Reviewerly/exHarmony huggingface dataset:
Genetic Expression Programming Based Dynamic Virtual Machine Consolidation in Cloud Computing

Increasing demands in calculation accelerate the development of cloud computing, building larger-scale data centers are essential. However, high energy consumption accompanied by data centers should be given attention. The main solution so far is VM consolidation, which uses dynamic migration of Virtual Machines (VMs) to close under-loaded Physical Machines(PMs) to reduce the energy consumption. In this paper, we employ Genetic Expression Programming (GEP) for deriving regression model of migrate consideration according to historical data. Then we carry out dynamic VM migrations from overloaded hosts and under-loaded hosts in consideration of VMs' migrate consideration that gotten by this regression model. Experimental results on real workload traces show that VM consolidation based on migration consideration cuts down overall energy consumption in cloud data centers. In addition, it can outperform some existing VM consolidation approaches in terms of SLA, number of VM migrations, and Performance Degradation due to Migrations (PDM).
A Circular ‘Smart’ World

In the fast-growing Internet of Things (IoT) industry, algorithmic technology promises ‘smart’ solutions to everyday problems. Drawing on a design researchResearch investigation, this chapter questions and critically examines the embedded epistemologyEpistemology of IoT, in the context of what I call the algorithmic paradigm. This examination reconsiders the prevailing epistemology Epistemology and offers an alternative based on a second-order cybernetics perspective. This alternative recognises the importance of accounting for the role/agency of the observer/designer/user, the circular causality of user behaviour and technology, and the relationality of ‘smartness’. To explore the possibility of a shift in perspective from the current algorithmic paradigm to a second-order one, users are approached as experiential, non-linear subjects rather than as probabilistic and linear ones. Outcomes reveal the value of second-order cybernetics Second-order cybernetics (SoC) as an epistemological Epistemology stance and a practical approach to research Research on the design of ‘smart’ interactions. The methodological framework demonstrates how design research Research and second-order considerations can work together, asking novel questions to inform disciplines with an interest in IoT interactions Interaction , from both a design perspective (the way designers Designers approach their practice) and in terms of broader implications for society.
Pattern Selection for Optimal Classical Planning with Saturated Cost Partitioning

Pattern databases are the foundation of some of the strongest admissible heuristics for optimal classical planning. Experiments showed that the most informative way of combining information from multiple pattern databases is to use saturated cost partitioning. Previous work selected patterns and computed saturated cost partitionings over the resulting pattern database heuristics in two separate steps. We introduce a new method that uses saturated cost partitioning to select patterns and show that it outperforms all existing pattern selection algorithms.
Analyzing Windows Subsystem for Linux Metadata to Detect Timestamp Forgery

Timestamp patterns assist forensic analysts in detecting user activities, especially operations performed on files and folders. However, the Windows Subsystem for Linux feature in Windows 10 versions 1607 and later enables users to access and manipulate NTFS files using Linux command-line tools within the Bash shell. Therefore, forensic analysts should consider the timestamp patterns generated by file operations performed using Windows command-line utilities and Linux tools within the Bash shell. This chapter describes the identification of timestamp patterns of various file operations in stand-alone NTFS and Ext4 filesystems as well as file interactions between the filesystems. Experiments are performed to analyze the anti-forensic capabilities of file timestamp changing utilities – called timestomping tools – on NTFS and Ext4 filesystems. The forensic implications of timestamp patterns and timestomping are also discussed.
The upper bound function of nonadiabatic dynamics in parametric driving quantum systems*

The adiabatic control is a powerful technique for many practical applications in quantum state engineering, light-driven chemical reactions and geometrical quantum computations. This paper reveals a speed limit of nonadiabatic transition in a general time-dependent parametric quantum system that leads to an upper bound function which lays down an optimal criteria for the adiabatic controls. The upper bound function of transition rate between instantaneous eigenstates of a time-dependent system is determined by the power fluctuations of the system relative to the minimum gap between the instantaneous levels. In a parametric Hilbert space, the driving power corresponds to the quantum work done by the parametric force multiplying the parametric velocity along the parametric driving path. The general two-state time-dependent models are investigated as examples to calculate the bound functions in some general driving schemes with one and two driving parameters. The calculations show that the upper bound function provides a tighter real-time estimation of nonadiabatic transition and is closely dependent on the driving frequencies and the energy gap of the system. The deviations of the real phase from Berry phase on different closed paths are induced by the nonadiabatic transitions and can be efficiently controlled by the upper bound functions. When the upper bound is adiabatically controlled, the Berry phases of the electronic spin exhibit nonlinear step-like behaviors and it is closely related to topological structures of the complicated parametric paths on Bloch sphere.
2M-SELAR: A Model for Mining Sequential Least Association Rules

