Reviewerly/exHarmony huggingface dataset:
Extracting Valences from a Dependency Treebank for Populating the Verb Lexicon of a Portuguese HPSG Grammar

We propose a methodology to populate the verb type hierarchy of a deep computational grammar in the HPSG formalism using syntactic and morphological information from Universal Dependencies (UD) treebanks. It is exemplified by means of the UD Bosque corpus and PorGram, a computational grammar for Portuguese constructed in the LinGO Grammar Matrix framework, but it can be applied to analogous grammars of other languages using other UD treebanks. The main component of the methodology is a Python module that extracts from the annotated sentences the core arguments and other features that are relevant to determine verb valence. This module enables the creation of a Python dictionary that maps valence frames to verb objects. This dictionary facilitates not only determining which frames occur with which verbs, but also detecting annotation errors. The potential of the module for rapid expansion of the lexical coverage of PorGram and corpus annotation error detection is illustrated with concrete examples.
Design and Implementation of MIPS Experiment Platform Based on FPGA Device

MIPS architecture shows an important function in computer science knowledge graph. A MIPS experiment platform with FPGA is explained in this article containing MIPS CPU implement, the method of connecting to PC and run programs of MIPS assembly language (with the help of an operating system). Compared to software simulators, the MIPS experiment platform may give MIPS learners intuitive experience of learning how MIPS architecture works. Furthermore, they may design and implement instructions of their own.
Optimization methods for collision aircraft avoidance

This paper presents the geometric equations of motion for an aircraft in 3D space, and a series of numerical simulations have been performed, investigating the analysis of the TCAS system near an obstacle. This numerical simulation includes two distinct cases of interest; 1. when the obstacle is considered fixed (with the presentation of the situations in which there is a danger of a collision and in which there is no imminent danger) and 2. when the obstacle is considered mobile (another aircraft). A sphere with a radius of 5NM (nautical miles) has been created around the obstacle which represents the protection volume of the aircraft considered in the study. After the detailed analysis of the presented situations, the trajectory of the aircraft has been determined in case of bypassing the obstacle, as well as in the situation where there is an imminent danger of collision between the intruder aircraft and the aircraft considered as the target.
Parameter Tuning for the $${(1 + (\lambda , \lambda ))}$$ Genetic Algorithm Using Landscape Analysis and Machine Learning

The choice of parameter values in evolutionary algorithms greatly affects their performance. Many popular parameter tuning techniques are limited by the tuning budget for finding a good set of parameter values. Recently, we proposed an approach to parameter tuning that uses fitness landscape analysis and machine learning to recommend good parameter values for problem instances based on their landscape features. Using fitness landscape features allows to identify similar problems and use parameter tuning data obtained on benchmark problems, significantly reducing the tuning budget requirements. In this paper, we present our study of the landscape-aware parameter tuning approach for the $${(1 + (\lambda , \lambda ))}$$ genetic algorithm. We evaluate the performance of the algorithm tuned by this approach on the linear integer weights problem and the MAX-3SAT problem, in addition to the W-model problem used for the collection of training data. Our results suggest that the proposed approach allows to make meaningful parameter choices and shows good performance without high fitness evaluation budget requirements.
A New Approach to E-Health Application Development Using Blockchain

Drugs are a marvel of modern medicine. With the huge drug market present in the world today, there is also a huge bane to the existence of drugs which is the presence of counterfeit drugs in the market that may cause irreversible damage to us. This chapter proposes a solution to counter this menace and save lives. After exploring the existing solutions in the global market and reviewing academic literature, it was found that such a comprehensive solution has not yet been implemented in India or the world. A comprehensive, affordable, easily accessible mobile application and website is proposed as the outcome of this research work. The application can provide the consumer with necessary details like the constituent salts, side effects, age barrier, safe consumption limit, and the purpose of the drug. To achieve this, a database of all known drugs and their images will be created and will be continuously updated with the help of machine learning and artificial intelligence. The chapter also discusses the business model to make it commercially viable.
Beyond a Video Frame Interpolator: A Space Decoupled Learning Approach to Continuous Image Transition

