Reviewerly/exHarmony huggingface dataset:
TENFOLD BOOTSTRAP PROCEDURE FOR SUPPORT VECTOR MACHINES

Cross validation is often used to split input data into training and test set in Support vector machines. The two most commonly used cross validation versions are the tenfold and leave-one-out cross validation. Another commonly used resampling method is the random test/train split. The advantage of these methods is that they avoid overﬁtting in the model and perform model selection. They, however, can increase the computational time for ﬁtting Support vector machines with the increase of the size of the dataset. In this research, we propose an alternative for ﬁtting SVM, which we call the tenfold bootstrap for Support vector machines. This resampling procedure can signiﬁcantly reduce execution time despite the big number of observations, while preserving model’s accuracy. With this ﬁnding, we propose a solution to the problem of slow execution time when ﬁtting support vector machines on big datasets.
Learning the Language of Software Errors

&#x0D; &#x0D; &#x0D; We propose to use algorithms for learning deterministic finite automata (DFA), such as Angluin’s L* algorithm, for learning a DFA that describes the possible scenarios under which a given program error occurs. The alphabet of this automaton is given by the user (for instance, a subset of the function call sites or branches), and hence the automaton describes a user-defined abstraction of those scenarios. More generally, the same technique can be used for visualising the behavior of a program or parts thereof. It can also be used for visually comparing different versions of a program (by presenting an automaton for the behavior in the symmetric difference between them), and for assisting in merging several development branches. We present experiments that demonstrate the power of an abstract visual representation of errors and of program segments, accessible via the project’s web page. In addition, our experiments in this paper demonstrate that such automata can be learned efficiently over real-world programs. We also present lazy learning, which is a method for reducing the number of membership queries while using L*, and demonstrate its effectiveness on standard benchmarks.&#x0D; &#x0D; &#x0D;
Cellular Decomposition for Nonrepetitive Coverage Task With Minimum Discontinuities

A mechanism to derive nonrepetitive coverage path solutions with a proven minimal number of discontinuities is proposed in this work, with the aim to avoid unnecessary, costly end-effector lift-offs for manipulators. The problem is motivated by the automatic polishing of an object. Due to the nonbijective mapping between the workspace and the joint-space, a continuous coverage path in the workspace may easily be truncated in the joint-space, incurring undesirable end-effector lift-offs. Inversely, there may be multiple configuration choices to cover the same point of a coverage path through the solution of the inverse kinematics. The solution departs from the conventional local optimization of the coverage path shape in task space, or choosing appropriate but possibly disconnected configurations, to instead explicitly explore the least number of discontinuous motions through the analysis of the structure of valid configurations in joint-space. The two novel contributions of this article include proof that the least number of path discontinuities is predicated on the surrounding environment, independent from the choice of the actual coverage path; thus, has a minimum. In addition, an efficient finite cellular decomposition method to optimally divide the workspace into the minimum number of cells, each traversable without discontinuities by any arbitrary coverage path within. Extensive simulation examples and real-world results on a 5 DoF manipulator are presented to prove the validity of the proposed strategy in realistic settings.
Database Traffic Interception for Graybox Detection of Stored and Context-sensitive XSS

Cross site scripting (XSS) is a type of a security vulnerability that permits injecting malicious code into the client side of a web application. In the simplest situations, XSS vulnerabilities arise when a web application includes the user input in the web output without due sanitization. Such simple XSS vulnerabilities can be detected fairly reliably with blackbox scanners, which inject malicious payload into sensitive parts of HTTP requests and look for the reflected values in the web output. Contemporary blackbox scanners are not effective against stored XSS vulnerabilities, where the malicious payload in an HTTP response originates from the database storage of the web application, rather than from the associated HTTP request. Similarly, many blackbox scanners do not systematically handle context-sensitive XSS vulnerabilities, where the user input is included in the web output after a transformation that prevents the scanner from recognizing the original value but does not sanitize the value sufficiently. Among the combination of two basic data sources (stored vs. reflected) and two basic vulnerability patterns (context sensitive vs. not so), only one is therefore tested systematically by state-of-the-art blackbox scanners. Our work focuses on systematic coverage of the three remaining combinations. We present a graybox mechanism that extends a general purpose database to cooperate with our XSS scanner, reporting and injecting the test inputs at the boundary between the database and the web application. Furthermore, we design a mechanism for identifying the injected inputs in the web output even after encoding by the web application and check whether the encoding sanitizes the injected inputs correctly in the respective browser context. We evaluate our approach on eight mature and technologically diverse web applications, discovering previously unknown and exploitable XSS flaws in each of those applications.
Analysis of Customer Segmentation Based on Broad Learning System

