Reviewerly/exHarmony huggingface dataset:
Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text

Although temporal tagging is still dominated by rule-based systems, there have been recent attempts at neural temporal taggers.However, all of them focus on monolingual settings.In this paper, we explore multilingual methods for the extraction of temporal expressions from text and investigate adversarial training for aligning embedding spaces to one common space.With this, we create a single multilingual model that can also be transferred to unseen languages and set the new state of the art in those cross-lingual transfer experiments.
Advanced sensing solutions for ambient assisted living: The NATIFLife framework

The term assistive technology identifies a wide range of solutions and services to improve the life quality of frail people, by supporting them in performing everyday tasks, accessing public services, participating in education and civic life, as well as performing well at their job [1]-[3]. Potential users of assistive technology are elders, sometimes with disabilities, diseases or mental health conditions. To highlight the crucial role of assistive technologies, it should be considered that by 2050, one out of six people in the world will be over the age of 65, almost doubled as respect to the current situation [1]-[3]. In general, it can be affirmed that frail people may incur difficulties while performing Activities of Daily Living (ADLs), such as eating, drinking, mobility, housework, use of home appliances, social interactions and many other everyday activities.
Transliteration for Cross-Lingual Morphological Inflection

Cross-lingual transfer between typologically related languages has been proven successful for the task of morphological inflection.However, if the languages do not share the same script, current methods yield more modest improvements.We explore the use of transliteration between related languages, as well as grapheme-to-phoneme conversion, as data preprocessing methods in order to alleviate this issue.We experimented with several diverse language pairs, finding that in most cases transliterating the transfer language data into the target one leads to accuracy improvements, even up to 9 percentage points.Converting both languages into a shared space like the International Phonetic Alphabet or the Latin alphabet is also beneficial, leading to improvements of up to 16 percentage points. 1
Pengaruh Model Pembelajaran SFAE Berbasis Tri Kaya Parisudha Terhadap Hasil Belajar Matematika Siswa

This study aims to determine the difference of the students’ mathematics achievementbetween the students who are taught with student facilitator and explaining (SFAE) learning model based on Tri Kaya Parisudhaand those are taught with conventional learning in third-grade students of Gugus IV Gerokgak, Bulelengregency in academic year 2017/2018. The design of this study was quasi experimentalwith posttest only control group design. The population of this study was all the third-grade students of Gugus IV, Gerokgak which were 186 students. The sample of this study was the third-grade students of SD Negeri 2 Gerokgak with 34 students as experimental group and the third-grade students of SD Negeri 3 Gerokgak with 40 students as control group. Data collection was obtained by using objective test method. The data were analyzed by using T-test. The result of this study wasthitung = 7,38 with the significant level 55 and dk = 72 with ttabel= 1.99 so thitung = 7,38&gt;ttabel = 1,99. Based on the test criteria, then H0 was rejected and Ha was accepted. The average score of mathematics learning achievement in the experimental group was 14.71, while the average score for the control group was 11.2. Based on the results above, it can be concluded that there was a significant effect toward the students’ mathematics achievementbetween the students who are taught with Student Facilitator and Explaining (SFAE) learning model based on Tri Kaya Parisudha and those are taught with conventional learning in third grade students of Gugus IV Gerokgak, Bulelengregency in academic year 2017/2018.
Load Balancing Optimization Based on Deep Learning Approach in Cloud Environment

Load balancing is a significant aspect of cloud computing which is essential for identical load sharing among resources like servers, network interfaces, hard drives (storage) and virtual machines (VMs) hosted on physical servers.In cloud computing, Deep Learning (DL) techniques can be used to achieve QoS such as improve resource utilization and throughput; while reduce latency, response time and cost, balancing load across machines, thus, increasing the system reliability.DL results in effective and accurate decision making of intelligent resource allocation to the incoming requests, thereby, choosing the most suitable resource to complete them.However, in previous researches on load balancing, there is limited application of DL approaches.In this paper, the significance of DL approaches have been analysed in the area of cloud computing.A Framework for Workflow execution in cloud environment has been proposed and implemented, namely, Deep Learningbased Deadline-constrained, Dynamic VM Provisioning and Load Balancing (DLD-PLB).Optimal schedule for VMs has been generated using Deep Learning based technique.The Genome workflow tasks have been taken as input to the suggested framework.The results for makespan and cost has been computed for the proposed framework and has been compared with our earlier proposed framework for load balancing optimization -Hybrid approach based Deadline-constrained, Dynamic VM Provisioning and Load Balancing (HDD-PLB)" framework for Workflow execution.The earlier proposed approaches for load balancing were based on hybrid Predict-Earliest-Finish Time (PEFT) with ACO for underutilized VM optimization and hybrid PEFT-Bat approach for optimize the utilization of overflow VMs.
Unsupervised Machine Learning for the Discovery of Latent Clusters in COVID-19 Patients Using Electronic Health Records.

