Reviewerly/exHarmony huggingface dataset:
Grouping Business News Articles using Multiple Objectives Based Particle Swarm Optimization

with the increase in internet use, a large amount of online business news text articles are posting on web portals, social media platforms, and other information-sharing platforms. For any business news platform, it is very crucial for organizing such business news articles for their subscribers, readers, traders, and investors. These business articles are on focused and specific themes like new hiring, funding, IPO, NFO, merger, and acquisitions. These themes may overlap with each other because of similar domain jargon and vocabulary. Efficient segregation of these business articles makes it easier to manage and handle. The clustering algorithm can be useful for grouping and segregating business articles efficiently. These groups and clusters are highly similar within clusters compared to other clusters. Clustering business articles is applicable in web mining, search engine, business news summarization, and information retrieval. In this paper, we are clustering the business news articles scraped from various news portals using various feature engineerings such as BoW, TF-IDF, Part of Speech (PoS) tags, Named Entity Recognition (NER) tags, and Word Embedding such as Word2Vec (CBOW and Skip-gram) and Doc2Vec (PV-DBOW and PV-DM) and grouped the business news articles using Multi-objective Particle Swarm optimization (MOPSO) clustering approach. We have compared the results with available cluster analysis techniques such as K-means, GMM, hierarchical, spectral, and HMM clustering techniques.
BGGF: A Gated Information Fusion Model For Biomedical Entity Recognition

Extracting valuable information from the biomedical literature is gaining attention among researchers, and Biomedical Named Entity Recognition (BioNER) becomes one of the most essential tasks in text mining. Previous studies have shown that long-distance interactions between words play an important role in enhancing hidden representations in NER. However, the existing mainstream NER models treat texts as plain linear sequences, resulting in the loss of structural information in sentences, such as BiLSTM-CRF. To overcome this shortcoming, this paper proposes BGGF, a novel model that is able to capture both semantic information in text sequences and structural information in dependency trees. Experiments are conducted on BC2GM and NCBI disease datasets to demonstrate the effectiveness of the proposed model in improving BioNER. The resulting model achieves state-of-the-art performance on the BC2GM and NCBI disease datasets.
Efficiency-First Fault-Tolerant Replica Scheduling Strategy for Reliability Constrained Cloud Application

Reliability requirement assurance is an important prerequisite for application execution in the cloud. Although copy management can improve the reliability of applications, it also brings a series of resource waste and overhead issues. Therefore, the efficiency-first fault-tolerant algorithm (EFFT) with minimum execution cost in the cloud application is proposed. This algorithm minimizes the execution cost of the application under the constraints of reliability, and solves the problem of excessive overhead caused by too many copies. The EFFT algorithm is divided into two stages: initial allocation and dynamic adjustment. On the initial allocation of EFFT algorithm, a sorting rule is defined to determine the priority of tasks and instances. During the adjustment phase, by defining an actual efficiency ratio indicator to measure the cost-effectiveness of an instance, the EFFT algorithm makes a good trade-off between cost and reliability in order to minimize execution costs. Run our algorithm on randomly generated parallel applications of different scales and compare the experimental results with four advanced algorithms. The experiments show that the performance of the algorithm we proposed is better than the other algorithms in terms of execution cost and fault tolerance.
Multi-player Reach-Avoid Game in Dynamic Environment

With autonomy gaining popularity and acceptance in various fields, humans have begun shifting to employing autonomous vehicles to perform risky tasks in hostile environment. Therefore, it is crucial that we investigate motion planning problem with safety in mind. In this sense, reach-avoid game is a good problem to analyze due to its complex nature and the fact that worst-case disturbance is considered. In this work we present an iterative open-loop formulation for a reach-avoid game with multiple attackers and defenders. We show the proposed method can find more direct paths compared to the basic open-loop formulation, and perhaps more importantly, the algorithm can find feasible paths in situation where open-loop formulation simply cannot. By solving the game with the proposed method we can guarantee the safety of the planned path, despite the dynamic environment with the presence of unpredictable obstacles, adversarial defender, as well as other attackers. In addition, we offer a practical method that ensures agents with minimum turning radius only take paths that respect the constraints. We verify the effectiveness of our method through a series of simulations.
Building the Future