In the field of retail industry and marketing, identifying customer segments is one of the most important tasks. A meaningful segmentation is able to help the managers to enhance the quality of products and services for the targeting segments. Most of traditional methods used POS data to classify the customer loyalty as “heavy” segment while others are belonging to “light” segment. Based on the previous studies, this paper presents three improvements. Firstly, in addition to customer purchasing behavior, we also include RFID (Radio Frequency IDentification) data, which can accurately represent the consumers' in-store behavior. Secondly, this paper uses broad learning system (BLS) to analyze the consumer segmentation. BLS is one of the most state-of-the-art machine learning techniques, and quite efficient and effective for classification tasks. Thirdly, the customer behavior data used in this paper are collected from a real-world supermarket in Japan. We also consider the customer segmentation as a multi-label classification problem based on both of POS data and RFID data. In the experiment, the results were compared with other popular classification models, such as neural network and support vector machine, and it was found that BLS greatly reduced training time while guaranteeing accuracy.
A NOVEL APPROACH FOR SHARPENING BLUR IMAGE USING CONVOLUTIONAL NEURAL NETWORKS

Image sharpening is done by passing blur picture as input and obtaining the sharp one at full resolution as output. The technique of sharpening blur image has more impact on different fields such as medical imaging, forensic science and astronomy. To enhance an image by using LSTM it takes more time complexity. In order to overcome those drawbacks we proposed CNN algorithm to get better performance by using ResBlocks. The proposed model addresses two general issues: the solver and relative arguments in every scale is same and different arguments in each scale cause instability. Another issue is the input image has different resolutions and different scales. In each scale parameter tweaking is allowed and over-fit is raised to the particular image. The proposed model takes sequence of blurry images in different resolutions and down sampled at encoder as-well-as convert the given input to the feature map then generates latent sharp image at every layer and provide the final sharp image at full resolution by converting back into the original format at decoder. Thus, it reduces the time complexity and gives the stable results.
Fermat Factorization using a Multi-Core System

Factoring a composite odd integer into its prime factors is one of the security problems for some public-key cryptosystems such as the Rivest-Shamir-Adleman cryptosystem.Many strategies have been proposed to solve factorization problem in a fast running time.However, the main drawback of the algorithms used in such strategies is the high computational time needed to find prime factors.Therefore, in this study, we focus on one of the factorization algorithms that is used when the two prime factors are of the same size, namely, the Fermat factorization (FF) algorithm.We investigate the performance of the FF method using three parameters: (1) the number of bits for the composite odd integer, (2) size of the difference between the two prime factors, and (3) number of threads used.The results of our experiments in which we used different parameters values indicate that the running time of the parallel FF algorithm is faster than that of the sequential FF algorithm.The maximum speed up achieved by the parallel FF algorithm is 6.7 times that of the sequential FF algorithm using 12 cores.Moreover, the parallel FF algorithm has near-linear scalability.
EE-LEACH: Energy Enhancement in LEACH to Improve Network Life Time of Homogeneous Wireless Sensor Network

Purpose ”“ In the recent scenario, there are various issues related to wireless sensor networks such as clustering, routing, packet loss, network strength. The core functionality of primarily wireless sensor networks is sensor nodes that are randomly scattered over a specific area. The sensor senses the data and sends it to the base station. Energy consumption is an important issue in wireless sensor networks. Clustering and cluster head selection is an important method used to extend the lifetime of wireless sensor networks. The main goal of this research article is to reduce energy consumption using a clustering process such as CH determination, cluster formation, and data dissemination.&#x0D; &#x0D; Methodology/approach/design ”“ The simulation in this paper was finished utilizing MATLAB programming methodology and the proposed technique is contrasted with the LEACH and MOD-LEACH protocols.&#x0D; &#x0D; Findings ”“ The simulation results of this research show that the energy consumption and dead node ratio are improved of wireless sensor networks as compared to the LEACH and MOD-LEACH algorithms.&#x0D; &#x0D; Originality/value ”“ In the wireless sensor network there are various constraints energy is one of them. In order to solve this problem use CH selection algorithms to reduce energy consumption and consequently increase network lifetime.
Stock Market Analysis using LSTM in Deep Learning

