nils-herrmann/clean_scirepeval huggingface dataset:
Active and Semi-Supervised Learning in ASR: Benefits on the Acoustic and Language Models

The goal of this paper is to simulate the benefits of jointly applying active learning (AL) and semi-supervised training (SST) in a new speech recognition application. Our data selection approach relies on confidence filtering, and its impact on both the acoustic and language models (AM and LM) is studied. While AL is known to be beneficial to AM training, we show that it also carries out substantial improvements to the LM when combined with SST. Sophisticated confidence models, on the other hand, did not prove to yield any data selection gain. Our results indicate that, while SST is crucial at the beginning of the labeling process, its gains degrade rapidly as AL is set in place. The final simulation reports that AL allows a transcription cost reduction of about 70% over random selection. Alternatively, for a fixed transcription budget, the proposed approach improves the word error rate by about 12.5% relative.
Position Measurement in Industrial Drives by Means of Low-Cost Resolver-to-Digital Converter

A fully integrated board for resolver-to-digital conversion is proposed in this paper. It is based on a combined analog/digital circuit, which allows one to track the shaft angle of a standard resolver in a digital form. Furthermore, the board also provides the feeding signal for the resolver-rotor circuit. All the tuning and configuration settings, which allow one to adapt different kind of resolvers to the proposed system, can be easily implemented directly on the board. The final assembly is a compact low-cost resolver-to-digital converter. The entire board design and implementation are described in this paper. In addition, several tests at different resolver speeds have been performed for validation.
Appropriating and assessing heuristics for mobile computing

Mobile computing presents formidable challenges not only to the design of applications but also to each and every phase of the systems lifecycle. In particular, the HCI community is still struggling with the challenges that mobile computing poses to evaluation. Expert-based evaluation techniques are well known and they do enable a relatively quick and easy evaluation. Heuristic evaluation, in particular, has been widely applied and investigated, most likely due to its efficiency in detecting most of usability flaws at front of a rather limited investment of time and human resources in the evaluation. However, the capacity of expert-based techniques to capture contextual factors in mobile computing is a major concern. In this paper, we report an effort for realizing usability heuristics appropriate for mobile computing. The effort intends to capture contextual requirements while still drawing from the inexpensive and flexible nature of heuristic-based techniques. This work has been carried out in the context of a research project task geared toward developing a heuristic-based evaluation methodology for mobile computing. This paper describes the methodology that we adopted toward realizing mobile heuristics. It also reports a study that we carried out in order to assess the relevance of the realized mobile heuristics by comparing their performance with that of the standard/traditional usability heuristics. The study yielded positive results in terms of the number of usability flaws identified and the severity ranking assigned.
Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection

Network intrusion detection systems (NIDSs) provide a better solution to network security than other traditional network defense technologies, such as firewall systems. The success of NIDS is highly dependent on the performance of the algorithms and improvement methods used to increase the classification accuracy and decrease the training and testing times of the algorithms. We propose an effective deep learning approach, self-taught learning (STL)-IDS, based on the STL framework. The proposed approach is used for feature learning and dimensionality reduction. It reduces training and testing time considerably and effectively improves the prediction accuracy of support vector machines (SVM) with regard to attacks. The proposed model is built using the sparse autoencoder mechanism, which is an effective learning algorithm for reconstructing a new feature representation in an unsupervised manner. After the pre-training stage, the new features are fed into the SVM algorithm to improve its detection capability for intrusion and classification accuracy. Moreover, the efficiency of the approach in binary and multiclass classification is studied and compared with that of shallow classification methods, such as J48, naive Bayesian, random forest, and SVM. Results show that our approach has accelerated SVM training and testing times and performed better than most of the previous approaches in terms of performance metrics in binary and multiclass classification. The proposed STL-IDS approach improves network intrusion detection and provides a new research method for intrusion detection.
Policy Recognition in the Abstract Hidden Markov Model

