Reviewerly/exHarmony huggingface dataset:
Scalable Multi Corpora Neural Language Models for ASR

Neural language models (NLM) have been shown to outperform conventional n-gram language models by a substantial margin in Automatic Speech Recognition (ASR) and other tasks. There are, however, a number of challenges that need to be addressed for an NLM to be used in a practical large-scale ASR system. In this paper, we present solutions to some of the challenges, including training NLM from heterogenous corpora, limiting latency impact and handling personalized bias in the second-pass rescorer. Overall, we show that we can achieve a 6.2% relative WER reduction using neural LM in a second-pass n-best rescoring framework with a minimal increase in latency.
Concept Drift Detection Of Event Streams Using An Adaptive Window

Process mining is an emerging data mining task of gathering valuable knowledge out of the huge collections of business operation data. Despite its relatively young age, it has successfully provided many new insights into business workflows using established data mining techniques. Recently, with the huge improvements in the technologies of sensoring, collection and storing of data, a big demand for both shorter mining times and adaptive models of streaming process events arose. This initiated the field of stream process mining very recently. Drifts in the underlying concepts of the business processes are of a great interest for decision makers. One important advantage of stream process mining techniques over static ones is the ability to detect such drifts and to adapt its models accordingly. In this paper, we introduce an efficient approach that uses the collected information of an event stream miner to detect concept drifts. We use a dynamic window, which grows in size for stationary process behavior and shrinks for diverting data and thus indicating a concept drift. This adaptive window is used to build a model by focusing only on up-to-date information and discarding outdated items. Extensive experimental evaluations over real and synthetic log files show the ability of our algorithm to detect sudden drifts. We additionally show the effectiveness of our concept detection method in setting the pruning period of a recent stream mining algorithm.
High-accuracy calibration of line-structured light vision sensors using a plane mirror

A high-accuracy calibration technique using a white paper and a front coating plane mirror is proposed in this paper for line-structured light vision sensors. This method shows advantages in two aspects. First, a white paper can gain a very high-quality light stripe due to its approximate ideal diffuse Lambertian sheet, which overcomes the problems associated with the strong reflecting light and serious burrs of the light stripe on conventional rigid targets. Second, based on a front coating plane mirror with lithographic feature points, we can obtain a bilateral symmetric structure similar to a virtual binocular stereo vision to recover the 3D coordinates of the light stripe centers on white paper with high accuracy. Front coating guarantees the coplanarity with the lithographic feature points and avoids imaging distortion caused by refraction during back coating. Therefore, front coating can be used to obtain high accuracy structural parameters of the virtual binocular stereo vision sensors. Meanwhile, for the light stripe and its image in the plane mirror are auto-epipolar with all the epipolar lines arranged in parallel. These lines intersect at a vanishing point in the camera image, and this epipolar constraint is used to complete the matching of the light stripe centers without the need for the camera parameters. Experiments are conducted to demonstrate the performance of the proposed method.
Microc alcification Segmentation Using Modified U-net Segmentation Network from Mammogram Images

Breast cancer is the most common aggressive cancer in women while the early detection of this cancer can reduce the aggressiveness. But it is challenging to identify breast cancer features such as micro-calcification from mammogram images by the human eye because of its size and appearance. Therefore, the automatic detection of micro-calcification is essential for diagnosis and proper treatment. This work introduces an automated approach and segments any micro-calcification in the mammogram images. At first, the preprocessing applications of images are applied to enhance the image. After that, the breast region is segmented from the pectoral region. The suspicious regions are detected using fuzzy C-means clustering algorithm and divided them into negative and positive patches. This procedure eliminates the manual labelling of the region of interest. The positive patches which contain micro-calcification pixels are taken to train a modified U-net segmentation network. Finally, the trained network is utilised to segment the micro-calcification area automatically from the mammogram images. This process can help as an assistant to the radiologist for early diagnosis and increase the segmentation accuracy of the micro-calcification regions. The proposed system is trained up with a Digital Database for Screening Mammography (DDSM), which is prepared by the University of South Florida, USA. We obtain 98.5% F-measure and 97.8% Dice score respectively. Besides, Jaccard index is 97.4%. The average accuracy of the proposed method is 98.2% which provides better performance than state-of-the-art methods. This work can be embedded with the real-time mammography system.
QOS AND DEFENSE ENHANCEMENT USING BLOCK CHAIN FOR FLY WIRELESS NETWORKS