Stock market prediction is that the scene of trying to complete the long-run value of company stock.Analysis of the stock price we take the price.By using the neural network, we develop a model.within the neural network, we use a recurrent neural network that remembers each and each information through time.LSTM networks maintain to stay contextual information of inputs by associate a loop that permits information to travel from one step to the following.These loops make recurrent neural networks seem magical.Each x train has the last 60 days value for the present-day y data.this can add more accurate results when a measure to existing stock price prediction algorithms.The network is trained and evaluated for accuracy with various sizes of knowledge, and also the results are tabulated.
Person Re-ID by Fusion of Video Silhouettes and Wearable Signals for Home Monitoring Applications

The use of visual sensors for monitoring people in their living environments is critical in processing more accurate health measurements, but their use is undermined by the issue of privacy. Silhouettes, generated from RGB video, can help towards alleviating the issue of privacy to some considerable degree. However, the use of silhouettes would make it rather complex to discriminate between different subjects, preventing a subject-tailored analysis of the data within a free-living, multi-occupancy home. This limitation can be overcome with a strategic fusion of sensors that involves wearable accelerometer devices, which can be used in conjunction with the silhouette video data, to match video clips to a specific patient being monitored. The proposed method simultaneously solves the problem of Person ReID using silhouettes and enables home monitoring systems to employ sensor fusion techniques for data analysis. We develop a multimodal deep-learning detection framework that maps short video clips and accelerations into a latent space where the Euclidean distance can be measured to match video and acceleration streams. We train our method on the SPHERE Calorie Dataset, for which we show an average area under the ROC curve of 76.3% and an assignment accuracy of 77.4%. In addition, we propose a novel triplet loss for which we demonstrate improving performances and convergence speed.
A heterogeneous branch and multi-level classification network for person re-identification

Convolutional neural networks with multiple branches have recently been proved highly effective in person re-identification (re-ID). Researchers design multi-branch networks using part models, yet they always attribute the effectiveness to multiple parts. In addition, existing multi-branch networks always have isomorphic branches, which lack structural diversity. In order to improve this problem, we propose a novel Heterogeneous Branch and Multi-level Classification Network (HBMCN), which is designed based on the pre-trained ResNet-50 model. A new heterogeneous branch, SE-Res-Branch, is proposed based on the SE-Res module, which consists of the Squeeze-and-Excitation block and the residual block. Furthermore, a new multi-level classification joint objective function is proposed for the supervised learning of HBMCN, whereby multi-level features are extracted from multiple high-level layers and concatenated to represent a person. Based on three public person re-ID benchmarks (Market1501, DukeMTMC-reID and CUHK03), experimental results show that the proposed HBMCN reaches 94.4%, 85.7% and 73.8% in Rank-1, and 85.7%, 74.6% and 69.0% in mAP, achieving a state-of-the-art performance. Further analysis demonstrates that the specially designed heterogeneous branch performs better than an isomorphic branch, and multi-level classification provides more discriminative features compared to single-level classification. As a result, HBMCN provides substantial further improvements in person re-ID tasks.
IsoBN: Fine-Tuning BERT with Isotropic Batch Normalization

Fine-tuning pre-trained language models (PTLMs), such as BERT and its better variant RoBERTa, has been a common practice for advancing performance in natural language understanding (NLU) tasks. Recent advance in representation learning shows that isotropic (i.e., unit-variance and uncorrelated) embeddings can significantly improve performance on downstream tasks with faster convergence and better generalization. The isotropy of the pre-trained embeddings in PTLMs, however, is relatively under-explored. In this paper, we analyze the isotropy of the pre-trained [CLS] embeddings of PTLMs with straightforward visualization, and point out two major issues: high variance in their standard deviation, and high correlation between different dimensions. We also propose a new network regularization method, isotropic batch normalization (IsoBN) to address the issues, towards learning more isotropic representations in fine-tuning by dynamically penalizing dominating principal components. This simple yet effective fine-tuning method yields about 1.0 absolute increment on the average of seven NLU tasks.
Acoustic Modeling in Speech Recognition: A Systematic Review

