Reviewerly/exHarmony huggingface dataset:
Comparison of resource distribution metrics on multi-resource projects

Resource leveling is implemented to reduce the fluctuations of the daily resource usage and the peak resource usage throughout the construction period.Measurement of peak resource demand is straightforward, while fluctuations of resource distribution are difficult to evaluate.Therefore, many resource distribution metrics are proposed in order to measure the fluctuations of the resource distribution.In this study, minimum moment, absolute difference, sum of the squares, and minimum deviation resource distribution metrics are compared by implementing resource distribution on four case study problems obtained from the previous studies.The case study problems are optimized by exhaustive enumeration in order to ensure that the global optimum is obtained for each resource distribution metric.Recently the comparison was conducted for small projects.In this study, larger projects are evaluated to examine the effects of the project size and the search domain.Optimized resource distribution metrics are compared in terms of peak resource demand and the overall resource distribution histogram.Similarity to the standard 'S' curve is expected for good fit resource distribution.The metrics are also compared in terms of easiness of implementation and computational demand.Results of the analyses revealed that minimum moment metric provided lower peak resource demand among the other resource distribution metrics.In addition to this, computational demand and easiness of implementation of the minimum moment metric is the most suitable.The results of this research can be beneficial for the project managers through the selection of appropriate resource distribution metric.
AN INFINITELY SCALABLE DATASET OF SINGLE-POLYGON GRAYSCALE IMAGES AS A FAST TEST PLATFORM FOR SEMANTIC IMAGE SEGMENTATION

Background. Every new semantic image segmentation task requires fine-tuning the segmentation network architecture that is very hard to perform on images of high resolution, which may contain many categories and involve huge computational resources. So, the question is whether it is possible to test segmentation network architectures much faster in order to find optimal solutions that could be imparted to real-world semantic image segmentation tasks.Objective. The goal of the article is to design an infinitely scalable dataset, which could serve as a test platform for semantic image segmentation. The dataset will contain any number of entries of any size required for testing.Methods. A new artificial dataset is designed for semantic image segmentation. The dataset is of grayscale images with the white background. A polygonal object is randomly placed on the background. The polygon edges are black, whereas the polygon body is transparent. Thus, a dataset image is a set of edges of a convex polygon on the white background. The polygon edge is one pixel thick but the transition between the white background and the polygon black edges includes gray pixels in the vicinity of one-pixel edges. Such a noise is an aftermath of the image file format conversion process. The number of edges of the polygon is randomly generated for every next image. The polygon size and position of its center of mass with respect to image margins are randomized as well.Results. A toy dataset of any volume and image size from scratch can be generated. Besides, the dataset generator automatically labels pixels to classes “background” and “polygon”. The dataset does not need augmentation. Eventually, the dataset is infinitely scalable, and it will serve as a fast test platform for segmentation network architectures.Conclusions. The considered examples of using the polygonal dataset confirm its appropriateness and capability of networks trained on it to successfully segment stacks of objects. Additionally, a criterion of early stopping is revealed based on empty image segmentation.
JCTICOL at SemEval-2019 Task 6: Classifying Offensive Language in Social Media using Deep Learning Methods, Word/Character N-gram Features, and Preprocessing Methods

In this paper, we describe our submissions to SemEval-2019 task 6 contest.We tackled all three sub-tasks in this task "OffensEval -Identifying and Categorizing Offensive Language in Social Media".In our system called JCTICOL (Jerusalem College of Technology Identifies and Categorizes Offensive Language), we applied various supervised ML methods.We applied various combinations of word/character ngram features using the TF-IDF scheme.In addition, we applied various combinations of seven basic preprocessing methods.Our best submission, an RNN model was ranked at the 25 th position out of 65 submissions for the most complex sub-task (C).
Deteksi Pencilan pada Model ARIMA dengan Bayesian Information Criterion (BIC) Termodifikasi

Time series data may be affected by special events or circumstances such as promotions, natural disasters, etc. These events can lead to inconsistent observations in the series called outliers. Because outliers can make invalid conclusions, it is important to carry out procedures in detecting outlier effects. In outlier detection there is one type of outlier, namely additive outlier (AO). The process of detecting additive outliers in the ARIMA model can be said as a model selection problem, where the candidate model assumes additive outliers at a certain time. In the selection of models there are criteria that must be considered in order to produce the best model. The good criteria for models selection can use the Bayesian Information Criterion (BIC) derived by Schwarz (1978). Galeano and Pena (2011) proposed a modified Bayesian Information Criterion for model selection and detect potential outliers. The modified Bayesian Information Criterion for outlier detection will be applied to the data OutStanding Loan PT.Pegadaian Cimahi year 2013-2017. So that the best model is obtained that the model with adding 2 potential outliers with the ARIMA model (1.0,0), that outliers at observations 48, and 58 because it has a minimum BICUP value of 1064.95650.
INRIA at SemEval-2019 Task 9: Suggestion Mining Using SVM with Handcrafted Features

