Reviewerly/exHarmony huggingface dataset:
Machine Learning Approach for Live Migration Cost Prediction in VMware Environments

Virtualization became a commonly used technology in datacenters during the last decade. Live migration is an essential feature in most of the clusters hypervisors. Live migration process has a cost that includes the migration time, downtime, IP network overhead, CPU overhead and power consumption. This migration cost cannot be ignored, however datacenter admins do live migration without expectations about the resultant cost. Several research papers have discussed this problem, however they could not provide a practical model that can be easily implemented for cost prediction in VMware environments. In this paper, we propose a machine learning approach for live migration cost prediction in VMware environments. The proposed approach is implemented as a VMware PowerCLI script that can be easily implemented and run in any vCenter Server Cluster to do data collection of previous migrations statistics, train the machine learning models and then predict live migration cost. Testing results show how the proposed framework can predict live migration time, network throughput and power consumption cost with accurate results and for different kinds of workloads. This helps datacenters admins to have better planning for their VMware environments live migrations.
InterCriteria Analysis of oncological data of the patients for the city of Burgas

The InterCriteria Analysis approach is applied to data connected with the field of oncology.The statistical data for newly registered patients with oncological diseases for 2018 in Burgas are studied.The results are commented from different points of view: relations between gender and age of the patients, relations between gender and marital status.
Dr. BFS

The flexible architectures of Field Programmable Gate Arrays (FPGAs) lend themselves to an array of data analytical applications, among which Breadth-First Search (BFS), due to its vital importance, draws particular attention. Recent attempts that offload BFS on FPGAs either simply imitate the existing CPU- or Graphics Processing Units (GPU)- based mechanisms or suffer from scalability issues. To this end, we introduce a novel data centric design which extensively extracts the potential of FPGAs for BFS with the following two techniques. First, we advocate to partition and compress the BFS algorithmic metadata in order to buffer them in fast on-chip memory and circumvent the expensive metadata access. Second, we propose a hierarchical coalescing method to improve the throughput of graph data access. Taken together, our evaluation demonstrates that the proposed design achieves, on average, 1.6× and 2.2× speedups over the state-of-the-art FPGA designs TorusBFS and Umuroglu, respectively, across a collection of graph datasets.
Internet Utilization by the Students with Visual Impairment Disabilities

As a millennial generation, teenagers have the need to use the internet in their daily activities. The need to access the internet is also felt by students with visually impaired. Although many various sources of information are still in the form of text, with the help of various types of assistance programs, students with visually impaired can access this information. With the existence of various assistance programs, teenagers with visually impaired can access the internet more unimpeded. Based on this background, the purpose of this research is to find out the use of the internet by students with visually impaired. The results are sequentially stated that the use of the internet includes access to WhatsApp, YouTube, Google, Social Media (Facebook and Instagram), radio streaming, video downloads, music downloads, Map/location identifiers, online shopping and TV streaming to watch football broadcasts. However, there are still a number of obstacles faced by students with visually impaired who still need technological development so that the opportunity for people with visually impaired to access the internet is increasingly open. In addition, directing efforts from related institutions are needed to improve the ability of students to use the internet as well as supervision so as not to be contaminated with negative content from the internet.
Probability of traumatic situations in mechanized processes in agriculture using mathematical apparatus of Markov chain method

In this paper for exploring the mechanisms of formation of traumatic situations, the conditions and circumstances that contribute to them, and the study of the course of events leading to dangers in mechanized processes in agriculture the mathematical apparatus was used developed by the probability theory for Markov random processes with discrete states and continuous time, when the transition of a system from one state to another is possible at any unknown random time.It has been established that the processes of traumatic situations and their consequences can be represented by graph structures, using four states of the "man-machineindustrial environment" system: working and defective conditions of the tractor (machinery), hit of the machine operator into a dangerous and emergency condition (situation).A system of Kolmogorov differential equations was made, in which unknown functions are the probabilities of states of the system as functions of time with the normative condition that the sum of these probabilities will be equal to 1. Introducing the matrix of the intensity of events, the Laplace transform was used to solve the system of differential equations, which allowed the system to be transformed into a linear algebraic system.Elements of the matrix of intensities of the flows of events that bind separate states of the graph were given taking into account the probabilities of accidents with mechanics, established on the basis of the statistics of occupational injuries and data of accumulation of cracks in the array of tractor parts, were determined as a result of defectoscopic control.The results of the research allow in the medium and long term perspective to predict the probable states of the "human-machine-industrial environment" system in the risks of professional injury of agricultural machine operators.
Rethinking how healthcare is conceptualised and delivered through speculative design in the UK and Malaysia: A Comparative study

