Reviewerly/exHarmony huggingface dataset:
A method to remove image noise based on 6-tap Filter Interpolation

Abstract A method to remove the noise of image based on 6-tap filter interpolation is investigated in this work. To this goal, the algorithm principle of 6-tap filter interpolation including 1 / 2 pixel interpolation process, 1 / 4 pixel interpolation process and 1 / 8 pixel interpolation process are firstly analyzed. Then, in order to validate the effectiveness of 6-tap filter interpolation for the image denoising, two images added with Gaussian noise are processed by 6-tap filter interpolation and the SNR is used to evaluate the post-processing image. The obtained results show the SNR of post-image is larger than that of pre-processing image, which proves that the 6-tap filter interpolation can be acted as an effective method to remove the noise of image.
How Do Enterprise Software Providers Adapt their Strategies to the Cloud? An Analysis through Sap Hana Journey based on the Evolution of Sap’S Discourse (2010-2018)

This paper seeks to provide a better understanding of software business providers' strategy when adapting to the emergence of cloud computing markets.Based on a longitudinal case-study and on a historical content analysis of SAP's discourse, it highlights four main periods of adaptation, since 2009.The analysis of these four periods emphasizes the existence of an initial superior technology (HANA) on the ERP market when referring to cloud-based solutions.Overall, the evolution of SAP's strategy is understood as a change from a high level of control over the administration of its technological environment to a more flexible strategy that gives alternative options such as Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) mode.Content analysis shows that SAP's discourse gives an increasing importance to agreements with third parties in order to mitigate the perception of the perceived lock-in effect.
Part-of-Speech Tagging for Arabic Text using Particle Swarm Optimization and Genetic Algorithm

Background: Part of Speech (POS) Tagging is a process of defining the suitable part of speech for each word in the given context such as defining if a word is a verb, a noun or a particle. POS tagging is an important preprocessing step in many Natural Language Processing (NLP) applications such as question answering, text summarization, and information retrieval. Objective: The performance of NLP applications depends on the accuracy of POS taggers since assigning right tags for the words in a sentence enables the application to work properly after tagging. Many approaches have been proposed for the Arabic language, but more investigations are needed to improve the efficiency of Arabic POS taggers. Method: In this study, we propose a supervised POS tagging system for the Arabic language using Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) as well as Hidden Markov Model (HMM). The tagging process is considered as an optimization problem and illustrated as a swarm which consists of group of particles. Each particle represents sequence of tags. The PSO algorithm is applied to find the best sequence of tags which represent the correct tags of the sentence. The genetic operators: crossover and mutation are used to find personal best, global best, and velocity of the PSO algorithm. HMM is used to find the fitness of particles in the swarm. Results : The performance of the proposed approach is evaluated on the KALIMAT dataset which consists of 18 million words and a tag set consists of 45 tags which covers all Arabic POS tags. The proposed tagger achieved an accuracy of 90.5%. Conclusion: Experimental results revealed that the proposed tagger achieved promising results compared to four existing approaches. Other approaches can identify only three tags: noun, verb and particle. Also, the accuracy for some tags are outperformed those achieved by other approaches.
Improved Water Algorithm Based on K - Means Clustering Character Segmentation Method

When the adhesion of the adjacent two characters happen., if using traditional dripping algorithm., can make the water droplets in the vertical drop down two conglutination., character split phenomenon. If the character split character for character recognition operation., can't get the correct recognition as a result., when recognition accuracy is greatly reduced. Therefore., in order to solve this problem., based on the traditional algorithm on the basis of drop water., through the K - Means clustering method to determine the initial point of water., put forward the character segmentation method based on improved drop algorithm., reduce the error of the characters segmentation and segmentation.
A Case Study for Blockchain in OTC: “BATN”: A Prototype for Bid and Ask Trading Network

With product iteration and emerging market demand, traditional organizations have to look for ways to exchange information and trade options/swaps under trust-less network. In this paper, we propose – “BATN”: a prototype for bid and ask trading network based on Blockchain. We propose a system in which a decentralized network of trading agencies, supervisor institutions and witness nodes can enable public bid/ask, automated risk evaluation, and transparent supervision through a trail of historic trading behavior and smart contracts. Our system decentralizes critical information about alliance governance, authentication and credit evaluation through a verifiable audit trail. We present BATN using Hyperchain through which a consortium of geographically dispersed organizations can operate business, sign e-contracts and report transactions anywhere and anytime. Our prototype makes sure the transaction per second on a decentralized network can meet trading volume in reality. This paper intends to improve business effectiveness for much larger fields through the working prototype and discusses the potential of blockchain for finance IT.
Descriptive and prescriptive software

