BBAEG: Towards BERT-based Biomedical Adversarial Example Generation for Text Classification

Healthcare predictive analytics aids medical decision-making, diagnosis prediction and drug review analysis. Therefore, prediction accuracy is an important criteria which also necessitates robust predictive language models. However, the models using deep learning have been proven vulnerable towards insignificantly perturbed input instances which are less likely to be misclassified by humans. Recent efforts of generating adversaries using rule-based synonyms and BERT-MLMs have been witnessed in general domain, but the ever-increasing biomedical literature poses unique challenges. We propose BBAEG (Biomedical BERT-based Adversarial Example Generation), a black-box attack algorithm for biomedical text classification, leveraging the strengths of both domain-specific synonym replacement for biomedical named entities and BERT-MLM predictions, spelling variation and number replacement. Through automatic and human evaluation on two datasets, we demonstrate that BBAEG performs stronger attack with better language fluency, semantic coherence as compared to prior work.

Optimal Strengthening Design Of Reinforced Concrete Frames Using Steel Jacket Technique

This research investigates the behavior of RC frames strengthened using steel jacket technique and the impact of using this technique on the frame specifications was examined in terms of rigidity, ductility and resistance.

New Hybrid Evolutionary Algorithm for Solving Multi-Objective Optimization Problems

Multi-objective evolutionary algorithms are used in a wide range of fields to solve the issues of optimization, which require several conflicting objectives to be considered together. Basic evolutionary algorithm algorithms have several drawbacks, such as lack of a good criterion for termination, and lack of evidence of good convergence. A multi-objective hybrid evolutionary algorithm is often used to overcome these defects.

New Hybrid Evolutionary Algorithm for the Multi-Objective Traveling Salesman Problem (moTSP)

In the Multi-objective Traveling Salesman Problem (moTSP) simultaneous optimization of more than one objective functions is required. This paper proposes hybrid algorithm to solve the multiobjectives Traveling Salesman problem through the integration of the ant colony optimization algorithm with the Genetic algorithm.

Optimum Design of Reinforced Concrete T-Beam Sections Using Nonlinear Programming According to Syrian Code

This research deals with the minimum cost design of reinforced concrete T-beams according to the Syrian code. The aim is to minimize the total cost of the beam while respecting all the design requirements. Traditional method depend on a set of suppositions, in the opposite this methodology aim to reach the optimal solution among a set of constraints with respect the objective function. So that, using this methodology leading to the minimum cost reinforced section design. This research is shown that the problem can be formulated in a nonlinear mathematical programming format. Several cases are used to explain the applicability of the formulation in accordance with the current Syrian code. Traditional method of Syrian code has been used to design sections in this paper, utilizing the nonlinear programming method provided by Lingo14.0 software from LINDO Systems Inc. The comparison of the results shows that important saving can be obtained at the total cost of a reinforced concrete T-beams design.

Multi-Objective Optimization Algorithms to Solve Molecular Docking

Molecular docking is a hard optimization problem that has been tackled in the past, demonstrating new and challenging results when looking for one objective . However, only a few papers can be found in the literature that deal with this problem by means of a multi-objective approach, and no experimental comparisons have been made in order to clarify which of them has the best overall performance. In this research, we use and compare, a set of representative multi-objective optimization algorithms. The approach followed is focused on optimizing the inter-molecular and intra-molecular energies as two main objectives to minimize.

Selecting Real Seismic Records and Scaling it to Fit the Syrian Design Spectra using Genetic Algorithm

In this study, basic methodologies of the GA and the scaling procedures are summarized, the scaling criteria of real time history records to satisfy the Syrian design code are discussed. The traditional time domain scaling procedures and the scaling procedures using GA are utilized to scale a number of the available real records to match the Syrian design spectra. The resulting time histories of the procedures are investigated and compared in terms of meeting criteria.

Optimal Reactive Power Compensation in Electrical Distribution Networks

Reactive power compensation in distribution networks is one of the most important economic and environmental issues in power system studies. In this paper the following points are investigated: · The characteristics of the most developed equipment used for reactive power compensation. · Equations used in ETAP program calculation · OCP is part of ETAP program which gives us the possibility to determine optimal reactive power sizing and placement in distribution networks in order to achieve optimal Power loss and distribution power system enhancement. · ETAP program is applied on a part of Damascus suburb electrical network which was simulated by its real parameters and the positive economical and technical results have been clarified.

Evaluation the Stability of the Medial end of the Third Palatal Ruga as a Landmark in Maxilla in Extraction Cases, and the Possibility of Using it in Mandible

Cephalometric superimpositions are the most commonly means used to assess the orthodontic teeth movement – especially- in cases of extraction - with their attendant risks and difficulty, therefore dental casts were an alternative way for serial assessment. So the aim was to evaluate the stability of the medial end of the third palatal ruga as a landmark in maxilla in extraction cases, and the possibility of using it in the mandible.

The Effects Of Parallelism Of Branch and Bound Algorithms In Improving It's Performances

The Branch and Bound algorithms which are refereed to as B & B are commonly used to solve NP - hard combinatorial optimization problems. Although these algorithms were efficient, the size of problems which can solved and proved the optimality of solution by these algorithms was limited, because of the limitation of computers capabilities although of it’s highly development. When the parallel programming 46 and Multiprocessors computers were appeared, the researcher thought to use the capabilities of these techniques and machines to increase the size of solved problems. Three main anomalies may occur when the parallelism is used. This research aimed to design a new model of Branch and Bound algorithms in order to analyze the performance. This model based on a new rule to choose the best node among the equal evaluation node. Tight bounds of each rules were computed and proved the ability to achieve it. Sufficient and necessary condition anomalous are given regarding the predisposition for each of the three classes of behavior. In this research, we discussed and compared the results of further relaxations on the assumptions used in branch and bound algorithms. We suggested using the asynchronous models to have the utmost benefit of the capabilities of parallel programming.