The paper presents a systematic review of acoustic modeling (AM) techniques in speech recognition(SR).Acoustic modeling establishes a relationship between acoustic information and language construct in SR.Over the past decades, researchers presented studies addressing specific concerns in AM.However, all previous research works lack a systematic and comprehensive review of acoustic modeling issues.A systematic review is introduced to understand the acoustic modeling issues in speech recognition.This paper provides an extensive and comprehensive inspection of various researches that have been performed since 1984.The extensive investigation and analysis into AM was performed by getting the relevant data from 73 research works chose after the screening process between the years from 1984 to 2020.The systematic review process was divided into different parts to investigate acoustic modeling issues.Main issues in acoustic modeling such as feature extraction techniques, acoustic modeling units, speech corpora, classification methods, different tools used, language issues applied, and evaluation parameters were investigated.This study helps the reader to understand various acoustic modeling issues with comprehensive details.The research outcomes presented in this study depict research trends and shed light on new research topics in AM.The result of this review can be used to build a better speech recognition system by choosing a suitable acoustic modeling construct in SR.
ACMNet

Cross-modality human behavior analysis has attracted much attention from both academia and industry. In this article, we focus on the cross-modality image-text retrieval problem for human behavior analysis, which can learn a common latent space for cross-modality data and thus benefit the understanding of human behavior with data from different modalities. Existing state-of-the-art cross-modality image-text retrieval models tend to be fine-grained region-word matching approaches, where they begin with measuring similarities for each image region or text word followed by aggregating them to estimate the global image-text similarity. However, it is observed that such fine-grained approaches often encounter the similarity bias problem, because they only consider matched text words for an image region or matched image regions for a text word for similarity calculation, but they totally ignore unmatched words/regions, which might still be salient enough to affect the global image-text similarity. In this article, we propose an Adaptive Confidence Matching Network (ACMNet), which is also a fine-grained matching approach, to effectively deal with such a similarity bias. Apart from calculating the local similarity for each region(/word) with its matched words(/regions), ACMNet also introduces a confidence score for the local similarity by leveraging the global text(/image) information, which is expected to help measure the semantic relatedness of the region(/word) to the whole text(/image). Moreover, ACMNet also incorporates the confidence scores together with the local similarities in estimating the global image-text similarity. To verify the effectiveness of ACMNet, we conduct extensive experiments and make comparisons with state-of-the-art methods on two benchmark datasets, i.e., Flickr30k and MS COCO. Experimental results show that the proposed ACMNet can outperform the state-of-the-art methods by a clear margin, which well demonstrates the effectiveness of the proposed ACMNet in human behavior analysis and the reasonableness of tackling the mentioned similarity bias issue.
Multi-hop nondestructive teleportation between terminal nodes equipped with limited technology

In this paper, we propose a scheme for the multi-hop nondestructive teleportation of an arbitrary single-qudit state via channel modulation performed by one cloud node in a network. Considering the limited quantum technology of the sender, the receiver, and the ordinary intermediate nodes, we assume that there are certain cloud nodes in a quantum network equipped with quantum processing capability of performing high-dimensional multi-qudit unitary operations. In our scheme, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>N</mml:mi> </mml:math> intermediate nodes, involving one cloud node and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>N</mml:mi> <mml:mo>−<!-- − --></mml:mo> <mml:mn>1</mml:mn> </mml:math> ordinary relay nodes, are connected in a chain through <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>N</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:math> different non-maximally entangled Bell pairs. Our scheme reduces the hardware requirement of the terminals, which makes it more flexible and adaptable in a network. In addition, our scheme enables preservation of the original single-qudit state, even if the teleportation fails. Our finding provides a methodology for the multi-hop nondestructive teleportation in a network with limited technical processing capabilities of terminals.
Multi-domain Spoken Language Understanding Using Domain- and Task-aware Parameterization

Spoken language understanding (SLU) has been addressed as a supervised learning problem, where a set of training data is available for each domain. However, annotating data for a new domain can be both financially costly and non-scalable. One existing approach solves the problem by conducting multi-domain learning where parameters are shared for joint training across domains, which is domain-agnostic and task-agnostic . In the article, we propose to improve the parameterization of this method by using domain-specific and task-specific model parameters for fine-grained knowledge representation and transfer. Experiments on five domains show that our model is more effective for multi-domain SLU and obtain the best results. In addition, we show its transferability when adapting to a new domain with little data, outperforming the prior best model by 12.4%. Finally, we explore the strong pre-trained model in our framework and find that the contributions from our framework do not fully overlap with contextualized word representations (RoBERTa).