We present the INRIA approach to the suggestion mining task at SemEval 2019.The task consists of two subtasks: suggestion mining under single-domain (Subtask A) and crossdomain (Subtask B) settings.We used the Support Vector Machines algorithm trained on handcrafted features, function words, sentiment features, digits, and verbs for Subtask A, and handcrafted features for Subtask B. Our best run archived a F1-score of 51.18% on Subtask A, and ranked in the top ten of the submissions for Subtask B with 73.30% F1-score.
Improving the Accuracy of Weighing Bulk Materials in a Dispenser On-Stream Flow Meter with Two Strain Gauges

A weighing algorithm for an on-stream dispenser flow meter based on two strain gauges is developed and investigated. The main objectives are to achieve more accurate dosing of bulk materials and to prevent the influence of uneven material distribution along the weighing axis. Better dosing accuracy is achieved by installing strain gages on the material feeding side and using an intelligent conveyor belt speed control system, which is the main element in the developed weighing algorithm. The conveyor belt speed is adjusted by means of a fuzzy controller, because the controlled plant is a complex system, and the controllers synthesized according to the classical automatic control theory fail to meet the response speed requirements. The results from testing the selected kinematic design clearly show adequate performance of the fuzzy controller. Namely, when the feed of bulk material decreases, so does the conveyor belt speed, and vice versa. As a result, it becomes possible to decrease the relative measurement error to less than 0.5%. The developed device can be used in the food and agricultural-industrial complexes, in the civil construction, and chemical industries, and in other branches of the national economy, namely, in the processes involving metering and measuring the flow of bulk material in continuous technological processes. When used in combination with a feeder, on-stream flow meters can be used for high-precision weighted metering of various bulk materials. The product is supplied by a sluice or screw feeder, which can additionally be equipped with a smaller diameter screw for operation in rough/precise modes.
Pengaruh Profesionalisme, Independensi dan Pelatihan Auditor Terhadap Kinerja Auditor Pada Kantor Akuntan Publik Provinsi Bali

The purpose of this study is to obtain empirical evidence of the influence of professionalism, independence and auditor training on auditor performance. The location of this research is at the Public Accountant Office (KAP) in Bali Province in 2018 and has been registered with the Indonesian Institute of Certified Public Accountants (IAPI). The method of determining the sample used in this study is the saturated sample method. The method of data collection in this study is the questionnaire method. The data analysis technique used in this study is multiple linear regression analysis. Based on the results of multiple linear regression analysis shows that professionalism, independence and auditor training have a positive effect on auditor performance. This research is expected to provide more in-depth information and understanding, and it is hoped that the auditor can uphold the professionalism and independence when conducting audits, so that the auditor's performance will be good.&#x0D; Keywords: Professionalism, independence, auditor training, auditor performance
Using DIC techniques to measure strain ranges inside the cyclic plastic zone ahead of a fatigue crack tip

A stereo microscope coupled to a 3-D Digital Image Correlation system is used to measure strain ranges on the cyclic plastic zone ahead of a fatigue crack-tip. First, a very refined speckle pattern is applied on the surface of a disk-shaped compact tension specimen of 1020 low-C steel by spraying toner powder over a layer of white paint, a suitable technique to produce the very small dots needed to map surface strain loops on that small region. This technique can achieve enough resolution to measure elastoplastic strain ranges Δε near the crack tip, which cause damage accumulation inside the cyclic plastic zone as the crack grows. The applied technique can identify as well the effects of other nonlinear phenomena that happen in the near-tip region, such as crack closure. Therefore, such measurements can be used to verify the validity of the hypotheses behind the main fatigue crack growth models.
Modeling and Calculation of the Global Solar Irradiance on Slopes

The first step with regard to a simple model of a Photovoltaic Power Plant is developed in this paper based on astronomical and engineering principles.A solar irradiance model is presented in this paper that can be used to forecast the solar energy a surface on Earth is exposed to.The obtained model is verified against engineering expectations.The developed model can serve as a basis for forecasting the power of solar energy.
ESprint: QoS-Aware Management for Effective Computational Sprinting in Data Centers