In this paper, we present a method for recognising an agent’s behaviour in dynamic, noisy, uncertain domains, and across multiple levels of abstracti on. We term this problemon-line plan recognition under uncertainty and view it generally as probabilistic inference on the stochastic process representing the execution of the agent ’s plan. Our contributions in this paper are two fold. In terms of plan recognition, we propose a novel plan recognition framework based on the Abstract Hidden Markov Model (AHMM) as the p lan execution model. In terms of probabilistic inference, we introduce the AHMM, a novel type of stochastic processes, provide its dynamic Bayesian network (DBN) structu e and analyse the properties of this network model. We describe a novel application of the Ra o-Blackwellisation procedure to general DBN, which allows us to construct hybrid inferenc methods that take advantage of the tractable sub-structures present in the DBN. When appli ed to the AHMM, we are able to take advantage of the independence properties inherent to a model of action and plan execution and derive an online probabilistic plan recognition algori thm that scales well with the number of levels in the plan hierarchy. This illustrates that while stochastic models for plan execution can be complex, they exhibit special structures which, if ex ploited, can lead to efficient plan recognition algorithms.
The Synthetic Data Vault

The goal of this paper is to build a system that automatically creates synthetic data to enable data science endeavors. To achieve this, we present the Synthetic Data Vault (SDV), a system that builds generative models of relational databases. We are able to sample from the model and create synthetic data, hence the name SDV. When implementing the SDV, we also developed an algorithm that computes statistics at the intersection of related database tables. We then used a state-of-the-art multivariate modeling approach to model this data. The SDV iterates through all possible relations, ultimately creating a model for the entire database. Once this model is computed, the same relational information allows the SDV to synthesize data by sampling from any part of the database. After building the SDV, we used it to generate synthetic data for five different publicly available datasets. We then published these datasets, and asked data scientists to develop predictive models for them as part of a crowdsourced experiment. By analyzing the outcomes, we show that synthetic data can successfully replace original data for data science. Our analysis indicates that there is no significant difference in the work produced by data scientists who used synthetic data as opposed to real data. We conclude that the SDV is a viable solution for synthetic data generation.
Some from Here, Some from There: Cross-Project Code Reuse in GitHub

Code reuse has well-known benefits on code quality, coding efficiency, and maintenance. Open Source Software (OSS) programmers gladly share their own code and they happily reuse others'. Social programming platforms like GitHub have normalized code foraging via their common platforms, enabling code search and reuse across different projects. Removing project borders may facilitate more efficient code foraging, and consequently faster programming. But looking for code across projects takes longer and, once found, may be more challenging to tailor to one's needs. Learning how much code reuse goes on across projects, and identifying emerging patterns in past cross-project search behavior may help future foraging efforts. To understand cross-project code reuse, here we present an in-depth study of cloning in GitHub. Using Deckard, a clone finding tool, we identified copies of code fragments across projects, and investigate their prevalence and characteristics using statistical and network science approaches, and with multiple case studies. By triangulating findings from different methods, we find that cross-project cloning is prevalent in GitHub, ranging from cloning few lines of code to whole project repositories. Some of the projects serve as popular sources of clones, and others seem to contain more clones than their fair share. Moreover, we find that ecosystem cloning follows an onion model: most clones come from the same project, then from projects in the same application domain, and finally from projects in different domains. Our results show directions for new tools that can facilitate code foraging and sharing within GitHub.
Deep Deconvolutional Neural Network for Target Segmentation of Nasopharyngeal Cancer in Planning Computed Tomography Images

Radiotherapy is one of the main treatment methods for nasopharyngeal carcinoma (NPC). It requires exact delineation of the nasopharynx gross tumor volume (GTVnx), the metastatic lymph node gross tumor volume (GTVnd), the clinical target volume (CTV), and organs at risk in the planning computed tomography images. However, this task is time-consuming and operator dependent. In the present study, we developed an end-to-end deep deconvolutional neural network (DDNN) for segmentation of these targets.



The proposed DDNN is an end-to-end architecture enabling fast training and testing. It consists of two important components: an encoder network and a decoder network. The encoder network was used to extract the visual features of a medical image and the decoder network was used to recover the original resolution by deploying deconvolution. A total of 230 patients diagnosed with NPC stage I or stage II were included in this study. Data from 184 patients were chosen randomly as a training set to adjust the parameters of DDNN, and the remaining 46 patients were the test set to assess the performance of the model. The Dice similarity coefficient (DSC) was used to quantify the segmentation results of the GTVnx, GTVnd, and CTV. In addition, the performance of DDNN was compared with the VGG-16 model.



The proposed DDNN method outperformed the VGG-16 in all the segmentation. The mean DSC values of DDNN were 80.9% for GTVnx, 62.3% for the GTVnd, and 82.6% for CTV, whereas VGG-16 obtained 72.3, 33.7, and 73.7% for the DSC values, respectively.