The goal of this paper was to apply unsupervised machine learning techniques towards the discovery of latent clusters in COVID-19 patients. Over 6,000 adult patients tested positive for the SARS-CoV-2 infection at the Mount Sinai Health System in New York, USA met the inclusion criteria for analysis. Patients' diagnoses were mapped onto chronicity and one of the 18 body systems, and the optimal number of clusters was determined using K-means algorithm and the elbow method. 4 clusters were identified; the most frequently associated comorbidities involved infectious, respiratory, cardiovascular, endocrine, and genitourinary disorders, as well as socioeconomic factors that influence health status and contact with health services. These results offer a strong direction for future research and more granular analysis.
Novelty search for automatic bug repair

Genetic Improvement (GI) focuses on the development of evolutionary methods to automate software engineering tasks, such as performance improvement or software bugs removal. Concerning the latter, one of the earliest and most well-known methods in this area is the Genetic Program Repair (GenProg), a variant of Genetic Programming (GP). However, most GI systems encounter problems that are derived from the fact that they operate directly at source code level. These problems include highly neutral fitness landscapes and loss of diversity during the search, which are always undesirable in search and optimization tasks. This paper explores the use of Novelty Search (NS) with GenProg, since it can allow a search process to overcome these type of issues. While NS has been combined with GP before, and recently used with other GI systems, in the area of automatic bug repair NS has not been used until this work. Results show that GenProg with NS outperforms the original algorithm in some cases, based on an extensive experimental evaluation.
Impact of Historical Software Metric Changes in Predicting Future Maintainability Trends in Open-Source Software Development

Software maintenance is one of the key stages in the software lifecycle and it includes a variety of activities that consume the significant portion of the costs of a software project. Previous research suggest that future software maintainability can be predicted, based on various source code aspects, but most of the research focuses on the prediction based on the present state of the code and ignores its history. While taking the history into account in software maintainability prediction seems intuitive, the research empirically testing this has not been done, and is the main goal of this paper. This paper empirically evaluates the contribution of historical measurements of the Chidamber &amp; Kemerer (C&amp;K) software metrics to software maintainability prediction models. The main contribution of the paper is the building of the prediction models with classification and regression trees and random forest learners in iterations by adding historical measurement data extracted from previous releases gradually. The maintainability prediction models were built based on software metric measurements obtained from real-world open-source software projects. The analysis of the results show that an additional amount of historical metric measurements contributes to the maintainability prediction. Additionally, the study evaluates the contribution of individual C&amp;K software metrics on the performance of maintainability prediction models.
Facing Cold-Start: A Live TV Recommender System Based on Neural Networks

With the increase in the number of live TV channels, audiences must spend increasing amounts of time and energy deciding which shows to watch; this problem is called information overload, and recommender systems (RSs) are effective methods for addressing such problems. Due to the high update rates and low replay rates of TV programs, the item cold-start problem is prominent, and this problem seriously affects the effectiveness of the recommender and limits the application of recommendation algorithms for live TV. To solve this problem better, RSs must consider information in addition to the time slot strategy, which relies on experience. At present, no methods make good use of viewing behavior records. Therefore, in this paper, we proposed a viewing environment model called DeepTV that considers viewing behavior records and electronic program guides and includes a feature generation process and a model construction process. In the feature generation process, we defined seven key features by clustering viewing time, distinguishing positive and negative feedback, capturing continuous viewing preference and introducing the remaining time proportion of candidate programs. We normalize the continuous features and add powers of them. In the model construction process, we regard the live TV recommendation task as a classification problem and fuse the above features by using a neural network. Finally, experiments on industrial datasets show that the proposed model significantly outperforms baseline algorithms.
Top-Down Human-Cyber-Physical Data Fusion Based on Reinforcement Learning