Recently, mining least association rule from the sequential data becomes more important in certain domain areas such as education, healthcare, text mining, etc. due to its uniqueness and usefulness. However, discovering such rule is a great challenge because it involves with a set of least items which usually holds a very low in term of support. Therefore, in this paper propose a model for mining sequential least association rule (2M-SELAR) that embedded with SELAR algorithm, and Critical Relative Support (CRS) and Definite Factor (DF) measures. The experimental results reveal that 2M-SELAR can successfully generate the desired rule from the given datasets.
Complete and Near-Optimal Path Planning for Simultaneous Sensor-Based Inspection and Footprint Coverage in Robotic Crack Filling

A simultaneous robotic footprint and sensor coverage planning scheme is proposed to efficiently detect all the unknown targets with range sensors and cover the targets with the robot's footprint in a structured environment. The proposed online Sensor-based Complete Coverage (online SCC) planning minimizes the total traveling distance of the robot, guarantees the complete sensor coverage of the whole free space, and achieves near-optimal footprint coverage of all the targets. The planning strategy is applied to a crack-filling robotic prototype to detect and fill all the unknown cracks on ground surfaces. Simulation and experimental results are presented that confirm the efficiency and effectiveness of the proposed online planning algorithm.
Training Transfer of Bimanual Assembly Tasks in Cost-Differentiated Virtual Reality Systems

Recent advances of the affordable virtual reality headsets make virtual reality training an economical choice when compared to traditional training. However, these virtual reality devices present a range of different levels of virtual reality fidelity and interactions. Few works have evaluated their validity against the traditional training formats. This paper presents a study that compares the learning efficiency of a bimanual gearbox assembly task among traditional training, virtual reality training with direct 3D inputs (HTC VIVE), and virtual reality training without 3D inputs (Google Cardboard). A pilot study was conducted and the result shows that HTC VIVE brings the best learning outcomes.
Meal Information Recognition Based on Smart Tableware Using Multiple Instance Learning

In recent years, people are paying more and more attention to the management of healthy meals because of the influence of lifestyle-related diseases, and some diet management systems are trying to help people lead a healthy life. Moreover, some studies have found that the proper meal habits can play a role in preventing disease to some extent. This paper introduces the smart tableware consisting of an acceleration sensor and a pressure sensor, which is used to obtain information about the sequence and content of meals. In addition, a method of analyzing and processing meal information through Multiple Instance Learning (MIL) is proposed to help people prevent diseases that are affected by lifestyle habits. At the same time, the acquisition process of MIL dataset is introduced and Support Vector Machine (SVM) is used. The performance evaluation results show that good results can be achieved by using MIL.
Neural Network Modeling of a Drying Process in Radio Frequency Field

In this study, a neural network model was developed and tested to predict the characteristics of a possible industrial wood processing based on electromagnetic technologies. The application presented in this paper is a drying process for a half-finished oak blanks exposed in a radiofrequency electromagnetic field. To develop the model of this process, MATLAB's Neural network toolbox was used. The drying process was simulated with the help of FEM-BEM.3D-RFmove _term_masa software and the simulation results were used then for the training of the proposed neural network. The trained neural network is effective to provide the process characteristics with good accuracy.
A Text Annotation Tool with Pre-annotation Based on Deep Learning

In this paper, we introduce an open-source tool, YEDDA, supported by a pre-annotation module based deep learning. EPAD proposes a novel annotation workflow, combining pre-annotation and manual annotation, which improves the efficiency and quality of annotation. The pre-annotation module can effectively reduce the annotation time, and meanwhile improve the precision and recall of annotation. EPAD also contains some of the mechanisms to facilitate the usage of the pre-annotation module. As a collaborative design, EPAD provides administrators with annotation statistics and analysis functions. Experiments showed that EPAD shortened almost 60.0$$\%$$ of the total annotation time, and improved 12.7$$\%$$ of F-measure for annotation quality.
SQL-Identifier Injection Attacks

This paper defines a class of SQL-injection attacks that are based on injecting identifiers, such as table and column names, into SQL statements. An automated analysis of GitHub shows that 15.7% of 120,412 posted Java source files contain code vulnerable to SQL-Identifier Injection Attacks (SQL-IDIAs). We have manually verified that some of the 18,939 Java files identified during the automated analysis are indeed vulnerable to SQL-ID IAs, including deployed Electronic Medical Record software for which SQL-IDIAs enable discovery of confidential patient information. Although prepared statements are the standard defense against SQL injection attacks, existing prepared-statement APIs do not protect against SQL-IDIAs. This paper therefore proposes and evaluates an extended prepared-statement API to protect against SQL-IDIAs.
Sentiment Analysis with CNNs Built on LSTM on Tourists Comments

