactions (FX transactions) Consensus A series of procedures from approving a transaction as an official one and mutually confirming said results by using the following consensus algorithm Consensus algorithm Algorithm in general for mutually approving a distributed ledger using Proof of Work and Proof of Stake, etc. Token Virtual currency unique to blockchains; Virtual currency used for paying fees for asset management, etc. on blockchains is referred to …
Wideband Ceiling-Mount Omnidirectional Antenna for Indoor Distributed Antenna Systems

A wideband omnidirectional antenna is presented in this letter. The proposed antenna consists of three radiators over a ground plane, a monopole composed of three patches fed by a coaxial line, a coupling patch with three shorting legs above the monopole to extend the lowest frequencies of operation, and a top-loading disk located on the top of the coupling patch for further decreasing the lowest frequencies of operation. An enhanced impedance bandwidth of 9.23:1 ranging from 650 MHz to 6 GHz with 13.9 dB (for SWR ≤ 1.5) is achieved. The antenna meets network operator's requirements for cellular bands and has good radiation efficiency in the WiFi bands. The proposed antenna can be widely used in indoor distributed antenna systems, such as 3G, 4G LTE, and WiFi (2.4 and 5.8 GHz) bands. The antenna is simulated by ANSYS HFSS and measured in a microwave anechoic chamber. The simulated and measured results show good agreement.
Human activity recognition with smartphone sensors using deep learning neural networks

Human activities are inherently translation invariant and hierarchical. Human activity recognition (HAR), a field that has garnered a lot of attention in recent years due to its high demand in various application domains, makes use of time-series sensor data to infer activities. In this paper, a deep convolutional neural network (convnet) is proposed to perform efficient and effective HAR using smartphone sensors by exploiting the inherent characteristics of activities and 1D time-series signals, at the same time providing a way to automatically and data-adaptively extract robust features from raw data. Experiments show that convnets indeed derive relevant and more complex features with every additional layer, although difference of feature complexity level decreases with every additional layer. A wider time span of temporal local correlation can be exploited (1x9~1x14) and a low pooling size (1x2~1x3) is shown to be beneficial. Convnets also achieved an almost perfect classification on moving activities, especially very similar ones which were previously perceived to be very difficult to classify. Lastly, convnets outperform other state-of-the-art data mining techniques in HAR for the benchmark dataset collected from 30 volunteer subjects, achieving an overall performance of 94.79% on the test set with raw sensor data, and 95.75% with additional information of temporal fast Fourier transform of the HAR data set.
AUTOMATIC CAMERA TRACKING

Camera tracking for uncalibrated image sequences has now reached a level of maturity where 3D point structure and cameras can be recovered automatically for a significant class of scene types and camera motions. However, problems still occur, and their solution requires a combination of theoretical analysis and good engineering. We describe several such problems including missing data, degeneracy and deviations from the pinhole camera model, and discuss their solutions. We also discuss the incorporation of prior knowledge and the case of multiple rigid motions.
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, IMAGENET-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Then we propose a new dataset called IMAGENET-P which enables researchers to benchmark a classifier’s robustness to common perturbations. Unlike recent robustness research, this benchmark evaluates performance on common corruptions and perturbations not worst-case adversarial perturbations. We find that there are negligible changes in relative corruption robustness from AlexNet classifiers to ResNet classifiers. Afterward we discover ways to enhance corruption and perturbation robustness. We even find that a bypassed adversarial defense provides substantial common perturbation robustness. Together our benchmarks may aid future work toward networks that robustly generalize.
Fast and robust super-resolution

In the last two decades, many papers have been published, proposing a variety methods of multi-frame resolution enhancement. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses their shortcomings. We propose a different implementation usingL1 norm minimization and robust regularization to deal with different data and noise models. This computationally inexpensive method is robust to errors in motion and blur estimation, and results in sharp edges. Simulation results confirm the effectiveness of our method and demonstrate its superiority to other robust super-resolution methods.