Every few months or so since graduate school, I encounter new circumstances that remind me of the relation between the descriptive and prescriptive approaches humans use to understand and navigate the world in which we live. My most recent reminder occurred while reading about bias in software and efforts by researchers to reduce the negative impacts of this bias on society by (re) designing algorithms to address such biases as gender, racial, age, etc. Typically, "software bias" arises from using a descriptive model of human behavior within a prescriptive program.
Exploring the Interfaces Between Big Data and Intellectual Property Law

JIPITEC 10 (2019) 1 - This article reviews the application of several IP rights (copyright, patent, sui generis database right, data exclusivity and trade secret) to Big Data. Beyond the protection of software used to collect and process Big Data corpora, copyright’s traditional role is challenged by the relatively unstructured nature of the non-relational (noSQL) databases typical of Big Data corpora. This also impacts the application of the EU sui generis right in databases. Misappropriation (tort-based) or anti-parasitic behaviour protection might apply, where available, to data generated by AI systems that has high but short-lived value. Copyright in material contained in Big Data corpora must also be considered. Exceptions for Text and Data Mining (TDM) are already in place in a number of legal systems and likely to emerge to allow the creation and use of corpora of literary and artistic works, such as texts and images. In the patent field, AI systems using Big Data corpora of patents and scientific literature can be used to expand patent applications. They can also be used to “guess” and disclose future incremental innovation. These developments pose serious doctrinal and normative challenges to the patent system and the incentives it creates in a number of areas, though data exclusivity regimes can fill certain gaps in patent protection for pharmaceutical and chemical products. Finally, trade secret law, in combination with contracts and technological protection measures, can protect data corpora and sets of correlations and insights generated by AI systems.
Random Net Implementation of MLP and LSTMs Using Averaging Ensembles of Deep Learning Models

Long Short Term Memory Networks are a special type of recurrent Neural Network having better performance. LSTMs are widely used for sequence prediction. The multilayer perceptron (MLP) provides a nonlinear mapping between an input vector and a corresponding output vector. MLPs are a popular class of feedforward neural networks used for binary as well as multi-class classification. Ensemble learning is a technique by which the variance in the performance of a deep learning model is reduced by combining learning from various models for a combination of different training data as well as other parameters. This paper is discussing an experiment where this mechanism is implemented for binary classification using MLP and LSTM sequence prediction. A random Net implementation using an ensemble of MLPs as well as LSTMs is discussed here. The network is trained for a random length of the training sequence and the performance of single and ensemble classifiers is compared. The results show that the ensemble classifier is more robust and has a better stability.
BenderNet and RingerNet: Highly Efficient Line Segmentation Deep Neural Network Architectures for Ice Rink Localization

A critical step for computer vision-driven hockey ice rink localization from broadcast video is the automatic segmentation of lines on the rink. While the leveraging of segmentation methods for sports field localization has been previously explored, the design of deep neural networks for segmenting ice rink lines has not been well studied. Furthermore, the exploration of efficient architecture designs is very important given the operational requirements of real-time sports analytics. Motivated by this, BenderNet and RingerNet, two highly efficient deep neural network architectures, have been designed specifically for ice rink line segmentation. Experiments on a dataset of annotated NHL broadcast video demonstrate high accuracy while maintaining high model efficiency, thus making the proposed methods well-suited for real-time ice hockey rink localization.
False Discovery Rates to Detect Signals from Incomplete Spatially Aggregated Data

There are a number of ways to test for the absence/presence of a spatial signal in a completely observed fine-resolution image. One of these is a powerful nonparametric procedure called enhanced false discovery rate (EFDR). A drawback of EFDR is that it requires the data to be defined on regular pixels in a rectangular spatial domain. Here, we develop an EFDR procedure for possibly incomplete data defined on irregular small areas. Motivated by statistical learning, we use conditional simulation (CS) to condition on the available data and simulate the full rectangular image at its finest resolution many times (M, say). EFDR is then applied to each of these simulations resulting in M estimates of the signal and M statistically dependent p-values. Averaging over these estimates yields a single, combined estimate of a possible signal, but inference is needed to determine whether there really is a signal present. We test the original null hypothesis of no signal by combining the M p-values into a single p-value using copulas and a composite likelihood. If the null hypothesis of no signal is rejected, we use the combined estimate. We call this new procedure EFDR-CS and, to demonstrate its effectiveness, we show results from a simulation study; an experiment where we introduce aggregation and incompleteness into temperature-change data in the Asia-Pacific; and an application to total-column carbon dioxide from satellite remote sensing data over a region of the Middle East, Afghanistan, and the western part of Pakistan. Supplementary materials for this article are available online.
Indonesian Gender Equality Survey Analysis Using Feature Selection Based Clustering