Abstract The fourth chapter describes how technological decentralisation emerged with advancements in cryptography and acted as a political counterweight of resistance to the encroachment of centralised governments across (online) spaces. The decentralist worldview is shown to be rooted in the specific political geography of the West Coast of the United States that, during the latter half of the 20th century, became a crucible of counterculture and entrepreneurship. Fuelled by this vision, a monetarist desire to create fairer economies through algorithmic decentralisation gave rise to the advent of cryptocurrencies. The intersection and slippage of this technologically deterministic imaginary (preaching a freedom from hierarchy and control) with geographies of material practice is developed throughout following chapters.
A Visual Programming Interface for Digital Luthiery: Implementing Circuits with Veneer

Abstract This article presents a method for programming musical signal-processing circuits visually, using expressive idioms and abstractions from functional programming. Special attention is paid to the creative workflow, framing the education in a constructionist context. Our aim is to empower musicians in signal processing: The claim was tested in a university workshop for relatively inexperienced programmers. The participants were able to study and implement signal-processing algorithms from literature and integrate them into their preexisting workflow, and appeared to gain self-confidence while doing so.
An Optimal Thresholder for Ancient Degraded Palm leaf Document Binarization

This paper presents a novel adaptive thresholding algorithmic model for ancient document image binarization. The objective of the algorithm is mainly to perform the removal of ink bleed troughs, smears, smudges and non-uniform illumination in palm leaf documents and also on degradation such as reflection of text incurred from other side of two-sided degraded text document images as well as palm leaf, Algorithm transmits the document image through an established sequence of steps such as contrast stretching transforms, conversion to greyscale image, a heuristic adaptive thresholder and post enhancement protocols. Experimentations are conducted on various bench marking ancient data-sets of DIBCO 2009, DIBCO 2010, DIBCO 2011, DIBCO 2012, DIBCO 2013, DIBCO 2014, DIBCO, 2016, DIBCO 2017, DIBCO 2018 and 30 palm leaf thanthrashashtam documents made available as a part of document image binarization contest. The results prove the efficiency of our algorithm towards handling variety of degradations with respect to DIBCO data-sets as well as palm leaf documents with its simplicity carried out through objective evaluation methods. A better PSNR of more than 10 is obtained for more than 80% of both documents and validation of enhanced documents with ground truths produced an average precision and sensitivity of about 95% for all the document samples tested.
An Unique ‘Assessment Framework’ for Agility in Software Development Projects

The purpose of this paper is to develop unique assessment framework for evaluating agility in Software Development Project (SDP). This study makes an original contribution to the body of knowledge related to agility in SDPs. The conceptual framework is developed by using four enablers, twenty three criteria and one hundred and twenty three attributes for agility level assessment in SDP. The multi-grade fuzzy approach is used for development of this assessment framework. The identified major enablers are team agility, process agility, engineering agility, and organizational agility. With the help of this framework, project managers can identify weaker attributes in their current projects, thereby improving overall SDP agility. It is a generalized and continuous assessment tool for evaluating the agility level of SDP.
An image classification network for network traffic representation learning

Network traffic classification builds a mapping between traffic data and application types according to traffic characteristics, which is one of the basic tasks in the field of network planning, maintenance management, and network security[5]. Due to the rapid development of network technology and the rapid increase of network traffic, it is extremly necessary and urgent for rapid and accurate automatic classification of network traffic. Through the research of ResNet[3], DenseNet[4], GoogleN et[1] and other classic convolutional neural networks, this paper proposes a lightweight network traffic classification algorithm, which completes the network traffic classification task based on the design ideas and structural advantages of the MobileNet[2]. The comparative experimental results demonstrate that the algorithm can significantly reduce the training time and model size, and achieves a superior performance of network traffic classification.
DIFFERENTIAL PRIVACY FOR ENERGY DATA PUBLICATION

DSOs need to publish data to enable energy transition but they also need to ensure their customers data protection. In order to satisfy both these contradictory requirements. Enedis, the French DSO works towards new anonymization methods, tuned for the specific and highly correlated data it manages: half hourly consumption time series. When applied to aggregated data, classical differential privacy needs a huge ‘point to point’ privacy budget and creates a highly distorted result. In this paper, we present a new two-step approach for data protection for publication purpose. In the first step, the load time series are projected on a customized wavelet base, taking known structural elements of our Time Series into account. In the second step differential privacy technique is applied with small privacy budget but good protection level. The proposed method ensure significant reduction in privacy budget in comparison with classical differential privacy and a good level of protection. The protected aggregated curves obtained with the proposed method can then be published with limited risk of privacy leaks.
Image Block Regression Based on Feature Fusion for CNN-Based Spatial Steganalysis

