nils-herrmann/clean_scirepeval huggingface dataset:
A Gold Standard Dependency Corpus for English

We present a gold standard annotation of syntactic dependencies in the English Web Treebank corpus using the Stanford Dependencies standard. This resource addresses the lack of a gold standard dependency treebank for English, as well as the limited availability of gold standard syntactic annotations for informal genres of English text. We also present experiments on the use of this resource, both for training dependency parsers and for evaluating dependency parsers like the one included as part of the Stanford Parser. We show that training a dependency parser on a mix of newswire and web data improves performance on that type of data without greatly hurting performance on newswire text, and therefore gold standard annotations for non-canonical text can be valuable for parsing in general. Furthermore, the systematic annotation effort has informed both the SD formalism and its implementation in the Stanford Parser’s dependency converter. In response to the challenges encountered by annotators in the EWT corpus, we revised and extended the Stanford Dependencies standard, and improved the Stanford Parser’s dependency converter.
Using convolutional features and a sparse autoencoder for land-use scene classification

In this article, we propose a novel approach based on convolutional features and sparse autoencoder (AE) for scene-level landuse (LU) classification. This approach starts by generating an initial feature representation of the scenes under analysis from a deep convolutional neural network (CNN) pre-learned on a large amount of labelled data from an auxiliary domain. Then these convolutional features are fed as input to a sparse AE for learning a new suitable representation in an unsupervised manner. After this pre-training phase, we propose two different scenarios for building the classification system. In the first scenario, we add a softmax layer on the top of the AE encoding layer and then finetune the resulting network in a supervised manner using the target training images available at hand. Then we classify the test images based on the posterior probabilities provided by the softmax layer. In the second scenario, we view the classification problem from a reconstruction perspective. To this end we train several class-specific AEs (i.e. one AE per class) and then classify the test images based on the reconstruction error. Experimental results conducted on the University of California (UC) Merced and Banja-Luka LU public data sets confirm the superiority of the proposed approach compared to state-of-the-art methods. ARTICLE HISTORY Received 20 November 2015
3D head tracking and pose-robust 2D texture map-based face recognition using a simple ellipsoid model

A human face provides a variety of different communicative functions such as identification, the perception of emotional expression, and lip-reading. For these reasons, many applications in robotics require tracking and recognizing a human face. A novel face recognition system should be able to deal with various changes in face images, such as pose, illumination, and expression, among which pose variation is the most difficult one to deal with. Therefore, face registration (alignment) is the key of robust face recognition. If we can register face images into frontal views, the recognition task would be much easier. To align a face image into a canonical frontal view, we need to know the pose information of a human head. Therefore, in this paper, we propose a novel method for modeling a human head as a simple 3D ellipsoid. And also, we present 3D head tracking and pose estimation methods using the proposed ellipsoidal model. After recovering full motion of the head, we can register face images with pose variations into stabilized view images which are suitable for frontal face recognition. By doing so, simple and efficient frontal face recognition can be easily carried out in the stabilized texture map space instead of the original input image space. To evaluate the feasibility of the proposed approach using a simple ellipsoid model, 3D head tracking experiments are carried out on 45 image sequences with ground truth from Boston University, and several face recognition experiments are conducted on our laboratory database and the Yale Face Database B by using subspace-based face recognition methods such as PCA, PCA+LAD, and DCV.
MobSafe: cloud computing based forensic analysis for massive mobile applications using data mining

With the explosive increase in mobile apps, more and more threats migrate from traditional PC client to mobile device. Compared with traditional Win+Intel alliance in PC, Android+ARM alliance dominates in Mobile Internet, the apps replace the PC client software as the major target of malicious usage. In this paper, to improve the security status of current mobile apps, we propose a methodology to evaluate mobile apps based on cloud computing platform and data mining. We also present a prototype system named MobSafe to identify the mobile app’s virulence or benignancy. Compared with traditional method, such as permission pattern based method, MobSafe combines the dynamic and static analysis methods to comprehensively evaluate an Android app. In the implementation, we adopt Android Security Evaluation Framework (ASEF) and Static Android Analysis Framework (SAAF), the two representative dynamic and static analysis methods, to evaluate the Android apps and estimate the total time needed to evaluate all the apps stored in one mobile app market. Based on the real trace from a commercial mobile app market called AppChina, we can collect the statistics of the number of active Android apps, the average number apps installed in one Android device, and the expanding ratio of mobile apps. As mobile app market serves as the main line of defence against mobile malwares, our evaluation results show that it is practical to use cloud computing platform and data mining to verify all stored apps routinely to filter out malware apps from mobile app markets. As the future work, MobSafe can extensively use machine learning to conduct automotive forensic analysis of mobile apps based on the generated multifaceted data in this stage.
Propagating Trust and Distrust to Demote Web Spam