The autonomous mobile nodes framing an instantaneous network, utilizing the nearby available device that volunteer in establishing network is coined as the fly wire network. These adhoc type of network framed simultaneously are prone to various vulnerabilities, developing alterations in the information’s or hacking of information’s or the blocking of services. These security threats causing losses in the information transmitted, makes it necessary for the trust evaluation of the nodes to identify the selfish nodes. So the paper proposes the block chain trust management of nodes to avoid the vulnerabilities in the transmission path, enhancing the performance of the network. The performance of the proposed method is validated in the network simulator –II to ensure its capability in terms of the quality of service and the security (defense).
Feature extraction of face image based on LBP and 2-D Gabor wavelet transform

Affected by illumination, gesture, expression and other factor's variation, face image pattern is easy to be changed, so it is important to find a robust data representation for the correct classification of face pattern. In this paper, a face image recognition algorithm based on 2-D Gabor wavelet transform and Local Binary Pattern (LBP) is proposed. LBP is a local describe operator, which is invariant against illumination variation. 2-D Gabor wavelet transform have the invariant property against pose and expression variation. Experimental results show that the large scale 2-D Gabor wavelet representation could get good classification accuracy. Using LBP to describe 2-D Gabor wavelet representation of face image, together with image block, histogram statistics, PCA dimensionality reduction, nearestneighbors classification, we finally find this algorithm can get a better classification performance in different scales and directions.
Providing a new approach to increase fault tolerance in cloud computing using fuzzy logic

It is generally accepted that with the ever-increasing need of users to various resources, cloud computing is rapidly evolving as one of the new practical technologies. Cloud computing can be categorized as a computing solution in which required technology or services allow users to access computing resources on demand. Moreover, fault tolerance is one of the major concerns to ensure availability and reliability of services as well as to perform the tasks. In order to minimize the impact of failures on the system and ensure correct task execution, they must be anticipated and managed. Few newly developed methods for fault tolerance have focused on fault detection dimension. Therefore, in this paper, a detailed analysis of the nature of the error and its detection will provide as well as a fuzzy-based method to prepare an appropriate response to the error tolerance. In order to increase the error tolerance and load balancing when the error occurs, the requesting a task re-execution and migration techniques through the checkpoint are used. The migration technique overlaps with time, so check-point use can avoid re-execution as well as tasks as much as possible. The results of the experiments also indicate a mean superiority of 6.49% for accuracy criterion in comparison with ABFT method and 2.27% in comparison with FFD algorithm.
Entropy evaluation of white chaos generated by optical heterodyne for certifying physical random number generators

The entropy of white chaos is evaluated to certify physical random number generators. White chaos is generated from the electric subtraction of two optical heterodyne signals of two chaotic outputs in semiconductor lasers with optical feedback. We use the statistical test suites of NIST Special Publication 800-90B for the evaluation of physical entropy sources of white chaos with an eight-bit resolution. The minimum value of entropy is 2.1 for eight most significant bits data. The entropy of white chaos is enhanced from that of the chaotic output of the semiconductor lasers. We evaluate the effect of detection noise and distinguish between the entropy that originates from the white chaos and the detection noise. It is found that the entropy of five most significant bits originates from white chaos. The minimum value of entropy is 1.1 for five most significant bits data, and it is considered that the entropy can be obtained at at least one bit per sample.
Implicit Aspect Extraction for Sentiment Analysis: A Survey of Recent Approaches

The research in Sentiment analysis (SA) is in vastly growing stage as people become more expressive on social media, blogs, forums and e-commerce websites by sharing their opinions, reviews and comments. In Aspect-level SA opinions about various aspect or features of an entity is extracted. Users specify aspects by explicit words (i.e. Explicit aspects) or sometimes the aspects must be inferred from the text (implicit aspects).Detecting implicit aspects is challenging but very important and limited studies focused on the extraction of implicit aspects. This paper provides a survey on recently proposed techniques for detecting implicit aspects. We have classified the studies according to approaches they have followed, also specified limitations and future work stated by authors. We have discussed different issues in implicit aspect extraction which will give directions for future research.
Cryptanalysis of a New Color Image Encryption Using Combination of the 1D Chaotic Map

Security of image communication is more and more important in many applications such as the transmission of military and medical images. To meet the requirement, a new color image encryption algorithm using a new one-dimension (1D) chaotic map was proposed recently, which can resist various attacks because the range of the new chaotic map is larger than that of the previous ones. In our study, the security of the new original method is analyzed and a novel attack method is proposed. It is demonstrated that the scheme is not secure under chosen-plaintext attack, by which the encrypted image can be successfully converted into the corresponding plaintext image without any error.
Breaking the Decisional Diffie-Hellman Problem for Class Group Actions Using Genus Theory