In the era of 'dark silicon', modern data centers have to provision additional hardware resources to guarantee the Quality of Service (QoS) of applications in case of bursty workloads that typically occur in low frequency but high intensity. Fortunately, Computational Sprinting has proven to be an effective approach to boost the computing performance of many-core processor chips, which allows a chip to exceed its power and thermal limits temporarily by turning on all processor cores and absorbing the extra heat dissipation with novel phase-changing materials. Consequently, it offers a promising way to deal with these occasional workload bursts by unleashing the full potentials of hardware, avoiding deploying extra computing resources. In this work, we propose ESprint, a QoS-aware management system based on an effective feedback control mechanism for latency-critical applications in data centers. ESprint can perform computational sprinting by precisely scheduling core count, frequency levels, and sprinting duration, serving bursty workloads without QoS violation under the thermal constraint. Specifically, ESprint effectively predict load intensity in the next time interval, and further dynamically allocates appropriate computing resources to minimize actual power consumption. Our prototype-based evaluation results show that ESprint achieves up to 1.92x improvement on energy efficiency for typical workloads while ensuring QoS, over the non-sprinting strategy. We also explore the design space among energy efficiency, core count/frequency scaling techniques, workload characteristics, burst intensity, and QoS requirements, and draw several key insights to guide the effective use of computational sprinting in data centers.
Evaluating the thermal stratification of Köyceğiz Lake (SW Turkey) using in-situ and remote sensing observations

The goal of this study is to evaluate the thermal properties of Köyceğiz Lake using in-situ measurements and satellite based thermal infrared imagery. In-situ measurements of surface water temperature and water depth, as well as meteorological data, were used in the analysis. Images of the Landsat 8 TIRS (Thermal Infrared (IR) Sensors) at IR channels were taken from the data archives of United State Geological Survey (USGS), and were validated with surface in-situ measurements. Specific Electrical conductivity (SEC) measurements done at the bottom of the lake were also used for a better understanding of the water thermal stratification and types (cold/hot) that impact on the underwater physical properties of the lake. In-situ measurements at the water surface were interpolated using a new method known as ‘Empirical Bayesian Kriging (EBK)’. The 2D and 3D (dimensional) temperature maps were generated using the Delaunay triangulation technique which utilizes nearest neighbor interpolation method. The results show that the correlation coefficient (R2) between water surface temperatures (WST) from in-situ measurements and IR channels is found to be greater than 0.86. The analysis of 2D and 3D temperature mapping of the lake water revealed evidences for subaqueous cold spring occurrence in the northern part and subaqueous hot springs in the south-western part of Koycegiz Lake. It is concluded that correlating in-situ water temperature measurements to Landsat 8 TIRS images is an efficient method for temporal and spatial evaluation of lake water characteristics.
A Hierarchical Approach for Timely Cyberbullying Detection

In this paper, the problem of accurately detecting cyberbullying instances in a timely manner is addressed. In stark contrast to most prior work that attempts to detect aggressive behavior by looking at individual messages, we consider cyberbullying as a repeated aggressive behavior towards an individual and jointly examine multiple messages exchanged between users. Moreover, we are interested in reaching an accurate decision fast. To this end, we propose a novel hierarchical approach that (i) first characterizes an individual message as aggressive or not by evaluating the optimum least number of informative features extracted from this message, and (ii) uses this new knowledge to decide if it should continue reviewing messages or conclude the process and raise a cyberbullying alert. It is shown that the proposed approach is guaranteed to review the least number of messages before reaching a decision, while the optimum decision rule is shown to minimize the average Bayes risk. Evaluation on real-world Instagram data demonstrates that the proposed method is able to accurately detect cyberbullying instances by reviewing up to 59% less messages than the state-of-the-art.
Working on Сomputer-Assisted Translation platforms: New advantages and new mistakes

The paper presents analysis of errors in translation on the CAT platform Smartcat, which accumulates all tools for computer-assisted translation (CAT) including a machine translation (MT) system and translation memory (TM). The research is conducted on the material of the translation on Smartcat platform (a joint project of a tourist guide translation (35,000 words) from Hebrew to Russian, English, and French). The errors on the CAT platform disclose difficulties in mastering text semantic coherence and stylistic features. The influence of English as lingua franca appears in peculiar orthographic and punctuation errors in the target text in Russian. Peculiar errors in translation on the CAT platform reveal the necessity of advanced technological competence in translators. The peculiar errors uncover problems associated with a source text segmentation into sentences. The segmentation can trigger a translator to preserve the sentence boundaries and use a Russian complicated compound sentence that provoke punctuation errors. Difficulties of the anaphora resolution in distant semantically coherent segments are also associated with the source text segmentation and working window formatting. A joint project presupposes different translators to translate different files of the source document. To generate the coherence, contiguity and integrity of the whole document, the files have to be revised by a third-party editor to avoid conflict of interest. The editor-reviser is also responsible for improving the target text pragmatic and genre characteristics while applying top-down strategy to target text analysis. Thus, the translator’s errors while applying CAT tools reveal the effect of bottom-up text processing alongside with cross-language interference.
Towards the Development of Autonomous Wheelchair