Web spamming describes behavior that attempts to deceive search engine’s ranking algorithms. TrustRank is a recent algorithm that can combat web spam by propagating trust among web pages. However, TrustRank propagates trust among web pages based on the number of outgoing links, which is also how PageRank propagates authority scores among Web pages. This type of propagation may be suited for propagating authority, but it is not optimal for calculating trust scores for demoting spam sites. In this paper, we propose several alternative methods to propagate trust on the web. With experiments on a real web data set, we show that these methods can greatly decrease the number of web spam sites within the top portion of the trust ranking. In addition, we investigate the possibility of propagating distrust among web pages. Experiments show that combining trust and distrust values can demote more spam sites than the sole use of trust values.
Bolt: Accelerated Data Mining with Fast Vector Compression

Vectors of data are at the heart of machine learning and data mining. Recently, vector quantization methods have shown great promise in reducing both the time and space costs of operating on vectors. We introduce a vector quantization algorithm that can compress vectors over 12x faster than existing techniques while also accelerating approximate vector operations such as distance and dot product computations by up to 10x. Because it can encode over 2GB of vectors per second, it makes vector quantization cheap enough to employ in many more circumstances. For example, using our technique to compute approximate dot products in a nested loop can multiply matrices faster than a state-of-the-art BLAS implementation, even when our algorithm must first compress the matrices. In addition to showing the above speedups, we demonstrate that our approach can accelerate nearest neighbor search and maximum inner product search by over 100x compared to floating point operations and 10x compared to other vector quantization methods. Our approximate Euclidean distance and dot product computations are not only faster than those of related algorithms with slower encodings, but also faster than Hamming distance computations, which have direct hardware support on the tested platforms. We also assess the errors of our algorithm's approximate distances and dot products, and find that it is competitive with existing, slower vector quantization algorithms.
Mathematical modelling and speed control of a sensored brushless DC motor using intelligent controller

Brushless DC motor are rapidly gaining popularity due to its high efficiency and accurate control characteristics. For any industrial and domestic application it is necessary to control the speed accurately. The drawbacks of the conventional PID controllers for the speed control can be minimized using Fuzzy PID controller. This paper aims at the design of Fuzzy PID controller for a BLDC motor inorder to dynamically update the parameters of PID controller. The mathematical modelling of the motor along with the controller is derived for the specific BLDC motor. The simulation is done on MATLAB/SIMULINK environment to verify the control characteristics of the Fuzzy PID controller.
Imbalanced sentiment classification

Sentiment classification has undergone significant development in recent years. However, most existing studies assume the balance between negative and positive samples, which may not be true in reality. In this paper, we investigate imbalanced sentiment classification instead. In particular, a novel clustering-based stratified under-sampling framework and a centroid-directed smoothing strategy are proposed to address the imbalanced class and feature distribution problems respectively. Evaluation across different datasets shows the effectiveness of both the under-sampling framework and the smoothing strategy in handling the imbalanced problems in real sentiment classification applications.
A four-channel bi-directional CMOS core chip for X-band phased array T/R modules

This paper presents a four-channel bi-directional core chip in 0.13 um CMOS for X-band phased array Transmit/Receive (T/R) module. Each channel consists of a 5-bit step attenuator, a 6-bit phase shifter, bi-directional gain blocks (BDGB), and a bi-directional amplifier (BDA). Additional circuits such as low drop out (LDO) regulator, bias circuits with band-gap reference (BGR), and serial to parallel interface (SPI) are integrated for stable biasing and ease of interface. The chip size is 6.9 × 1.6 mm2 including pads which corresponds to 2.8 mm2 per channel. The phase and attenuation coverage is 360° with the LSB of 5.625°, and 31dB with the LSB of 1dB, respectively. The RMS phase error is better than 2.3°, and the RMS attenuation error is better than 0.25 dB at 9-10 GHz. The Tx mode reference-state gain in each channel is 11.3-12.2 dB including the 4-way power combiner insertion losses ideally 6 dB, and the Rx mode gain is 8.6-9.5 dB at 9-10 GHz. The output P1dB in Tx mode is > 11 dBm at 9-10 GHz. To the best of authors' knowledge, this is the smallest size per channel X-band core chip in CMOS technology with bi-directional operation and competitive RF performance to-date.
AI2-THOR: An Interactive 3D Environment for Visual AI