In this paper, we use genus theory to analyze the hardness of the decisional Diffie–Hellman problem (DDH) for ideal class groups of imaginary quadratic orders, acting on sets of elliptic curves through isogenies; such actions are used in the Couveignes–Rostovtsev–Stolbunov protocol and in CSIDH. Concretely, genus theory equips every imaginary quadratic order $$\mathcal {O}$$ with a set of assigned characters $$\chi : {\text {cl}}(\mathcal {O}) \rightarrow \{ \pm 1\}$$ , and for each such character and every secret ideal class $$[\mathfrak {a}]$$ connecting two public elliptic curves E and $$E' = [\mathfrak {a}] \star E$$ , we show how to compute $$\chi ([\mathfrak {a}])$$ given only E and $$E'$$ , i.e. without knowledge of $$[\mathfrak {a}]$$ . In practice, this breaks DDH as soon as the class number is even, which is true for a density 1 subset of all imaginary quadratic orders. For instance, our attack works very efficiently for all supersingular elliptic curves over $$\mathbb {F}_p$$ with $$p \equiv 1 \bmod 4$$ . Our method relies on computing Tate pairings and walking down isogeny volcanoes.
Autonomous Driving Control Using End-to-End Deep Learning

With the development of the artificial intelligence technology, various industries have developed. Among them, the autonomous vehicle industry has developed considerably, and researches has been carried out actively on self-driving control systems using artificial intelligence. Recently, studies have been conducted on the use of image-based end-to-end deep learning to control autonomous driving systems. An image-based end-to-end autonomous system can be configured at a low cost, in a simple system that omits complex processes. This system receives images as the input and outputs control signals. In this paper, we propose an autonomous driving control system by using end-to-end deep learning.
OpenQL: A Portable Quantum Programming Framework for Quantum Accelerators

With the potential of quantum algorithms to solve intractable classical problems, quantum computing is rapidly evolving, and more algorithms are being developed and optimized. Expressing these quantum algorithms using a high-level language and making them executable on a quantum processor while abstracting away hardware details is a challenging task. First, a quantum programming language should provide an intuitive programming interface to describe those algorithms. Then a compiler has to transform the program into a quantum circuit, optimize it, and map it to the target quantum processor respecting the hardware constraints such as the supported quantum operations, the qubit connectivity, and the control electronics limitations. In this article, we propose a quantum programming framework named OpenQL, which includes a high-level quantum programming language and its associated quantum compiler. We present the programming interface of OpenQL, we describe the different layers of the compiler and how we can provide portability over different qubit technologies. Our experiments show that OpenQL allows the execution of the same high-level algorithm on two different qubit technologies, namely superconducting qubits and Si-Spin qubits. Besides the executable code, OpenQL also produces an intermediate quantum assembly code, which is technology independent and can be simulated using the QX simulator.
A comprehensive survey on Indian regional language processing

In recent information explosion, contents in internet are multilingual and majority will be in the form of natural languages. Processing of these natural languages for various language processing tasks is challenging. The Indian regional languages are considered to be low resourced when compared to other languages. In this survey, the various approaches and techniques contributed by the researchers for Indian regional language processing are reviewed. The tasks like machine translation, Named Entity Recognition, Sentiment Analysis and Parts-Of-Speech tagging are reviewed with respect to Rule, Statistical and Neural based approaches. The challenges which motivate to solve language processing problems are presented. The sources of dataset for the Indian regional languages are described. The future scope and essential requirements to enhance the processing of Indian regional languages for various language processing tasks are discussed.ϖ
Reducing Overfitting Problem in Machine Learning Using Novel L1/4 Regularization Method

The Machine learning model has two problems, they are Overfitting and Under-fitting. Underfitting is a statistical model or a machine learning algorithm, it cannot capture the underlying trend of the data. A statistical model is said to be overfitted when it has been trained with more data. When the model is trained on fewer features, the machine will be too biased, and then the model gets under fitting problems. So, it has been required to train the model on more features and there is one more problem that occurs. To reduce the overfitting problem, regularization functions and data augmentation are used. Lasso shrinks the less important feature's coefficient to zero thus removing some feature altogether. L2 regularization, on the other hand, does not remove most of the features. A novel regularization method is proposed to overcome these problems.
Automated Neoliberalism? The Digital Organisation of Markets In Technoscientific Capitalism

