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Register a new userCracking Coding Interview: 150 Programming interview questions and solutions
تغلب على مقابلات التوظيف البرمجية: ١٥٠ سؤال برمجي من مقابلات مع شركات برمجية عالمية مع الشرح
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يساعدك الكتاب على الاعداد لمقابلات برمجية مع شركات عالمية ضخمة مثل غوغل ومايكروسوف من خلال ١٥٠ مثال طرحوا سابقا مع الحل بالاضافة إلى: - خمس وسائل مضمونة لحل مسائل معقدة في الخوارزميات وبنى المعطيات. - ١٠ اخطاء يقوم بها المرشحون في مسابقات التوظيف وكيف تتفاداهم. - كيف تحضر نفسك للأسئلة السلوكية والتقنية.
150 programming interview questions and solutions Plus: • Five proven approaches to solving tough algorithm questions • Ten mistakes candidates make -- and how to avoid them • Steps to prepare for behavioral and technical questions • Interviewer war stories: a view from the interviewer’s side
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Given the diversity of the candidates and complexity of job requirements, and since interviewing is an inherently subjective process, it is an important task to ensure consistent, uniform, efficient and objective interviews that result in high qualit
y recruitment. We propose an interview assistant system to automatically, and in an objective manner, select an optimal set of technical questions (from question banks) personalized for a candidate. This set can help a human interviewer to plan for an upcoming interview of that candidate. We formalize the problem of selecting a set of questions as an integer linear programming problem and use standard solvers to get a solution. We use knowledge graph as background knowledge in this formulation, and derive our objective functions and constraints from it. We use candidate's resume to personalize the selection of questions. We propose an intrinsic evaluation to compare a set of suggested questions with actually asked questions. We also use expert interviewers to comparatively evaluate our approach with a set of reasonable baselines.
In this work, we present programming solutions for some nonlinear partial differential equations, which are the advection equation, the third-order KdV
equations, and a family of Burgers' equations.
This paper introduces MediaSum, a large-scale media interview dataset consisting of 463.6K transcripts with abstractive summaries. To create this dataset, we collect interview transcripts from NPR and CNN and employ the overview and topic description
s as summaries. Compared with existing public corpora for dialogue summarization, our dataset is an order of magnitude larger and contains complex multi-party conversations from multiple domains. We conduct statistical analysis to demonstrate the unique positional bias exhibited in the transcripts of televised and radioed interviews. We also show that MediaSum can be used in transfer learning to improve a model's performance on other dialogue summarization tasks.
Land subdivision is one of the most important works in cadastral surveying. It includes every process that concerned with making separated cadastral pieces by land subdividing into small parts for several objectives such as selling, preparing for lan
d irrigation or land improvements…etc. There are several cases for land subdivision. Land subdivision can be performed using three methods, first: the graphical method, second mathematical method and the last one: the method that depends on the engineering software such as Land Development software.
Modern summarization models generate highly fluent but often factually unreliable outputs. This motivated a surge of metrics attempting to measure the factuality of automatically generated summaries. Due to the lack of common benchmarks, these metric
s cannot be compared. Moreover, all these methods treat factuality as a binary concept and fail to provide deeper insights on the kinds of inconsistencies made by different systems. To address these limitations, we devise a typology of factual errors and use it to collect human annotations of generated summaries from state-of-the-art summarization systems for the CNN/DM and XSum datasets. Through these annotations we identify the proportion of different categories of factual errors and benchmark factuality metrics, showing their correlation with human judgement as well as their specific strengths and weaknesses.
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