With the development of industrial Internet and artificial intelligence, data fusion in cross-domains and cross-layers have become an inevitable trend. Most of the data fusion involved in the production process of hot rolling are concentrated on the level of sensors, Internet of Things (IoT) and the Internet; but human data are not well integrated. In order to avoid the human factor from becoming the bottleneck of the entire production schedule, this paper proposes a ternary data fusion model based on reinforcement learning algorithm. The related data source from human-cyber-physical space includes: social network, Internet and IoT. By merging the ternary data, a variety of data (including humans’) can be quickly calculated to obtain better and faster decisions. In order to achieve automated fusion from ternary data, this paper proposes a method based on reinforcement learning: firstly, the domain ontology used for associating ternary data is reduced and tessellated (dimension reduction), and then the reinforcement learning model is used to form “the new ontology”. Compared with resource-intensive global calculations (which may cost a few days), the new method can complete the calculations in minutes. This means that the new method optimizes the data source required for decision-making and improves the efficiency. Finally, the production scheduling of hot rolled steel is used as an example to verify the feasibility of the proposed method.
Light Field Image Coding with Image Prediction in Redundancy

The proposed work involves a hybrid data representation using efficient light field coding. The existing light field coding solution are implemented using sub-aperture or micro-images. However, the full capacity in terms of intrinsic redundancy in light field images is not completely explored. This paper represents a hybrid data representation which explores four major redundancy types. Using coding block, the most predominant redundancy is exploited to find the optimum coding solution that provides maximum flexibility. To show how efficient the hybrid representation works, we have proposed a combination of pseudo-video sequence coding approach with pixel prediction methods. The observed experimental results shows a positive bit rate saving when compared to other similar methods. Similarly, the proposed method is also said to outperform other coding algorithms such as WaSP and MuLE when compared on a HEVC-based benchmark.
Cybercrime is (often) boring: maintaining the infrastructure of cybercrime economies

It is generally accepted that the widespread availability of specialist services has helped drive the growth of cybercrime in the past fifteen to twenty years. Individuals and groups involved in cybercrime no longer need to build their own botnet or send their own spam because they can pay others to do these things. What has seldom been remarked upon is the amount of tedious administrative and maintenance work put in by these specialist suppliers. There is much discussion of the technically sophisticated work of developing new strains of malware or identifying zero-day exploits but the mundane nature of the day to day tasks of operating infrastructure has been almost entirely overlooked. Running bulletproof hosting services, herding botnets, or scanning for reflectors to use in a denial of service attack is unglamorous and tedious work, and is little different in character from the activity of legitimate sysadmins. We provide three case studies of specialist services that underpin illicit economies and map out their characteristics using qualitative sociological research involving interviews with infrastructure providers and scraped data from webforums and chat channels. This enables us to identify some of the distinct cultural and economic factors which attend this infrastructural work and to note, in particular, how its boring nature leads to burnout and the withdrawal of services. This leads us to suggest ways in which this new understanding could open novel avenues for the disruption of cybercrime.
Mining big data streams using business analytics tools: a bird's eye view on MOA and SAMOA

Big data evolves as the prominent field in modern computing era. Big data analytics and its impact on extracting business intelligence is becoming indispensable for plethora of applications. The non-proprietary software revolution paved the way for illustrious evolution of tools like Weka, rapid miner, orange and R. Traditional data mining techniques hardly adapts to the requirements of rapid data analysis. The data stream processing algorithms that handle multitude of data endow with greater challenge in real time. Big data mining requires further improvisation in traditional tools to address the challenges of massive data processing. This paper highlights the importance of data stream mining and explores two important open source frameworks, namely massive online analysis (MOA) and scalable advanced massive online analysis (SAMOA). The implications of both the tools augurs well for further deliberations in big data research community. Business information system (BIS) models can reach unprecedented heights with the proliferation of these business analytics tools.
Revisiting the economic growth and agriculture nexus in Nigeria: Evidence from asymmetric cointegration and frequency domain causality approaches