(2019). Rethinking how healthcare is conceptualised and delivered through speculative design in the UK and Malaysia: A Comparative study. The Design Journal: Vol. 22, Running with Scissors: 13th International Conference of the European Academy of Design, Dundee, 10-12th April, 2019, edited by Louise Valentine, Joanna Bletcher and Leon Cruickshank, pp. 429-444.
A First-Person Vision Dataset of Office Activities

We present a multi-subject first-person vision dataset of office activities. The dataset contains the highest number of subjects and activities compared to existing office activity datasets. Office activities include person-to-person interactions, such as chatting and handshaking, person-to-object interactions, such as using a computer or a whiteboard, as well as generic activities such as walking. The videos in the dataset present a number of challenges that, in addition to intra-class differences and inter-class similarities, include frames with illumination changes, motion blur, and lack of texture. Moreover, we present and discuss state-of-the-art features extracted from the dataset and baseline activity recognition results with a number of existing methods. The dataset is provided along with its annotation and the extracted features.
Brain Storm Algorithm Combined with Covariance Matrix Adaptation Evolution Strategy for Optimization

With the development of computational intelligence, many intelligence algorithms have attracted the attention of the scientific community, and a great deal of work on optimizing these algorithms is in full swing. One of the optimization techniques that we focus on is the hybridization of algorithms. Brain storm optimization algorithm (BSO), belonging to the swarm intelligence algorithms, is proposed by taking inspiration of human brain storming behavior. Meanwhile, the covariance matrix adaptive evolutionary strategy algorithm (CMA-ES) which belongs to the field of evolutionary strategy is also concerned. The purpose of this paper is to combine the search capability of BSO with the search efficiency of CMA-ES to achieve a relatively balanced and effective solution.
Understanding health management and safety decisions using signal processing and machine learning

Small group research in healthcare is important because it deals with interaction and decision-making processes that can help to identify and improve safer patient treatment and care. However, the number of studies is limited due to time- and resource-intensive data processing. The aim of this study was to examine the feasibility of using signal processing and machine learning techniques to understand teamwork and behaviour related to healthcare management and patient safety, and to contribute to literature and research of teamwork in healthcare. Clinical and non-clinical healthcare professionals organised into 28 teams took part in a video- and audio-recorded role-play exercise that represented a fictional healthcare system, and included the opportunity to discuss and improve healthcare management and patient safety. Group interactions were analysed using the recurrence quantification analysis (RQA; Knight et al., 2016), a signal processing method that examines stability, determinism, and complexity of group interactions. Data were benchmarked against self-reported quality of team participation and social support. Transcripts of group conversations were explored using the topic modelling approach (Blei et al., 2003), a machine learning method that helps to identify emerging themes within large corpora of qualitative data. Groups exhibited stable group interactions that were positively correlated with perceived social support, and negatively correlated with predictive behaviour. Data processing of the qualitative data revealed conversations focused on: (1) the management of patient incidents; (2) the responsibilities among team members; (3) the importance of a good internal team environment; and (4) the hospital culture. This study has shed new light on small group research using signal processing and machine learning methods. Future studies are encouraged to use these methods in the healthcare context, and to conduct further research on how the nature of group interaction and communication processes contribute to the quality of team and task decision-making.
A fuzzy logic based contrast and edge sensitive digital image watermarking technique

In this paper, a fuzzy logic based digital image watermarking technique is proposed. The contrast and edge values of the host image are analyzed by the fuzzy inference system (FIS) against fuzzy rules and then the FIS evaluates the output which is proposed as the watermarking strength (α) of the image. By varying the contrast and edge values of the host image, the fuzzy logic adjusts the watermarking strength to keep the system performance unchanged which helps to improve imperceptibility of the watermarked image. DWT is performed to divide the cover image and watermark image into sub-bands and the maximum entropy region among the sub-bands is calculated for selecting the embedding location because it is less affected by the image processing attacks. Hence, it makes the scheme more robust than other fuzzy based methods. In the extraction phase, the watermark is recovered from the sub-band where it was embedded. The effectiveness of the algorithm is measured in terms of performance parameters like peak signal to noise ratio and normalized correlation. Experimental results indicate that the fuzzy logic adjusts the watermarking strength to keep the performance parameters unchanged irrespective of the contrast and edge values of the host image.
Applying distributed ledger technology to digital evidence integrity