Video frame interpolation (VFI) aims to improve the temporal resolution of a video sequence. Most of the existing deep learning based VFI methods adopt off-the-shelf optical flow algorithms to estimate the bidirectional flows and interpolate the missing frames accordingly. Though having achieved a great success, these methods require much human experience to tune the bidirectional flows and often generate unpleasant results when the estimated flows are not accurate. In this work, we rethink the VFI problem and formulate it as a continuous image transition (CIT) task, whose key issue is to transition an image from one space to another space continuously. More specifically, we learn to implicitly decouple the images into a translatable flow space and a non-translatable feature space. The former depicts the translatable states between the given images, while the later aims to reconstruct the intermediate features that cannot be directly translated. In this way, we can easily perform image interpolation in the flow space and intermediate image synthesis in the feature space, obtaining a CIT model. The proposed space decoupled learning (SDL) approach is simple to implement, while it provides an effective framework to a variety of CIT problems beyond VFI, such as style transfer and image morphing. Our extensive experiments on a variety of CIT tasks demonstrate the superiority of SDL to existing methods. The source code and models can be found at https://github.com/yangxy/SDL .
Nearest-Neighbor Modified Constructive Covering Oversampling Method

The classification of imbalanced data is gradually becoming an important problem for research in machine learning and data mining. Oversampling is one of the effective approaches to solve the problem. However, how to generate minority samples efficiently and how to process the samples located in the decision boundary are still problems which are worthy of studying. To this end, a modified oversampling algorithm called Nearest-neighbor Modified Constructive Covering Oversampling Method (NMCCO) based on Constructive Coverage Algorithm (CCA) and K-Nearest Neighbor algorithm (KNN) is proposed in this study. It first cleans the majority class samples which located at the boundaries by combining KNN and CCA. Then, it utilizes the coverage radius to constrain the synthesis of minority samples based on the distribution information of minority samples. Numerical experiments conducted on 100 imbalanced datasets (KEEL) with 4 different classifiers validate the effectiveness of the proposed NMCCO.
Project Planning, Management, Monitoring, and More

Project management is the structuring of a project in terms of visible and manageable activities, which are the building blocks for executing the project across its lifecycle. In particular, one of the critical phases of the lifecycle of a project is project planning. Project planning must include the main strategy for a working method within the project team. It should structure the tasks and describe the tools for monitoring the execution and controlling the project's progress. On the one hand, this chapter describes the essential steps of a project. On the other hand, it presents the most used perspectives and methods such as PMBOK, PRINCE, Agile, Scrum, and supporting technologies such as Asana, BaseCamp, Trello, Slack, or Jira.
Research on the Method of Building a Secure Cloud Storage Platform

With the advent of the Internet information age in China, the number of documents generated by users while using the Internet is increasing, thus giving rise to the problem of how to carry out secure and convenient storage of large amounts of data, which can be effectively solved by cloud technology. By means of cloud storage, people lightened the load on hardware, but for the lack of control over storage hardware, security is still subject to discussion. This paper will discuss the split and merging of source documents, encryption of source document division table, recovery and backup of source documents, etc.
A Secure and Reliable IoT-Edge Framework with Blockchains for Smart MicroGrids

Growing energy demands and the rise in global warming calls for effective management of electric grid and renewable energy integration into the grid. Internet of Things, along with cloud computing has made the monitoring and control of the grid effective. To reduce latency and provide real-time monitoring of the grid Edge computing is an effective solution. Growing concerns of cyber-attacks in the grid system demands for security measures to be considered while designing the microgrid system. This work proposes a secure IoT-Edge framework for smart microgrid systems. The framework defines how edge computing will play a role in the microgrid system. The computing layers includes a blockchain-based security layer. Blockchains have proved to be an effective solution for secure transactions. There are many frameworks available for implementing blockchain. This work discusses the use cases of blockchain in energy sector and advises the use of two blockchain frameworks: Energy Web Chain for operational methods and CordaR3 based Distributed Ledger Technology for energy trading and other financial transactions in the grid.
Improving Association Rules Accuracy in Noisy Domains Using Instance Reduction Techniques

Association rules' learning is a machine learning method used in finding underlying associations in large datasets.Whether intentionally or unintentionally present, noise in training instances causes overfitting while building the classifier and negatively impacts classification accuracy.This paper uses instance reduction techniques for the datasets before mining the association rules and building the classifier.Instance reduction techniques were originally developed to reduce memory requirements in instance-based learning.This paper utilizes them to remove noise from the dataset before training the association rules classifier.Extensive experiments were conducted to assess the accuracy of association rules with different instance reduction techniques, namely: Decremental Reduction Optimization Procedure (DROP) 3, DROP5, ALL K-Nearest Neighbors (ALLKNN), Edited Nearest Neighbor (ENN), and Repeated Edited Nearest Neighbor (RENN) in different noise ratios.Experiments show that instance reduction techniques substantially improved the average classification accuracy on three different noise levels: 0%, 5%, and 10%.The RENN algorithm achieved the highest levels of accuracy with a significant improvement on seven out of eight used datasets from the University of California Irvine (UCI) machine learning repository.The improvements were more apparent in the 5% and the 10% noise cases.When RENN was applied, the average classification accuracy for the eight datasets in the zero-noise test enhanced from 70.47% to 76.65% compared to the original test.The average accuracy was improved from 66.08% to 77.47% for the 5%-noise case and from 59.89% to 77.59% in the 10%-noise case.Higher confidence was also reported in building the association rules when RENN was used.The above results indicate that RENN is a good solution in removing noise and avoiding overfitting during the construction of the association rules classifier, especially in noisy domains.
A Rank Metric Code-Based Group Signature Scheme