The core contradiction in neoliberalism (studies) is that markets are organised and require significant bureaucratic coordination and governance. In light of the increasingly technoscientific nature of contemporary capitalism, it is important to examine exactly how markets are organised and their governance configured by digital processes. In this article, I argue that the entanglement of digital technoscience and capitalism has led to an 'automated neoliberalism' in which markets are configured by digital platforms, personal lives are transformed through the accumulation of personal data, and social relations are automated through algorithms, distributed electronic ledgers, and rating systems. Two issues arise as a result of these changes: first, are markets being automated away, in that market exchange no longer underpins social organisation? And second, does individual and social reflexivity problematise techno-economic automation, in that new platforms, data assets, ranking algorithms, etc. are all dependent on individuals telling the 'truth'? My aim in this article is to answer these questions and to consider the political implications of automated neoliberalism and our reflexive enrolment in it.
The Devil is in Classification: A Simple Framework for Long-tail Instance Segmentation

Most existing object instance detection and segmentation models only workwell on fairly balanced benchmarks where per-category training sample numbersare comparable, such as COCO. They tend to suffer performance drop on realisticdatasets that are usually long-tailed. This work aims to study and address suchopen challenges. Specifically, we systematically investigate performance dropof the state-of-the-art two-stage instance segmentation model Mask R-CNN on therecent long-tail LVIS dataset, and unveil that a major cause is the inaccurateclassification of object proposals. Based on such an observation, we firstconsider various techniques for improving long-tail classification performancewhich indeed enhance instance segmentation results. We then propose a simplecalibration framework to more effectively alleviate classification head biaswith a bi-level class balanced sampling approach. Without bells and whistles,it significantly boosts the performance of instance segmentation for tailclasses on the recent LVIS dataset and our sampled COCO-LT dataset. Ouranalysis provides useful insights for solving long-tail instance detection andsegmentation problems, and the straightforward \emph{SimCal} method can serveas a simple but strong baseline. With the method we have won the 2019 LVISchallenge. Codes and models are available at\url{https://github.com/twangnh/SimCal}.
Application of deep learning for solar irradiance and solar photovoltaic multi-parameter forecast

In this study, the use of artificial neural network (ANN) models for solar irradiance and solar PV parameters forecasting in Nigeria is presented. Although solar irradiance prediction exists in literatures, the use of ANN for solar PV parameter prediction has not been considered in previous studies. Six different locations are selected in Nigeria and data from these locations have been used to train/test the ANN models developed. This study aims to model ANN algorithms that can forecast solar irradiance and solar PV parameters based on an hourly time-step more accurately. A deep learning regression model built on Levenberg-Marquardt back propagation algorithm is used to train and test the model for all the locations considered. Four different ANN models were developed for each location in Keras python using all the input parameters. The evaluation metrics used in this study are; R, R-squared, RMSE, and MAE. The models developed are capable of predicting solar irradiance and solar PV parameters. The R values for the ANN models range from 0.9046–0.9777 for solar irradiance and 0.7768–0.8739 for solar PV multi-parameters prediction.
Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue

A multi-turn dialogue is composed of multiple utterances from two or more different speaker roles. Thus utterance- and speaker-aware clues are supposed to be well captured in models. However, in the existing retrieval-based multi-turn dialogue modeling, the pre-trained language models (PrLMs) as encoder represent the dialogues coarsely by taking the pairwise dialogue history and candidate response as a whole, the hierarchical information on either utterance interrelation or speaker roles coupled in such representations is not well addressed. In this work, we propose a novel model to fill such a gap by modeling the effective utterance-aware and speaker-aware representations entailed in a dialogue history. In detail, we decouple the contextualized word representations by masking mechanisms in Transformer-based PrLM, making each word only focus on the words in current utterance, other utterances, two speaker roles (i.e., utterances of sender and utterances of receiver), respectively. Experimental results show that our method boosts the strong ELECTRA baseline substantially in four public benchmark datasets, and achieves various new state-of-the-art performance over previous methods. A series of ablation studies are conducted to demonstrate the effectiveness of our method.
Intelligent online English teaching system based on SVM algorithm and complex network

English Online teaching quality evaluation refers to the process of using effective technical means to comprehensively collect, sort and analyze the teaching status and make value judgments to improve teaching activities and improve teaching quality. The research work of this paper is mainly around the design of teaching quality evaluation model based on machine learning theory and has done in-depth research on the preprocessing of evaluation indicators and the construction of support vector machine teaching quality evaluation model. Moreover, this study uses improved principal component analysis to reduce the dimensionality of the evaluation index, thus avoiding the impact of the overly complicated network model on the prediction effect. In addition, in order to verify that the model proposed in this study has more advantages in evaluating teaching quality than other shallow models, the parameters of the model are tuned, and a control experiment is designed to verify the performance of the model. The research results show that this research model has a certain effect on the evaluation of school teaching quality, and it can be applied to practice.