We introduce The House Of inteRactions (THOR), a framework for visual AI research, available at http:// ai2thor.allenai.org. AI2-THOR consists of near photo-realistic 3D indoor scenes, where AI agents can navigate in the scenes and interact with objects to perform tasks. AI2-THOR enables research in many different domains including but not limited to deep reinforcement learning, imitation learning, learning by interaction, planning, visual question answering, unsupervised representation learning, object detection and segmentation, and learning models of cognition. The goal of AI2-THOR is to facilitate building visually intelligent models and push the research forward in this domain.
Public and Private Sector IT Governance: Identifying Contextual Differences

This paper highlights systemic contextual differences and the unique IT Governance issues that might arise in public and private sector organizations. Public sector organizations constitute a significant component of economic activity in most countries. Like their private sector counterparts, many public sector agencies are struggling to cope with reduced or inadequate IT budgets and are continuously looking for ways to extract maximum value from IT resources. While both sectors face similar managerial-level IT issues and challenges, we argue that there are systemic differences between private and public sector organizations suggesting that a one size fits all approach to IT Governance may not apply.
Machine Learning in Prediction of Stock Market Indicators Based on Historical Data and Data from Twitter Sentiment Analysis

Development of linguistic technologies and penetration of social media provide powerful possibilities to investigate users' moods and psychological states of people. In this paper we discussed possibility to improve accuracy of stock market indicators predictions by using data about psychological states of Twitter users. For analysis of psychological states we used lexicon-based approach, which allow us to evaluate presence of eight basic emotions in more than 755 million tweets. The application of Support Vectors Machine and Neural Networks algorithms to predict DJIA and S&P500 indicators are discussed.
Intellectual capital : an exploratory study that develops measures and models

This paper details an empirical pilot study that explores the development of several conceptual measures and models regarding intellectual capital and its impact on business performance. The objective of this pilot study is to explore the development of items and constructs through principal components analysis and partial least squares (PLS). The final retained, subjective measures and optimal structural specification show a valid, reliable, significant and substantive causal link between dimensions of intellectual capital and business performance. These results should help both academics and practitioners more readily understand the components of intellectual capital and provide insight into developing and increasing it within an organization. Suggestions are then made to advance and improve this research programme. All men by nature desire knowledge. Aristotle (384-322 BC), Greek philosopher.  Book 1, Chapter 1.
3D brain tumor segmentation in multimodal MR images based on learning population- and patient-specific feature sets

Brain tumor segmentation is a clinical requirement for brain tumor diagnosis and radiotherapy planning. Automating this process is a challenging task due to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this paper, we propose a method to construct a graph by learning the population- and patient-specific feature sets of multimodal magnetic resonance (MR) images and by utilizing the graph-cut to achieve a final segmentation. The probabilities of each pixel that belongs to the foreground (tumor) and the background are estimated by global and custom classifiers that are trained through learning population- and patient-specific feature sets, respectively. The proposed method is evaluated using 23 glioma image sequences, and the segmentation results are compared with other approaches. The encouraging evaluation results obtained, i.e., DSC (84.5%), Jaccard (74.1%), sensitivity (87.2%), and specificity (83.1%), show that the proposed method can effectively make use of both population- and patient-specific information.
High performance air-filled substrate integrated waveguide filter post-process tuning using capacitive post

This paper presents an air-filled substrate integrated waveguide (AFSIW) filter post-process tuning technique. The emerging high-performance AFSIW technology is of high interest for the design of microwave and millimeter-wave substrate integrated systems based on low-cost multilayer printed circuit board (PCB) process. However, to comply with stringent specifications, especially for space, aeronautical and safety applications, a filter post-process tuning technique is desired. AFSIW single pole filter post-process tuning using a capacitive post is theoretically analyzed. It is demonstrated that a tuning of more than 3% of the resonant frequency is achieved at 21 GHz using a 0.3 mm radius post with a 40% insertion ratio. For experimental demonstration, a fourth-order AFSIW band pass filter operating in the 20.88 to 21.11 GHz band is designed and fabricated. Due to fabrication tolerances, it is shown that its performances are not in line with expected results. Using capacitive post tuning, characteristics are improved and agree with optimized results. This post-process tuning can be used for other types of substrate integrated devices.
Tapping on the potential of q&a community by recommending answer providers