Group signature is a major tool in today's cryptography. Rank based cryptography has been known for almost 30 years and recently reached the second round of the NIST competition for post-quantum primitives. In this work, we present a code-based group signature scheme in the rank metric context. The scheme follows the path presented by Ezerman et al. (ASIACRYPT' 2015) for Hamming metric but in a rank metric context which requires some specific adaptation and generalization. The scheme used a rank metric variation of the Stern's authentication scheme and relies solely on generic decoding problems. It also satisfies the $$\mathsf {CPA}$$ -anonymity and traceability properties in the random oracle model. In general the parameters of our scheme are slightly better compared to the Hamming scheme.
Trajectory-Predicting Network: A Deep Learning Method Enhancing Feasibility of Path Planning

Feasibility of planned path represents the viability and accuracy for unmanned aerial vehicles (UAVs) to track. It is a bridge between UAV path planning and tracking, and also an important guarantee for the safe and efficient completion of the mission of UAV. At present, most researchers only take account of the simplified model and UAV state constraints when considering the path planning, but do not further consider the dynamics constraints of the UAV itself and the performance of the designed path tracking controller, which results in a certain distance error between the planned path and the actual trajectory. When UAVs perform missions in complex and dense areas, the actual trajectory generated by the planned path command even collides with obstacles, which is difficult to guarantee mission safety. This paper proposes a trajectory-predicting network (TPN) based on the deep learning, which characterize the complex nonlinear relation between the planning command and the actual trajectory considering nonlinear model and controller of the closed-loop system. In time of path planning, the predicted trajectory obtained by TPN corresponding to the planning command is evaluated to find the optimal planning command. The optimal trajectory is regarded as a planning path, and its corresponding command as path tracking control input of the control system. Simulation results verify the viability and effectiveness of the proposed method.
Shape-based tumor microenvironment analysis to differentiate non-small cell lung cancer subtypes: a radio-pathomic study

Shaped-based descriptors from Computed Tomography (CT) scans and whole slide digital pathology images were used to differentiate the two major histopathological subtypes of non-small-cell lung cancer (NSCLC). Our two hypotheses are 1) Encoding information on local heterogeneity will augment the model’s classification capabilities 2) Shape-based biomarkers from radiology and pathology can complement each other. Shape features were extracted from the tumor map from pathology and radiology images. In pathology, tumor-microenvironment features were encoded by clustering the tumor map into phenotype maps. These features performed better than the features from whole tumor map. Integration of radio-pathomics performed best, achieving 0.802 AUC.
The Learners’ Perceptions of Learning Design for Mobile MOOCs

Massive Open Online Courses (MOOCs) and the use of mobile learning are two learning/teaching forms that have received much attention in recent years. Despite the popularity of both approaches and the efforts to integrate or combination them, there is still a lack of papers that are focusing on the aspect of learning design for mobile MOOCs. To compensate for this lack, this paper is examining the preferences of MOOC learners between mobile and non-mobile MOOCs with respect to a number of crucial learning design parameters. An online anonymous survey was answered by 68 MOOCs participants, The results indicate that on the one hand, they think that MOOCs’ accessibility and flexibility are better supported using a mobile MOOC format. On the other hand, they think that the ability to concentrate, motivation, self-study/self-learning, and self-regulated learning are better supported in a non-mobile, conventional MOOC. The main conclusion drawn is that when it comes to learning design parameters that can promote deep learning, the participants perceive the non-mobile format as being superior. Yet, flexibility and accessibility are also important when it comes to creating inclusive learning environments. Thus, it seems that the creation of an inclusive mobile learning environment that would also support deep learning using MOOCs is a challenging problem for the learning designers.
What Matters for Shoppers: Investigating Key Attributes for Online Product Comparison