The contribution of different agricultural subsectors to economic growth in Nigeria is investigated and further suggests policy implications for investing in each of these subsectors. To this end, Johansen cointegration test and Gregory–Hansen test for cointegration with regime shift, vector error correction model (VECM), dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS), Granger causality, and frequency domain causality test are employed for data from 1981 to 2016. This paper further highlights the long and causal dynamics between the selected agricultural subsector, namely forestry, crop production, fishery and livestock, and economic growth. Findings from time and frequency domain causality tests indicate a one‐way causality running from various subsectors of agriculture to economic growth in Nigeria, meaning how various subsectors of agriculture are important for predicting economic growth. In addition, there is 54% speed of adjustment from the error correction model, suggesting a need for diversification of the economy into the agricultural sector as a means for sustainable economic growth in the face of the continuous plunge in the global oil price. In the long‐run, the effect of forestry, crop production, and fishery on economic growth is statistically significant and positive. These outcomes have inherent policy implication(s), which are elucidated in the concluding section.
A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

In Manufacturing Engineering there is a need to be able to model the behavior of technological variables versus input parameters in order to predict their behavior in advance, so that it is possible to determine the levels of variation that lead to optimal values of the response variables to be obtained. In recent years, it has been a common practice to rely on regression techniques to carry out the above-mentioned task. However, such models are sometimes not accurate enough to predict the behavior of these response variables, especially when they have significant non-linearities. In this present study a comparative analysis between the precision of different techniques based on conventional regression and soft computing is initially carried out. Specifically, regression techniques, based on the response surface model, as well as the use of artificial neural networks and fuzzy inference systems along with adaptive neuro-fuzzy inference systems will be employed to predict the behavior of the aforementioned technological variables. It will be shown that when there are difficulties in predicting the response parameters by using regression models, soft computing models are highly effective, being much more efficient than conventional regression models. In addition, a new method is proposed in this study that consists of using an iterative process to obtain a fuzzy inference system from a design of experiments and then using an adaptive neuro-fuzzy inference system for tuning the constants of the membership functions. As will be shown, with this method it is possible to obtain improved results in the validation metrics. The means of selecting the membership functions to develop this model from the design of experiments is discussed in this present study in order to obtain an initial solution, which will be then tuned by using an adaptive neuro-fuzzy inference system, to predict the behavior of the response variables. Moreover, the obtained results will also be compared.
Is Technology in Our Classrooms? EFL Teachers’ Beliefs and Engagement with Technology in the Classroom

This study aims to investigate the beliefs of Turkish EFL teachers about implementing digital technology in the classroom and the underlying factors which affect their beliefs. Quantitative research design was selected and online Beliefs Questionnaire was used for data collection. The participants of the study consisted of 563 Turkish in-service EFL teachers working at state schools, private schools or colleges in various parts of Turkey. Descriptive statistics and correlation analysis were performed to determine whether the participants’ four categorized beliefs about the use of digital technology (i.e. importance, use, expertise, and context) interrelated with each other, and if any of the participants’ demographic and background factors (i.e. age, gender, level of education, years of teaching experience) predicted the reported beliefs. The results indicated that teachers shared positive views on the use of digital technology in EFL classrooms in terms of importance, use, expertise, and context. It was also found that gender, age and teaching experience did not create any significant change on teachers’ beliefs.
Accurate phase analysis of interferometric fringes by the spatiotemporal phase-shifting method

Abstract Phase-shifting interferometry (PSI) has been widely applied in the field of accurate optical methodology. However, the fluctuation of background and amplitude intensities due to the instability of laser source, and phase-shifting error or vibration are significant problems for the PSI. In this study, the spatiotemporal phase-shifting method (ST-PSM) (Ri S et al 2019 J. Opt. 21 095702), which is a highly accurate and robust phase analysis method using spatial and temporal intensities information simultaneously, is first applied to laser interferometry to achieve a stable measurement. Through several simulations, three effects of fluctuations in background and amplitude intensities, phase-shifting error were investigated. As a result, we clarified that the periodic phase error with fundamental or second harmonic frequencies occurs in the conventional PSM method, whereas no periodic error occurs in the ST-PSM. Besides, the ST-PSM is also robust to the noise either for uniform or distorted interference fringe images. In the Michelson laser interferometer experiment, the ST-PSM realizes a much more stable measurement of phase and phase gradient distributions than the PSM. We have revealed the excellent performance and the striking advantage that ST-PSM is entirely free of periodic errors in a similar manner to the simulations. Therefore, laser interferometry using the ST-PSM can be expected to apply to various applications, including the extremely accurate non-contact shape and deformation measurement, as well as thickness measurement of transparent materials in life and material sciences.