This paper examines the way in which blockchain technology can be used to improve the verification of integrity of evidence in digital forensics. Some background into digital forensic practices and blockchain technology are discussed to provide necessary context. A particular scalable method of verifying point-in-time existence of a piece of digital evidence, using the OpenTimestamps (OTS) service, is described, and tests are carried out to independently validate the claims made by the service. The results demonstrate that the OTS service is highly reliable with a zero false positive and false negative error rate for timestamp attestations, but that it is not suitable for timesensitive timestamping due to the variance of the accuracy of timestamps induced by block confirmation times in the Bitcoin blockchain.
Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors

Word embedding models typically learn two types of vectors: target word vectors and context word vectors.These vectors are normally learned such that they are predictive of some word co-occurrence statistic, but they are otherwise unconstrained.However, the words from a given language can be organized in various natural groupings, such as syntactic word classes (e.g.nouns, adjectives, verbs) and semantic themes (e.g.sports, politics, sentiment).Our hypothesis in this paper is that embedding models can be improved by explicitly imposing a cluster structure on the set of context word vectors.To this end, our model relies on the assumption that context word vectors are drawn from a mixture of von Mises-Fisher (vMF) distributions, where the parameters of this mixture distribution are jointly optimized with the word vectors.We show that this results in word vectors which are qualitatively different from those obtained with existing word embedding models.We furthermore show that our embedding model can also be used to learn high-quality document representations.
Status, identity, and language: A study of issue discussions in GitHub

Successful open source software (OSS) projects comprise freely observable, task-oriented social networks with hundreds or thousands of participants and large amounts of (textual and technical) discussion. The sheer volume of interactions and participants makes it challenging for participants to find relevant tasks, discussions and people. Tagging (e.g., @AmySmith) is a socio-technical practice that enables more focused discussion. By tagging important and relevant people, discussions can be advanced more effectively. However, for all but a few insiders, it can be difficult to identify important and/or relevant people. In this paper we study tagging in OSS projects from a socio-linguistics perspective. First we argue that textual content per se reveals a great deal about the status and identity of who is speaking and who is being addressed. Next, we suggest that this phenomenon can be usefully modeled using modern deep-learning methods. Finally, we illustrate the value of these approaches with tools that could assist people to find the important and relevant people for a discussion.
Towards a comprehensive understanding of the context concepts in context-aware business processes

Recent studies explore context-awareness as a new paradigm and principle in BPM (Business Process Management). However, there is still a lack of consensus on the meaning of context related concepts in the BPM area. While most people tacitly understand what context is and use context to optimize business processes, they find it hard to define. Researchers commonly provide their own definitions according to their own background and expertise, which leads to diversity and inconsistency.
Bayes Theorem and Forward Chaining Method On Expert System for Determine Hypercholesterolemia Drugs

The development of technology capable to imitating the process of human thinking and led to a new branch of computer science named the expert system. One of the problem that can be solved by an expert system is selecting hypercholesterolemia drugs. Drug selection starts from find the symptoms and then determine the best drug for the patient. This is consist with the mechanism of forward chaining which starts from searching for information about the symptoms, and then try to illustrate the conclusions. To accommodate the missing fact, expert systems can be complemented with the Bayes theorem that provides a simple rule for calculating the conditional probability so the accuracy of the method approaches the accuracy of the experts. This reseacrh uses 30 training data and 76 testing data of medical record that use hypercholesterolemia drugs from Tugurejo Hospital of Semarang. The variable are common symptoms and some hypercholesterolemia drugs. This research obtained a selection of hypercholesterolemia drugs system with 96.05% accuracy
Sorting Large Data Sets with FPGA-Accelerated Samplesort

Sorting is a fundamental operation in many applications such as databases, search, and social networks. Although FPGAs have been shown effective at sorting data sizes that fit on chip, systems that sort larger data sets by shuffling data on and off chip are typically bottlenecked by costly merge operations or data transfer time. We propose a new approach to sorting large data sets by accelerating the samplesort algorithm using a server with a PCIe-connected FPGA. Samplesort works by randomly sampling to determine how to partition data into approximately equal-sized non-overlapping "buckets," sorting each bucket, and concatenating the results. Although samplesort can partition a large problem into smaller ones that fit in the FPGA's on-chip memory, partitioning in software is slow. Our system uses a novel parallel hardware partitioner that is only limited in data set size by available FPGA hardware resources. After partitioning, each bucket is sorted using parallel sorting hardware. The CPU is responsible for sampling data, cleaning up any potential problems caused by variation in bucket size, and providing scalability by performing an initial coarse-grained partitioning when the input set is larger than the FPGA can sort. We prototype our design using Amazon Web Services FPGA instances, which pair a Xilinx Virtex UltraScale+ FPGA with a high-performance server. Our experiments demonstrate a 17.1x speedup over GNU parallel sort when sorting 2^23 key-value records and a speedup of 4.2x when sorting 2^30 records.
Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information