DDNN can be used to segment the GTVnx and CTV accurately. The accuracy for the GTVnd segmentation was relatively low due to the considerable differences in its shape, volume, and location among patients. The accuracy is expected to increase with more training data and combination of MR images. In conclusion, DDNN has the potential to improve the consistency of contouring and streamline radiotherapy workflows, but careful human review and a considerable amount of editing will be required.
Visual Navigation with Actor-Critic Deep Reinforcement Learning

Visual navigation in complex environments is crucial for intelligent agents. In this paper, we propose an efficient deep reinforcement learning (DRL) method to tackle visual navigation tasks. We present the synchronous advantage actor-critic (A2C) with generalized advantage estimator (GAE) algorithm. The A2C enables agents to learn from multiple processes, which significantly reduces the training time. The GAE used to estimate the advantage function improves the policy gradient estimates. We focus on visual navigation tasks in ViZDoom, and train agents in two health gathering scenarios. The experimental results show this method successfully teaches our agents to navigate in these scenarios. The A2C with GAE agent reaches the highest score in the first task, and a competitive score in the second task. In addition, this agent has better average scores and lower variances in both tasks.
Automated text summarisation and evidence-based medicine: A survey of two domains

The practice of evidence-based medicine (EBM) urges medical practitioners to utilise the latest research evidence when making clinical decisions. Because of the massive and growing volume of published research on various medical topics, practitioners often find themselves overloaded with information. As such, natural language processing research has recently commenced exploring techniques for performing medical domain-specific automated text summarisation (ATS) techniques-- targeted towards the task of condensing large medical texts. However, the development of effective summarisation techniques for this task requires cross-domain knowledge. We present a survey of EBM, the domain-specific needs for EBM, automated summarisation techniques, and how they have been applied hitherto. We envision that this survey will serve as a first resource for the development of future operational text summarisation techniques for EBM.
Real-time procedural generation of 'pseudo infinite' cities

We present an approach to procedural generation of `pseudo infinite' virtual cities in real-time. The cities contain geometrically varied buildings that are generated as needed. The building generation parameters are created by a pseudo random number generator, seeded with an integer derived from the building's position. The varied building geometries are extruded from a set of floor plans. The floor plans for each building are created by combining randomly generated polygons in an iterative process. A display list caching and frustum filling approach manages the generation of buildings and the use of system resources. This approach has been implemented on commodity PC hardware, resulting in interactive frame rates.
A Literature Review on Financial Fraud

Financial fraud hasattracted the attentions of researchers because it concerns with many different market participants. The fraudulent firms provide misleading information in the financial reports in order to achieve unethical goals. Investors can suffer losses if they use such information to make decisions. Therefore, it is important to detect fraud in financial statements. This paper includes two parts: Part I talks about the motivations and influences of financial frauds. Part II talks about how to detect financial fraud in the financial statements.
A Low Profile Dual-Polarized Wideband Omnidirectional Antenna Based on AMC Reflector

A low-profile dual-polarized wideband omnidirectional antenna with artificial magnetic conductor (AMC) reflector is proposed. The proposed antenna is operated in the long term evolution band (1.7-2.7 GHz), and has a compact size of 200 mm × 200 mm × 30.6 mm (about 0.25λ height at 2.7 GHz). The antenna structure consists of a horizontally polarized circular loop antenna, a vertically polarized lowprofile monopole antenna, and an AMC reflector. By carefully designing the reflection characteristics of the AMC reflector, the profile height of the proposed antenna is significantly reduced as compared with those of antennas backed by conventional perfect electric conductor (PEC) ground planes. Simulated and measured results show that the proposed antenna is able to achieve over 45% impedance bandwidth (VSWR <; 1.8) with stable radiation patterns in the band of 1.7-2.7 GHz. Owing to the attractive wide bandwidth, low-profile configuration, and ease of fabrication, the proposed antenna is suitable for microbase station systems, especially for indoor ceiling antenna networking applications.
Application of Fuzzy Graph Coloring in Traffic Light Problem

Let G= (V,E,σ,μ) be a simple connected undirected fuzzy graph. In this paper, we use a fuzzy graph model to represent a traffic network of a city and discuss a method to find the different type of accidental zones in a traffic flows. This paper is based on fuzzy coloring of fuzzy graphs and fuzziness of vertices. Using this approach, we can minimize the total waiting time of a traffic flow. It will help to reduce the traffic jam of a road.
Bagging predictors

Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability of the prediction method. If perturbing the learning set can cause significant changes in the predictor constructed, then bagging can improve accuracy.
Reliability and validity of the Test of In-Hand Manipulation in children ages 5 to 6 years.