The research on image steganalysis has taken a great step forward thanks to the development of convolutional neural network (CNN). However, most existing CNN steganalyzers require deep network architecture and a long training time to obtain good performance. Besides, most methods input a cover image and the corresponding stego image as a pair during the training stage. In this case, not only the label, but also the texture difference between the cover and the stego can be used. In this paper, we propose a novel method for spatial domain steganalysis to boost the detection performance through a secondary task. First, a concise and efficient CNN is used to distinguish stego images from cover images. Second, an up-sampling module via transposed convolution and feature fusion is used to predict the number of modified pixels in different blocks of the image, which is called block regression. The loss functions of these two parts above are optimized simultaneously. Experimental results show that the proposed method can obtain a better performance in detection error rate compared to previous works and a good trade-off between training time and detection accuracy.
Real-time Queue Detection Using an Omnidirectional Camera

Omnidirectional cameras, which can capture an image that covers a 360-degree view, have been widely used in recent years. This study proposes a method to detect human queues in images captured with omnidirectional cameras. The method can be applied to further research in human flow analysis and congestion estimation. In this study, we analyzed the real space using panoramic images captured by an omnidirectional camera. These images contain distortions because the spherical image captured is converted into a flat image, making feature detection difficult. Previous studies focused on object detection and position estimation; our study, however, focused on detecting the direction of a person to enable the detection of a queue. Our results show that straight one-directional queues are detectable, but queues in different formations present problems.
Software reliability prediction using data abstraction and Random forest Algorithm

The opportunity of early estimating of the possibly faultiness of software should assist on making plans, controlling and executing software development sports correctly. It will also give the developer and users the confidence to appropriately select and apply models to their required systems. It is very essential to understand and quantify the relation between faults and failure for efficient detection and prevention for quality software development. The proper investigation of the sources of fault and failure helps developers to focus their efforts in right direction. This enhances the fault and failure detection and prevention mechanism for the quality and cost effective solution in software development. So in this paper we are going to use data extraction method to extract data and to predict values we are going to use random forest algorithm also here we have defined flow diagram and Failure rate decreases, schedule time execution. In next step we are trying to implement these technique to give very high reliability value.
SS-CCN: Scale Self-guided Crowd Counting Network

With the emergence of deep learning, many CNN-based methods have achieved competitive performance in crowd counting, in which how to effectively solve the scale variation problem plays a key role. To tackle with the problem, we present an innovative scale self-guided crowd counting network (SS-CCN) by taking full advantage of scale information in a multi-level network. The proposed SS-CCN highlights crowd information by applying scale enhancement and scale-aware attention modules in multi-level features. Moreover, semantic attention module is applied on deep layers to extract semantic information. Besides, the fine-grained residual module is proposed to further refine the crowd information. Furthermore, we pioneer a scale pyramid loss with different loss functions applied to different scales. Integrating the proposed module, our method can effectively solve the scale variation problem. Extensive experimental results on several public datasets show that our proposed SS-CCN achieves satisfactory and superior performance compared to the state-of-the-art methods.
Biomedical Image Segmentation Based on Classification Supervision

Convolutional neural networks (CNN) has been widely used in the biomedical image segmentation (BIS) for their remarkable feature representation capability. However, there are often segmentation errors and missing segmentation problems in biomedical image segmentation based on deep learning. In this paper, we propose a full convolutional neural network, which is assisted by classification supervision based on segmentation network. The algorithm first obtains a segmentation result through a basic segmentation network. Then a Classification Supervision Module (CSM) is designed to enable the network to judge whether each slicer contains lesions from the perspective of classification. In this way we allow the network to take advantage of more global information. Experimental results on several available databases demonstrate the effectiveness and advancement of the proposed method.
Generalization in Cardiac Image Segmentation

Deep learning methods have achieved great success in medical imaging applications. Although data is very crucial for deep learning models, the medical imaging domain is restricted by the limited size of datasets and differences between them. This makes it difficult for models to generalize across datasets and achieve robust performance in practical settings. Therefore, knowing how to combine different datasets together to take advantage of new data while retaining performance on previous data becomes an important problem. In this paper, we focus on the task of cardiac image semantic segmentation and synthesize five different real-world scenarios to find the optimal training approach for deep learning models to achieve good generalization across different datasets.
Smart IoT Based People Counting System

People counting is of interest in many commercial scenarios. The number of people entering and leaving shops, the occupancy of office buildings or the passenger count of trains provide useful information to shop owners, security officials, train operators, tourism management, transport management and disaster management. To that end, this paper proposes a scheme for counting people based on a variety of approaches. One with RaspberryPi and USB webcam and another with Arduino UNO and IR sensors and further compare their accuracies. In the work it is observed that people counter using IR sensors is much more accurate than the counter which uses USB webcam and OpenCV algorithm. But different ways to increase the frame per seconds of the webcam also make RaspberryPi more efficient to process the OpenCV algorithms accurately.
A Scalable Multi-factor Fault Analysis Framework for Information Systems