Before making high-consideration purchase decisions, shoppers generally need to identify and evaluate products' key differentiating features or attributes. Many customers, however, lack the knowledge required to do so for all product domains. In this work, we investigate and analyze alternatives for identifying important product attributes, which customers can then use to compare candidate products. We propose an unsupervised attribute-ranking approach ReBARC, that combines both objective data from structured product catalogs, and subjective information from unstructured customer reviews, to suggest to the shopper the most important attributes to consider. Our detailed analysis of product attribute importance across various domains on a shopping website shows that ReBARC significantly outperforms prior efforts judged by both automated and human evaluation metrics. We also analyze the correlation and overlap between key product attributes detected by ReBARC, and those visible to customers during online product search.
A Survey on State of Enterprise Machine Learning in Pakistan

This paper presents the results of an industrial survey on evaluating the state of machine learning applications in enterprises in Pakistan by the end of 2020. It highlights the machine learning use cases in Pakistan and the challenges in its adoption in business solutions. The survey was conducted in enterprises in Pakistan ranging from companies with well-developed machine learning lifecycles to those with little or no experience in machine learning. The results of this survey have been shared in this paper along with comparisons with earlier works in this field. We believe that our results present useful recommendations for industry practitioners to enhance machine learning applications in Pakistan's enterprises.
Deep Learning Based Face Mask Detection System in OpenCV

Now a days, a life threatening disease called COVID -19 is spreading very rapidly from 2019 onwards. It also affects the respiratory system of human very badly that leads to a horrible death. This death ratio is increasing all over the world, day by day. In India, death rate has reached upto 4.03 lakhs. The World Health Organization (WHO) is suggesting the common people to follow the general instructions like wearing the mask properly at public places; sanitize the hands at regular intervals and maintain social distancing at public places. Along with this, Indian Government and other countries are initiating the immunization process and giving the awareness about the immunization. But still, people should wear the mask in common places to cut over the chain of corona virus and its new versions. In keeping this view, very few researches has done till now and there is a need to explore more researches to detect whether the people are covered their face with the mask properly or not in the common places. There are two phases in proposed system, out of which, first phase is to train the system and second phase is to apply the system to detect the faces. Keras / Tensorflow, OpenCV and MobileNetV2 have used to achieve better results and this model has been trained with the help of large dataset that includes the images of faces with and without mask. By using this architecture, the manufacturing cost of this face mask detection system is reduced to some huge values.
Anomaly Detection Via Kpis for Software Performance Failures

As cloud services have become more popular, high-reliability requirements for distributed storage systems have also increased significantly. However, the software systems often suffer performance failures during long-term running. Anomaly detection based on performance indicators is an effective approach for performance failures. To handle the complexity of the distributed databases, this paper proposes a novel anomaly detection methodology via key performance indicators monitored at different levels. Then, a variance inflation factor (VIF) feature selection method is proposed to reduce the multicollinearity problem in the multivariate time series of performance indicators. For a long-running software system, some unavoidable noise will be introduced when monitoring these performance indicators. Therefore, it is necessary to explore the suitable feature processing method to enhance features and eliminate noise interference from indicators. Discrete wavelet analysis has a superior performance in feature extraction in signal analysis. Thus, in this paper, discrete wavelet transform is modified to create an adaptive DWT for noise reduction and feature extraction by taking advantage of the multiresolution analysis. Then, unsupervised anomaly detection algorithms are used to detect the expected indicator outliers. The anomaly detection performance of the proposed method is evaluated in terms of precision, recall, and the F1-score. Finally, this approach is verified through the experiments conducted on Cassandra, and the F1-score is 0.974 which means this approach is effective.
Introducing a Dynamic Scheduling Algorithm in a Dvfs-Enabled Cloud Environment to Reduce Energy Consumption

From the perspective of users, tasks are being processing at a lower speed despite the hardware developments and increased processing potentials in today's computers. This is due to the emergence of complex operating systems and applications; however, this problem is mostly addressed through using computing technology. Cloud computing, though, has introduced some challenges, one of the most significant of which is scheduling and resource allocation. This challenge is of NP-Hard complexity class and cannot be responded at a reasonable time using linear methods. As a result of attempts made by various data centers to reduce energy consumption, some data centers consisting of processors capable of increasing and decreasing voltage and frequency are created. This makes the scheduling operation even more difficult. Accordingly, in this study, a task scheduling method capable of changing the voltage and frequency is proposed for cloud computing environments. In the proposed algorithm, task clustering and classification operations were performed in the first phase, and then the tasks were scheduled using the HEFT algorithm. In the second phase, the scheduling was optimized to shut down the low-demand processors. In the third phase, the voltage and frequency were changed. Finally, the simulations indicated that the proposed method produced better responses than similar methods.