This paper presents an analysis of an Indonesian gender equality survey: in 2019, we conducted a survey of attitudes about gender roles in Indonesia and obtained data from 122 individuals. The obtained data were analyzed using our original clustering method (UFVS, Unsupervised Feature Value Selection) to form clusters. The extracted features characterized the clusters and helped to analyze the attitudes of Indonesians towards gender equality. This method allowed the respondents to be grouped by features and each group characteristics could be easily identified. It facilitated the understanding of the survey data.
Particle Filtering Tracking Study of Automatic Extraction Tracking Range

Abstract What needs to be solved is the problem of automatic tracking of pedestrians in a complex monitoring environment. In the actual monitoring environment, there are usually chaotic scenes, noise, light changes, and constant changes in human motion, in this context, the post-test probability and observation probability are non-Gaussic, nonlinear, so the framework of particle filtering is chosen to solve the pedestrian tracking problem. In target modeling, human motion is non-rigid body deformation, and color features for the target plane rotation, non-rigid deformation, partial masking and other situations are more robust, so in tracking pedestrians to choose color features. This paper proposes an overlay algorithm that automatically selects the maximum attribute area to determine the trace area. Finally, this paper uses color features to realize the automatic tracking of pedestrians under the theoretical framework of particle filtering.
Wearable statues game

In sports, automatic judgments made by electronic devices such as sensors are more accurate than those made by humans. However, automatic judgments are not always satisfactory to players and audiences. In sports that involve ambiguous judging criteria based on the subjectivity of human judges, rigorous automatic judgments may decrease the satisfaction of players and audiences. This decrease hinders the installation of automatic judging, even though computerized officiating is more effective than human judgment in several ways. In this study, we aim to construct an automatic judgment system that satisfies both players and audiences. To investigate the factors that affect the conviction of players and audiences, we implemented an automatic judgment system used for the Japanese statues game. Our system recognizes human motion using an acceleration sensor and automatically judges whether the players are moving. We conducted a user study to compare two threshold patterns for the recognition of the motion.
Research on Server Fault Diagnosis Based on Expert System

With the rapid development of network technology, computer resources, including server resources, have shown a trend of high concentration, multiple devices, and multiple types. As a result, various types of server failures occur frequently, not only for basic monitoring software monitoring services. It puts forward higher requirements and has higher strength in the timeliness and accuracy of sending alarm information. Therefore, it is necessary to converge and analyze the alarm information and give expert-level solutions to the fault information. This article first designed the monitoring and alarm module using the flask framework to enable it to receive alarm information uniformly, converge and merge the alarm information, and use the Vue framework to display the alarm information on the page. Then, use the Python PyCLIPS library based on CLIPS to build the inference engine of the server fault diagnosis expert system. Finally use the “production rules” expert system knowledge expression to complete the construction of the knowledge base.
Target motion detection algorithm based on dynamic threshold

Abstract Realizing moving target detection through visual algorithms is a major branch of computer technology. Moving target detection has a wide range of applications in many fields such as video surveillance, unmanned driving, and behavior research. The current algorithm has been able to achieve moving target detection well in a static background, but the detection of multiple moving targets in a dynamic background is still a challenging research area. The main pain points are complex background, light changes, noise interference and Occlusion and real-time detection requirements, etc. Aiming at the detection of moving targets in complex scenes, a moving target detection algorithm based on dynamic threshold is proposed. The algorithm first uses the gray difference gradient of three adjacent frames of the video sequence to calculate the segmentation threshold of foreground moving targets to realize the dynamic threshold. Then use dynamic thresholds to achieve multi-target motion detection in complex backgrounds or scenes with changing lighting. Because the gradient change of adjacent frames of the scene is considered in the process of moving target detection and segmentation, and the gradient change is converted into a dynamic threshold to participate in moving target detection and segmentation, the algorithm can adapt to scenes with changing lighting. Experiments show that this algorithm is feasible for moving target detection in complex scenes, and has a lower error rate than that of traditional algorithms, and is more suitable for to complex or changing lighting scenes.
Analysis of Detecting White Blood Cells by Computer Vision Methods