The rapidly increasing popularity of community-based Question Answering (cQA) services, e.g. Yahoo! Answers, Baidu Zhidao, etc. have attracted great attention from both academia and industry. Besides the basic problems, like question searching and answer finding, it should be noted that the low participation rate of users in cQA service is the crucial problem which limits its development potential. In this paper, we focus on addressing this problem by recommending answer providers, in which a question is given as a query and a ranked list of users is returned according to the likelihood of answering the question. Based on the intuitive idea for recommendation, we try to introduce topic-level model to improve heuristic term-level methods, which are treated as the baselines. The proposed approach consists of two steps: (1) discovering latent topics in the content of questions and answers as well as latent interests of users to build user profiles; (2) recommending question answerers for new arrival questions based on latent topics and term-level model. Specifically, we develop a general generative model for questions and answers in cQA, which is then altered to obtain a novel computationally tractable Bayesian network model. Experiments are carried out on a real-world data crawled from Yahoo! Answers during Jun 12 2007 to Aug 04 2007, which consists of 118510 questions, 772962 answers and 150324 users. The experimental results reveal significant improvements over the baseline methods and validate the positive influence of topic-level information.
Text normalization in mandarin text-to-speech system

Text normalization is an important component in text-to-speech system and the difficulty in text normalization is to disambiguate the non-standard words (NSWs). This paper develops a taxonomy of NSWs on the basis of a large scale Chinese corpus, and proposes a two-stage NSWs disambiguation strategy, finite state automata (FSA) for initial classification and maximum entropy (ME) classifiers for subclass disambiguation. Based on the above NSWs taxonomy, the two-stage approach achieves an F-score of 98.53% in open test, 5.23% higher than that of FSA based approach. Experiments show that the NSWs taxonomy ensures FSA a high baseline performance and ME classifiers make considerable improvement, and the two-stage approach adapts well to new domains.
A defense-centric taxonomy based on attack manifestations

Many classifications of attacks have been tendered, often in taxonomic form, A common basis of these taxonomies is that they have been framed from the perspective of an attacker - they organize attacks with respect to the attacker's goals, such as privilege elevation from user to root (from the well known Lincoln taxonomy). Taxonomies based on attacker goals are attack-centric; those based on defender goals are defense-centric. Defenders need a way of determining whether or not their detectors will detect a given attack. It is suggested that a defense-centric taxonomy would suit this role more effectively than an attack-centric taxonomy. This paper presents a new, defense-centric attack taxonomy, based on the way that attacks manifest as anomalies in monitored sensor data. Unique manifestations, drawn from 25 attacks, were used to organize the taxonomy, which was validated through exposure to an intrusion-detection system, confirming attack detect ability. The taxonomy's predictive utility was compared against that of a well-known extant attack-centric taxonomy. The defense-centric taxonomy is shown to be a more effective predictor of a detector's ability to detect specific attacks, hence informing a defender that a given detector is competent against an entire class of attacks.
Deep Learning of Audio and Language Features for Humor Prediction

We propose a comparison between various supervised machine learning methods to predict and detect humor in dialogues. We retrieve our humorous dialogues from a very popular TV sitcom: “The Big Bang Theory”. We build a corpus where punchlines are annotated using the canned laughter embedded in the audio track. Our comparative study involves a linear-chain Conditional Random Field over a Recurrent Neural Network and a Convolutional Neural Network. Using a combination of word-level and audio frame-level features, the CNN outperforms the other methods, obtaining the best F-score of 68.5% over 66.5% by CRF and 52.9% by RNN. Our work is a starting point to developing more effective machine learning and neural network models on the humor prediction task, as well as developing machines capable in understanding humor in general.
Steady-state somatosensory evoked potentials: suitable brain signals for brain-computer interfaces?

One of the main issues in designing a brain-computer interface (BCI) is to find brain patterns, which could easily be detected. One of these pattern is the steady-state evoked potential (SSEP). SSEPs induced through the visual sense have already been used for brain-computer communication. In this work, a BCI system is introduced based on steady-state somatosensory evoked potentials (SSSEPs). Transducers have been used for the stimulation of both index fingers using tactile stimulation in the "resonance"-like frequency range of the somatosensory system. Four subjects participated in the experiments and were trained to modulate induced SSSEPs. Two of them learned to modify the patterns in order to set up a BCI with an accuracy of between 70% and 80%. Results presented in this work give evidence that it is possible to set up a BCI which is based on SSSEPs.