Information systems such as cellular networks produce large volumes of data, making the characterization of network faults a difficult task. In this work, we introduce a new fault analysis framework based on association rule mining and validate it to identify the root cause of faults in information systems. The paper describes a strategy using association rules to specifically target faults while improving runtime performance relative to the standard Apache Spark implementation. We also introduce a novel association rule filtering strategy called Cover Set filtering that prunes and merges rule sets to produce high-quality, concise and interpretable results. The proposed framework is evaluated with real-world telecommunication datasets. Comparing this framework approach with other strategies, we demonstrate a better rule diversity in general and a sufficiently compact analysis of the faults. The validation of our experimental results was conducted by telecommunications experts, who confirmed the clarity and value of the results for a quantitative assessment of cellular network failures.
Research on Measurement Method of Tread Pattern Groove Depth Based on Computer Vision and Structured Light Measurement Technology

This paper presents a fast and convenient method to detect the tread pattern groove depths, which is based on computer vision and structured light measurement technology. Firstly, the intrinsic parameter matrix of the camera needs to be calibrated. Secondly, the centerline pixel coordinates that the centerline of the structured light stripe on the calibration target are extracted by irradiating the structured light to the calibration target. Thirdly, the camera coordinates are calculated by extrinsic parameters. The RANSAC algorithm is used to fit the a structured light plane in the camera coordinate system. Finally, the groove point is located by calculating the gradient change in the neighborhood of the pixel point, and the groove depth is calculated by the distance between the groove point and the calculation baseline. The experiment results show that the precision of this method meets the real-time measurement requirements.
Model Selection in Local Approximation Gaussian Processes: A Markov Random Fields Approach

Local approximations are popular methods to scale Gaussian processes (GPs) to big data. Local approximations reduce time complexity by dividing the original dataset into subsets and training a local expert on each subset. Aggregating the experts' prediction is done assuming either conditional dependence or independence between the experts. Imposing the \emph{conditional independence assumption} (CI) between the experts renders the aggregation of different expert predictions time efficient at the cost of poor uncertainty quantification. On the other hand, modeling dependent experts can provide precise predictions and uncertainty quantification at the expense of impractically high computational costs. By eliminating weak experts via a theory-guided expert selection step, we substantially reduce the computational cost of aggregating dependent experts while ensuring calibrated uncertainty quantification. We leverage techniques from the literature on undirected graphical models, using sparse precision matrices that encode conditional dependencies between experts to select the most important experts. Moreov
AutoEncoder regularization using Support Vector Data Description for Anomaly Detection

In computer vision, learning discriminative features to detect anomalies in images is a challenge. The majority of deep learning-based image anomaly detection approaches are compression-reconstruction or generation-based models that were not initially intended for the anomaly detection task. Only a few methods involve dedicated objective function to help detect anomalies and they are not visually explainable as well as reconstruction based approaches. Though the popular reconstruction-based approach for anomaly detection using Convolutional AutoEncoder has achieved the state of the art results, there is no provision to induce the fabrication of discriminatively learnt embeddings from the inputs to well reflect anomalies in the output. We propose an approach using Support Vector Data Description as a regularizer to enforce discriminative ability to easily segregate anomalies from normality with little effort in modelling and tuning the AutoEncoders in our work. We evaluate our approach on several visual anomaly detection datasets to show the capability of our approach. We also perform extensive ablation studies for efficient tuning of parameters.
Calibrating Word-Sized Graphics for Financial Accounting Applications: Evidence and Examples

ABSTRACT We argue that, to be complete, financial reports should provide information to users about all “material” items. It is therefore desirable that all material effects in financial graphs should be visible to users. If small, word-size graphics are calibrated improperly, users may overlook visually small but numerically material effects. Prior literature does not provide adequate design guidance. We test subjects' ability to detect differing absolute and relative size effects in sparklines. We expand the concept of cognitive fit to include minimum relative and absolute visual sizes of key effects. Consistent with the legibility literature, we find accounting student participants' ability to detect patterns and anomalies over certain approximate minimum relative and absolute size thresholds is robust. Two examples illustrate how our findings on approximate relative and absolute size thresholds apply to financial accounting applications. JEL Classifications: M41; M40.