This research developed a sentiment analysis system for customers' comments on a scenic spot. It is based on CNNs built on LSTM for text feature extraction under the deep learning framework. The CNNs built on LSTM model applies convolutional filters of CNNs repeatedly operate on the output matrix of LSTM to obtain robust text feature vector. In the experiments, the optimal parameter configurations for each component of CNNs and LSTM are identified separately in the first place. Then, the entire optimal parameter configuration for the integration recognition frame of the system is identified around the optimum of each component. Experimental results demonstrate that the accuracy for sentiment analysis with CNNs built on LSTM model is improved by 3.13% and 1.71% respectively, compared with a single CNNs or LSTM model.
Short-Liveness of Error Propagation in Kernel Can Improve Operating Systems Availability

The reliability of operating systems is crucial to achieving high availability of computer systems. Unfortunately, Linux, a widely used operating system, is far from bug-free. Some recent studies point out error propagation is very short in the kernel and thus most data in the kernel are not corrupt even when a failure occurs. This paper explores the possibility of exploiting the property of "short-liveness" of error propagation in the kernel to improve the operating system availability. Our novel design of the memory management scheme allows us to recover the kernel by removing inconsistent data structures corrupted during error propagations.
Optimizing selection using Pearson correlation coefficient: A case study in movie industry

Selecting products available in the market is a very tedious process, thus a need to employed computing techniques to recommend something/products for the user is very important. The study focuses in developing a system that recommend movies with the used of three algorithms: content-based filtering, collaborative filtering and Pearson correlation coefficient. The system is a web-based platform that recommends movie titles based on attributes such as online queries, item description, user borrowing and movie historical data and user preferences. The implementation is successful and the combination of the implemented algorithms is not limited to providing movie recommendations alone, but can also be applied on different platforms e-learning, eCommerce, social media, news article websites, product selection and others.
Using OntoLex-Lemon for Representing and Interlinking German Multiword Expressions in OdeNet and MMORPH

We describe work consisting in porting two large German lexical resources into the OntoLex-Lemon model in order to establish complementary interlinkings between them.One resource is OdeNet (Open German Word-Net) and the other is a further development of the German version of the MMORPH morphological analyzer.We show how the Multiword Expressions (MWEs) contained in OdeNet can be morphologically specified by the use of the lexical representation and linking features of OntoLex-Lemon, which also support the formulation of restrictions in the usage of such expressions.
Development of an Android Recognition Open-Loop Tracking Control of Writing Robotic Arm

Writing is one of the medium of the manifestations of the expression which is staged by going through many evolutionary phases for the time being.And most importantly robots and technology are dramatically meliorated in the 21st century.So, robotic writing hand was introduced with the collaboration with human brain and technology.The whole methodology is staged for the development of a low-cost robotic arm able to draw in an arbitrary 2-D plane through an android application.The Android app will take drawing inputs on its Android screen and the data bits are transferred to the robot through wireless standard IEEE 802.15.1 communication technology.The human friendly robotic arm uses the polar coordinate system instead of the Cartesian coordinate system for determining the end-effector position.The modified slider model with the rope-pulley mechanism makes the robot compact.The description of the mechanical and electrical design along with the practical and simulation results are presented in this paper.
Enhancing Availability of Traffic-Aware Virtual Cluster Allocation in Cloud Datacenters

As more and more services are deployed in the cloud datacenter, network traffic is growing exponentially. Virtual machines (VMs) of a virtual cluster (VC) must be allocated on physical machines (PMs) in the datacenter with a certain topology. Each VM need some resources to run various services. Apparently, allocating VMs in a VC as compactly as possible can reduce traffic consumption and avoid bandwidth-related bottlenecks. However, loose allocation scheme can reduce the loss expectation of VMs due to failure possibilities of PMs and switches, and the availability of the VC is increased thereby. To enhance availability and reduce network bandwidth usage, it is significant to determine the scheme of allocating VMs of the VC. In this paper, we first introduce four typical datacenter architectures with network topologies and corresponding cost matrices, and extend to generality. Then we propose a joint optimization function to measure risk of VC and core bandwidth usage with a global availability constraint. Subsequently, an evolution algorithm is raised to minimize the value of the constrained optimization function. Finally, the evaluation results show the effectiveness of the proposed approach and performance advancement over the existing approaches.
Unsupervised Compositional Translation of Multiword Expressions

This article describes a dependency-based strategy that uses compositional distributional semantics and cross-lingual word embeddings to translate multiword expressions (MWEs).Our unsupervised approach performs translation as a process of word contextualization by taking into account lexico-syntactic contexts and selectional preferences.This strategy is suited to translate phraseological combinations and phrases whose constituent words are lexically restricted by each other.Several experiments in adjective-noun and verb-object compounds show that mutual contextualization (co-compositionality) clearly outperforms other compositional methods.The paper also contributes with a new freely available dataset of English-Spanish MWEs used to validate the proposed compositional strategy.
MSIT_SRIB at MEDIQA 2019: Knowledge Directed Multi-task Framework for Natural Language Inference in Clinical Domain.