Precise and fast indoor navigation represent well-known challenges that, if solved, can improve many aspects of our lives. In this paper we propose a complex system architecture dedicated to precise indoor navigation in unknown environments with the final purpose of developing an autonomous electric wheelchair. The desired outcome is to use computer vision to detect floors and doors with high accuracy and speed and compute a safe path to be followed. Our solution is based on fully convolutional neural networks (FCN) for image segmentation of corridor images to identify the floor area further used for navigation.
How Old Do You Think I Am? Speech-Language Predictors of Perceived Age and Communicative Competence

Purpose Spoken language serves as a primary means of social interaction, but speech and language skills change with age, a potential source of age-related stereotyping. The goals of this study were to examine how accurately age could be estimated from language samples, to determine which speech and language cues were most informative, and to assess the impact of perceived age on judgments of the speakers' communication skills. Method We analyzed narratives from 84 speakers aged 30–89 years to identify age-related differences and compared these differences to factors affecting perceptions of age and communicative competence. Three groups of raters estimated the speakers' ages and judged the quality of their communication: 44 listeners listened to audio-recorded narratives, 51 readers read transcripts of the narratives, and 24 voice raters listened to 10-s samples of speech extracted from one of the narratives. Results Older speakers spoke more slowly but showed minimal linguistic differences compared to younger speakers. Speakers' ages were estimated quite accurately, even from 10-s samples. Estimates were largely based on cues available in the acoustic signal—speech rate and vocal characteristics—so listeners were more accurate than readers. However, an overreliance on these cues also contributed to overestimates of speakers' ages. Communication ratings were not strongly related to perceived age but were influenced by various aspects of speech and language. In particular, speakers who produced longer narratives and spoke more quickly were judged to be better communicators. Conclusion Speakers tend to be judged on relatively superficial aspects of spoken language, in part because age-related change is most evident at these levels. Implications of these findings for age-related theories of stereotyping and speech-language intervention are discussed.
Innovation as a Mediator on the Assessment of Market Orientation, Training, Social Network, Access to Finance and Women Business Performance in the North-Western Nigeria: A Proposed Framework

Studies in the field of entrepreneur business SMEs report to have contribute 46% of Nigerian GDP and men contributed to the economic growth more than women counterpart while women constitute more than half of Nigeria population therefore, this study seek to proposal the mediating role of innovation on the influence of market orientation, training, social network, access to finance on women business performance with the inclusion of micro enterprise because women constituted more than three third of the population of micro businesses and the introduction of innovation as a mediator is to see how women can use modern technology in improving their business performance.
Quantum speedup of training radial basis function networks

Radial basis function (RBF) network is a simple but useful neural network model that contains wide applications in machine learning. The training of an RBF network reduces to solve a linear system, which is time consuming classically. Based on HHL algorithm, we propose two quantum algorithms to train RBF networks. To apply the HHL algorithm, we choose using the Hamiltonian simulation algorithm proposed in [P. Rebentrost, A. Steffens, I. Marvian and S. Lloyd, Phys. Rev. A 97, 012327, 2018]. However, to use this result, an oracle to query the entries of the matrix of the network should be constructed. We apply the amplitude estimation technique to build this oracle. The final results indicate that if the centers of the RBF network are the training samples, then the quantum computer achieves exponential speedup at the number and the dimension of training samples over the classical computer; if the centers are determined by the K-means algorithm, then the quantum computer achieves quadratic speedup at the number of samples and exponential speedup at the dimension of samples.
Image descriptor based on local color directional quaternionic pattern

Most of the existing local binary pattern methods discard local color differences by holding their binary information. And local spatial information is also neglected. To address these problems, a robust color image descriptor local color directional quaternionic pattern (LCDQP) is proposed. In the descriptor, the color distance map (CDM) is generated in the RGB color space to capture the color distribution among three channels. According to the distribution of CDM elements and their neighbors, four edge models are defined to describe the change trend of the original image. Then, based on this, the LCDQP strings are gained according to the distribution of four models in the four directions of 0 deg, 45 deg, 90 deg, and 135 deg. Finally, an effective quaternionic code method is adopted to construct the LCDQP descriptor. The proposed descriptor not only captures the local color features but also reflects the spatial structure information. Experiments on four representative databases demonstrate that the proposed descriptor is superior to other state-of-the-art approaches.