Experts in both medicine and computer vision were engaged in the study of WBC images. Since WBC can be approximated by ellipsoid shape, an algorithm for the ellipse detector can be implemented to recognize them successfully. This paper introduces a complicated and unpacked novel algorithm for scanning WBC automatically, which sees the whole process as a problem for the sensing of multiellipse. The process converts the detection function into an optimization problem based on the novel differential evolution algorithm (DE), which is used to mimic ellipses of individuals. An objective function assesses whether these ellipses really exist in the edging image of the smear. The encoded candidate group creates ellipses (electors) using the novel DE algorithm to fit into the WBC, based on the values of a single edge map image.
Processing Multilingual Word Embeddings of Indian Languages with Semi-supervised Learning

Most of the methods used nowadays for learning bilingual word embeddings are dependent on vast parallel corpora or dictionaries, which is rather tough to cover for many languages, and when it comes to Indian languages, the language pairs not unavailable most of the times. With modern-day natural language processing algorithms, we can surpass this hurdle, with a notion of convolutional neural nets. To surpass this requirement, a simple self-learning approach has been adopted in this paper, which diminishes the requirement of document aligned corpora for languages under test. The approach of this paper is to reduce the use of bilingual resources for mapping and device a own learning technique that can relate with a dictionary relied mapping algorithm. The system can work with as less as 30-word pairs and still perform at par with the algorithms using richer sources of bilingual data.
Performance of Machine Learning Algorithms for IT Incident Management

Incident Management is a part of managing IT services, improving services, and achieving organizational goals. IT incidents can be learned and predicted future incidents. This research compares the factors that cause incidents using initial machine learning techniques such as Random Forest, SVM, Multilayer perceptron, and the latest machine learning techniques such as RNN, LSTM, GRU, to predict IT incidents. Grid search is used to find the optimal parameter combination. 5-fold and 10-fold Cross-validation evaluates the model's optimal performance by dividing the dataset into training data and test data. The results show that the highest accuracy of 98.866% is produced by LSTM machine learning techniques at 5-fold and 10-fold cross-validation. SVM has the lowest accuracy of 97.837% made at 5-fold and 10-fold cross-validation.
Hot Topic Detection Based on Image Intelligent Understanding and Comment Mining

Micro-blog has become the main distribution center of public opinion. Therefore, how to mine hot topics from microblog has become a hot topic. However, microblogs are mostly short texts and contain a large number of invalid information, which significantly increases the difficulty of hot topic detection. On the other hand, micro-blog includes text, pictures and comment information, which greatly enriches the diversity of information. Therefore, this paper proposed a new method, which applies deep learning to image understanding. Image content is interpreted as analyzable text semantic information combined with comments to enrich short text information. Experimental results show the effectiveness of the proposed method, and better experimental results are obtained compared with other methods.
Combining Symbolic and Data-Driven Methods for Goal Recognition

Recently, there is an increased research interest in context-aware systems that are able to autonomously and intelligently support users with their tasks. An important feature of such systems is the ability to dynamically adapt to the current needs of their users. Hence, it is critical for such systems to be able to recognize the user's current activities and goals. While there is some work on the problem of goal recognition in this context, the majority of research works focus on the problem of recognizing a user's current activities. Furthermore, the existing methods mostly rely on purely symbolic methods, which have problems to handle low signals in the observed user data. As a consequence, these approaches are not able to reliably recognize the user goals as early as it should be possible, based on the information contained in the observed data. Hence, in our research, we focus on the combination of symbolic and data-driven methods to hybrid methods for goal recognition and their application in the context of pervasive computing environments like smart homes.
Scalable and Updatable Attribute-based Privacy Protection Scheme for Big Data Publishing

To ensure data security and privacy during big data publishing, it is challenging to design a security and privacy protection scheme for the big data environment with a large scale of users. At the same time, due to the users' dynamically joining and exiting, it is also very important to design a user's dynamic update mechanism. To address such challenges, we design a novel scalable and updatable attribute-based privacy protection scheme (SUAPP) for big data publishing. The proposed scheme can realize users' hierarchical management, which can reduce the overhead on key generation and management caused by the large scale of data users in the big data center (BDC). We set a user group for each attribute, then adapt the Chinese remaining theorem to dynamically assist the big data center to generate and update group keys for the attribute users group. Analyses and experiments show that while ensuring the privacy protection of big data publishing, our scheme also has low communication and computation overhead and higher efficiency compared with two state peer schemes.