In this paper, we present Biomedical Multi-Task Deep Neural Network (Bio-MTDNN) on the NLI task of MediQA 2019 challenge (Ben Abacha et al., 2019).Bio-MTDNN utilizes "transfer learning" based paradigm where not only the source and target domains are different but also the source and target tasks are varied, although related.Further, Bio-MTDNN integrates knowledge from external sources such as clinical databases (UMLS) enhancing its performance on the clinical domain.Our proposed method outperformed the official baseline and other prior models (such as ESIM and Infersent on dev set) by a considerable margin as evident from our experimental results.
High-Fidelity Hyperentangled Cluster States of Two-Photon Systems and Their Applications

An efficient scheme is proposed in this study to prepare four symmetric hyperentangled cluster states in the polarization degrees of freedom (DOF) and spatial DOF with a two-photon system. This system consists of two nitrogen-vacancy (NV) centers which are coupled to two microtoroidal resonators. The two-photon polarization-spatial hyperentangled cluster states can be generated with our system by virtue of the input and output process. Compared with previous works, our quantum circuit for preparing the hyperentangled cluster states is simple and economic. Moreover, our scheme works deterministically and does not need any extra qubits, making it applicable to existing technologies. Our calculations show that our scheme has high fidelity with current technology, which can help hyperentangled cluster states to play a very useful role in quantum communication networks with long distances and high capacity.
DUT-BIM at MEDIQA 2019: Utilizing Transformer Network and Medical Domain-Specific Contextualized Representations for Question Answering

In medical domain, given a medical question, it is difficult to manually select the most relevant information from a large number of search results. BioNLP 2019 proposes Question Answering (QA) task, which encourages the use of text mining technology to automatically judge whether a search result is an answer to the medical question. The main challenge of QA task is how to mine the semantic relation between question and answer. We propose BioBERT Transformer model to tackle this challenge, which applies Transformers to extract semantic relation between different words in questions and answers. Furthermore, BioBERT is utilized to encode medical domain-specific contextualized word representations. Our method has reached the accuracy of 76.24% and spearman of 17.12% on the BioNLP 2019 QA task.
Adaptive Prediction of Spam Emails : Using Bayesian Inference

As the recent advancement in communication technologies revolutionizes the world, Email is emerging as a wide-known communication paradigm in various business processes. Email is an effective, brisk and minimal effort correspondence approach. Email Spam is non-asked for information sent to the E-letter drops. Spam could be an enormous downside each for clients and for ISPs. As per examination these days client gets a considerable measure of spam messages then non-spam messages. In some cases spam messages may damage the reputation of a particular business process. It can be observed that in most of the popular mailing services the spam filters are being biased for the profit from the ads, i.e. they are allowing some exception for some companies that pay for advertising. This is not an ethical practice, but it is very profitable for their future business processes. Our aim is to build a spam detector using machine learning in python with the packages NLTK, Matplotlib, Word cloud, Math, pandas, NumPy. With this proposed model, we can state a specified message as spam or non-spam. It can be implemented by using Bayes’ Theorem, a simple yet powerful theorem.
FaRE: Open Source Face Recognition Performance Evaluation Package

Biometrics-related research has been accelerated significantly by deep learning technology. However, there are limited open-source resources to help researchers evaluate their deep learning-based biometrics algorithms efficiently, especially for the face recognition tasks. In this work, we design, implement, and evaluate a computationally lightweight, maintainable, scalable, generalizable, and extendable face recognition evaluation toolbox named FaRE that supports both online and offline evaluation to provide feedback to algorithm development and accelerate biometricsrelated research. FaRE includes a set of evaluation metrics and provides various APIs for commonly-used face recognition datasets including LFW, CFP, UHDB31, and IJBseries datasets. FaRE can be easily extended to include other datasets. The package is publically available for research use at https://github.com/uh-cbl/FaRE.
Methods of Software Self-adaptation Based on Monitoring the Information Environment

The paper is devoted to the problem of developing a universal self-adaptation method of software systems. The main difference of proposed method is that a system extracts information about preferred structural and behavioral changes from a user feedback received in the form of reports and reviews. A semantic network of program characteristics will be built on the basis of this feedback information. This network will determine a new system configuration. For the synthesis of a semantic network, it is proposed to use methods of latent-semantic and distributive-statistical analysis. The feature diagram (the graphical representation of a feature model) will be used as a variability model of self-adaptive software. The developed method will allow a software system to independently identify and correct its own objective shortcomings directly in the process of functioning, as well as to adjust itself to different categories of users. The study was carried out with the financial support of the Russian Foundation for Basic Research in the framework of the research project No. 18-07-00408.