Smart detection of offensive words in social media using the soundex algorithm and permuterm index

Offensive posts in the social media that are inappropriate for a specific age, level of maturity, or impression are quite often destined more to unadult than adult participants. Nowadays, the growth in the number of the masked offensive words in the social media is one of the ethically challenging problems. Thus, there has been growing interest in development of methods that can automatically detect posts with such words. This study aimed at developing a method that can detect the masked offensive words in which partial alteration of the word may trick the conventional monitoring systems when being posted on social media. The proposed method progresses in a series of phases that can be broken down into a pre-processing phase, which includes filtering, tokenization, and stemming; offensive word extraction phase, which relies on using the soundex algorithm and permuterm index; and a post-processing phase that classifies the users’ posts in order to highlight the offensive content. Accordingly, the method detects the masked offensive words in the written text, thus forbidding certain types of offensive words from being published. Results of evaluation of performance of the proposed method indicate a 99% accuracy of detection of offensive words.

Decommissioning Offshore Structures by Extraction of Foundation Mono piles Applying a Vibratory Hammer

The vibratory hammer is one of the tools for the extraction of offshore foundation piles as well as monopiles for the decommissioning of offshore structures. In addition to the standard application, where a pile is driven downward to be installed, a vibratory hammer can also be applied to extract piles. For an efficient and commercially attractive application of vibratory hammers for this purpose, the extraction process needs to be modeled during the planning phase to ensure that the appropriate equipment is used. This paper describes how pile driving simulation software can be used to model the extraction process. This is further illustrated through a case study covering the extraction phase of the 1st (onshore) and 2nd (offshore) part of the Delft Offshore Turbine Project. A monopile with a diameter of 4.0 m was extracted approximate 6 months after installation onshore and then extracted several times offshore shortly after installation in the 2nd phase. The paper will not only present the actual extraction predictions, but also the monitoring data obtained during extraction and the results of the post-analysis.

Purging of silence for robust speaker identification in colossal database

The aim of this work is to develop an effective speaker recognition system under noisy environments for large data sets. The important phases involved in typical identification systems are feature extraction, training and testing. During the feature extraction phase, the speaker-specific information is processed based on the characteristics of the voice signal. Effective methods have been proposed for the silence removal in order to achieve accurate recognition under noisy environments in this work. Pitch and Pitch-strength parameters are extracted as distinct features from the input speech spectrum. Multi-linear principle component analysis (MPCA) is is utilized to minimize the complexity of the parameter matrix. Silence removal using zero crossing rate (ZCR) and endpoint detection algorithm (EDA) methods are applied on the source utterance during the feature extraction phase. These features are useful in later classification phase, where the identification is made on the basis of support vector machine (SVM) algorithms. Forward loking schostic (FOLOS) is the efficient large-scale SVM algorithm that has been employed for the effective classification among speakers. The evaluation findings indicate that the methods suggested increase the performance for large amounts of data in noise ecosystems.

A Rapid Analytical Approach for Monitoring Pharmaceuticals in Hospital Wastewater—A DPX-Based Procedure with Environmentally-Friendly Extraction Phase Coupled to High Performance Liquid Chromatography–Diode Array/Fluorescence Detectors

In this study, a novel analytical methodology based on disposable pipette extraction (DPX) was developed using an alternative extraction phase for the extraction/determination of six pharmaceutical compounds, including carbamazepine, diclofenac, naproxen, fluoxetine, losartan and 17α-ethinylestradiol, in samples of hospital wastewater by high-performance liquid chromatography coupled to diode array and fluorescence detectors. The performance of three extraction phases was examined, including 3-n-propyl (3-methylpyridinium) silsesquioxane chloride (Si3Py+Cl−), the conductive polymer polypyrrole (PPy), and polypyrrole modified with cetyltrimethylammonium bromide (PPy.CTAB). The optimization of the experimental parameters was performed through univariate and multivariate approaches. The optimized condition was obtained with the use of 20 mg of Si3Py+Cl− as extraction phase; six extraction cycles with 700 μL of sample in each cycle and 15 s of extraction time; three desorption cycles with 100 μL of ACN (same aliquot) and 15 s of desorption time; and sample pH adjusted at 3.5 and addition of 15% (w/v) of NaCl in the sample. The methodology proposed exhibited environmentally-friendly aspects with a significantly reduced volume of organic solvent (only 100 µL) and a small amount of extraction phase (20 mg). In addition, the extraction phase employed exhibits a simple synthetic procedure, low cost, and high stability in organic solvent. Moreover, the method developed exhibits high throughput (extraction time of 6.5 min per sample), and robustness. The analytical figures of merit were obtained using hospital wastewater, and the values were very satisfactory. The correlation coefficients were higher than 0.9710. LODs and LOQs ranged from 0.030 µg L−1 to 1.510 µg L−1 and 0.10 µg L−1 to 5.00 µg L−1, respectively. Relative recoveries varied from 80 to 127%, and intra-day (n = 3) and inter-day (n = 9) precision was lower than 19%.

A virtual machine migration policy for multi-tier application in cloud computing based on game theory

Cloud computing technology provides shared computing which can be accessed over the Internet. When cloud data centers are flooded by end-users, how to efficiently manage virtual machines to balance both economical cost and ensure QoS becomes a mandatory work to service providers. Virtual machine migration feature brings a plenty of benefits to stakeholders such as cost, energy, performance, stability, availability. However, stakeholder's objectives are usually conflicted with each other. Also, the optimal resource allocation problem in cloud infrastructure is usually NP-Hard or NP-Complete class. In this paper, the virtual migration problem is formulated by applying game theory to ensure both load balance and resource utilization. The virtual machine migration algorithm, named V2PQL, is proposed based on Markov Decision Process and Q-learning algorithm. The results of the simulation demonstrate the efficiency of our proposal which are divided into training phase and extraction phase. The proposed V2PQL policy has been benchmarked to the Round-Robin policy in order to highlight their strength and feasibility in policy extraction phase.

Strategies To Promote The Quality of Occupational Therapy Fieldwork Education: A Qualitative Study

Background Clinical education is a bridge between theory and practice. The purpose of this study was to develop the strategies to promote the quality of occupational therapy fieldwork education.MethodsThe qualitative content analysis was used to identify the promoting strategies of fieldwork quality in occupational therapy from students' and clinical educators’ perspectives during the 2019–2020 academic sessions. Participants were 12 fieldwork educators (mean age=39.33yr, Male=5, female 7) and 14 students (mean age=23.28yr, Male=7, female 7) in code extraction phase and 16 fieldwork educators (12 of them were in code extraction phase too) in actions of strategies scoring phase.ResultsFollowing the analysis of the data, the prompting strategies categorized into 4 major categories and 10 subcategories: factors related to Fieldwork educators (Improving clinical teaching skills, Enhancing fieldwork management skills, Motivating fieldwork educators and Fostering coordination between fieldwork educators), related to Educational Planning (Modifying clinical fieldwork planning, Revising curriculums), related to Students (Empowering students, Motivating students) and related to Fieldwork settings ( Improving social environment, Improving physical environment ). Based on the qualitative content analysis and 3 expert panels finally the 23 promoting actions were identified.Conclusions Improving the quality of fieldwork education in occupational therapy needs a systematic collaboration between educators, students and educational planners. By doing the promoting actions on fieldwork education process in occupational therapy, the quality of fieldwork education in occupational therapy may improve.