Automatic commenting of online articles can provide additional opinions and facts to the reader, which improves user experience and engagement on social media platforms.Previous work focuses on automatic commenting based solely on textual content.However, in real-scenarios, online articles usually contain multiple modal contents.For instance, graphic news contains plenty of images in addition to text.Contents other than text are also vital because they are not only more attractive to the reader but also may provide critical information.To remedy this, we propose a new task: cross-model automatic commenting (CMAC), which aims to make comments by integrating multiple modal contents.We construct a largescale dataset for this task and explore several representative methods.Going a step further, an effective co-attention model is presented to capture the dependency between textual and visual information.Evaluation results show that our proposed model can achieve better performance than competitive baselines.
Improved Sentiment Detection via Label Transfer from Monolingual to Synthetic Code-Switched Text

Multilingual writers and speakers often alternate between two languages in a single discourse, a practice called "code-switching".Existing sentiment detection methods are usually trained on sentiment-labeled monolingual text.Manually labeled code-switched text, especially involving minority languages, is extremely rare.Consequently, the best monolingual methods perform relatively poorly on code-switched text.We present an effective technique for synthesizing labeled code-switched text from labeled monolingual text, which is more readily available.The idea is to replace carefully selected subtrees of constituency parses of sentences in the resource-rich language with suitable token spans selected from automatic translations to the resource-poor language.By augmenting scarce human-labeled code-switched text with plentiful synthetic code-switched text, we achieve significant improvements in sentiment labeling accuracy (1.5%, 5.11%, 7.20%) for three different language pairs (English-Hindi, English-Spanish and English-Bengali).We also get significant gains for hate speech detection: 4% improvement using only synthetic text and 6% if augmented with real text.
Question Answering in the Biomedical Domain

Question answering techniques have mainly been investigated in open domains.However, there are particular challenges in extending these open-domain techniques to extend into the biomedical domain.Question answering focusing on patients is less studied.We find that there are some challenges in patient question answering such as limited annotated data, lexical gap and quality of answer spans.We aim to address some of these gaps by extending and developing upon the literature to design a question answering system that can decide on the most appropriate answers for patients attempting to self-diagnose while including the ability to abstain from answering when confidence is low.
Fast GPU-Based Enhanced Wiener Filter for Despeckling SAR Data

Speckle noise is presented as an inherent dilemma that affects the image processing field, and in particular synthetic aperture radar images. In order to mitigate the adverse effects caused by this phenomenon, several approaches have been introduced in the scientific community during the last three decades including spatial-based and non-local filtering approaches. However, these proposed techniques suffer from some limitations. In fact, it is very difficult to find an approach that is able, on the one hand, to perform well in terms of noise reduction and image detail preservation and, on the other hand, provide a filtering output solution without high computational complexity and within a short processing time. In this paper, we aim to evaluate the performance of a newly-developed despeckling algorithm, presented as an enhancement of the classical Wiener filter and properly designed to work with a Graphics Processing Unit (GPU). The algorithm is tested on both a simulated framework and real Sentinel-1 SAR data. The results, obtained in comparison with other filters, are interesting and promising. Indeed, the proposed method turns out to be a useful filtering instrument in the case of large images by performing the processing within a limited time and ensuring good speckle noise reduction with a considerable image detail preservation.
CESSNA Citation X Engine Model Identification using Neural Networks and Extended Great Deluge Algorithms

Accurate numerical engine model is an enabling factor in aircraft performance evaluation and improvement.In this work, nonlinear engine input-output relationships are learned and predicted by two cascading multilayer feedforward neural networks.Machine learning approaches necessitate a great amount of data to achieve efficiency.To satisfy this operational requirement, 441,000 flight cases are designed for a Cessna Citation X turbofan engine using a Level D Research Aircraft Flight Simulator designed and manufactured by CAE Inc.For each flight case, cruise phase data comprising Mach number, altitude, throttle level angle, low-pressure compressor speed, high-pressure compressor speed, engine net thrust and engine fuel flow are recorded.These data are then organized into subsets for training and validation purposes.Each neural network configuration is obtained by means of the Extended Great Deluge algorithm.The latter is also responsible for coordinating neural network training and learning error computation.Analyses based on computer experiments showed a mean relative prediction error upper bound of 4% is achievable for engine output parameters for all cruise phase flight cases.