The Test of In-Hand Manipulation (TIHM; Case-Smith, 2000) is a five-task test that uses a 9-hole pegboard to examine 2 key components of in-hand manipulation: rotation and translation with stabilization. The authors used Rasch modeling to examine the TIHM's construct validity, interrater reliability, and test-retest reliability in 45 typically developing children ages 5.5 years to 6.5 years. A version of the test, revised using Rasch modeling, was found to have evidence for adequate construct validity and excellent interrater reliability. However, test-retest reliability over a 2-week retest period was not supported. The TIHM demonstrates potential as a clinically useful assessment of in-hand manipulation. The test does not examine all aspects of in-hand manipulation, however, and it may have limited sensitivity to the performance of finger-to-palm and palm-to-finger translation. Further validation of the test is needed before the TIHM can confidently be used in occupational therapy practice.
A high voltage half bridge gate driver with mismatch-insensitive dead-time generator

A high voltage half bridge gate driver with mismatchinsensitive dead-time generator is proposed. The high voltage high-side level shifter with common-mode noise canceller technique guarantees the stable operation in negative output voltage level. Unlike a conventional dead-time generator, the proposed dead-time generator uses one delay cell to generate dead-time and shares it with the high-side and low-side paths. The high-side gate driver allows stable negative operation up to −10.2V DC level and −40.5V peak level at 15 V power supply. Measurement results of proposed dead-time generator show 1.7 times and 16.7 times improvement of dead-time mismatch when deadtime is 350 nsec which is minimum set value and 5 μsec, respectively, compared to the conventional dead-time generator.
Handover Control in Wireless Systems via Asynchronous Multiuser Deep Reinforcement Learning

In this paper, we propose a two-layer framework to learn the optimal handover (HO) controllers in possibly large-scale wireless systems supporting mobile Internet-of-Things users or traditional cellular users, where the user mobility patterns could be heterogeneous. In particular, our proposed framework first partitions the user equipments (UEs) with different mobility patterns into clusters, where the mobility patterns are similar in the same cluster. Then, within each cluster, an asynchronous multiuser deep reinforcement learning (RL) scheme is developed to control the HO processes across the UEs in each cluster, in the goal of lowering the HO rate while ensuring certain system throughput. In this scheme, we use a deep neural network (DNN) as an HO controller learned by each UE via RL in a collaborative fashion. Moreover, we use supervised learning in initializing the DNN controller before the execution of RL to exploit what we already know with traditional HO schemes and to mitigate the negative effects of random exploration at the initial stage. Furthermore, we show that the adopted global-parameter-based asynchronous framework enables us to train faster with more UEs, which could nicely address the scalability issue to support large systems. Finally, simulation results demonstrate that the proposed framework can achieve better performance than the state-of-art online schemes, in terms of HO rates.
Social Network Analysis and Knowledge Management : A Triangulation of Sorts ?

As its name suggests, Business Process Management seeks to manage the processes companies typically undertake on a day to day basis. In line with many management techniques, improvements can made through analysing at varying granularity how processes are actually undertaken compared to how management may consider they are being accomplished and vice versa. One innovative way Business Process Management may be improved is through the use of Social Network Analysis to observe actual working relationships among employees. This latter technique permits the workflow manager specifically to consider how well matched employees are to their workflow and as a result of this, we have a means of either reconstructing workflows or alternatively employee practices. A small research-in-progress case study is presented illustrating how these principles may be applied in practice. Overall one may consider such improvements as aiding in the knowledge management of the organization as a whole.
A novel data glove for fingers motion capture using inertial and magnetic measurement units

A novel data glove embedded low cost MEMS inertial and magnetic measurement units, is proposed for fingers motion capture. Each unit consists of a tri-axial gyroscope, a tri-axial accelerometer and a tri-axial magnetometer. The sensor board and processor board are compactly designed, which are small enough to fit the size of our fingers. The data glove is equipped with fifteen units to measure each joint angle of the fingers. Then the calibration approach is put up to improve the accuracy of measurements by both offline and online procedures, and a fast estimation method is used to determine the three orientations of fifteen units simultaneously. The proposed algorithm is easy to be implemented, and more precise and efficient measurements can be obtained as compared with existing methods. The fingers motion capture experiments are implemented to acquire the characteristics of the fingers and teleoperate the robotic hands, which prove the effectiveness of the data glove.