source_dataset stringclasses 1
value | question stringlengths 6 1.87k | choices stringlengths 20 1.02k | answer stringclasses 4
values | rationale float64 | documents stringlengths 1.01k 5.9k |
|---|---|---|---|---|---|
epfl-collab | Which of the following scheduler policies are preemptive? | ['RR (Round Robin)', 'FIFO (First In, First Out)', 'STCF (Shortest Time to Completion First)', 'SJF (Shortest Job First)'] | C | null | Document 1:::
Kernel preemption
In computer operating system design, kernel preemption is a property possessed by some kernels (the cores of operating systems), in which the CPU can be interrupted in the middle of executing kernel code and assigned other tasks (from which it later returns to finish its kernel tasks).
D... |
epfl-collab | Which of the following are correct implementation for acquire function ? Assume 0 means UNLOCKED and 1 means LOCKED. Initially l->locked = 0. | ['c \n void acquire(struct lock *l)\n {\n if(l->locked == 0) \n return;\n }', 'c \n void acquire(struct lock *l)\n {\n for(;;)\n if(xchg(&l->locked, 1) == 0)\n return;\n }', 'c \n void acquire(struct lock *l)\n {\n for(;;)\n if(cas(&l->locked, 1, 0) == 1)\n ... | B | null | Document 1:::
Test-and-set
A lock can be built using an atomic test-and-set instruction as follows: This code assumes that the memory location was initialized to 0 at some point prior to the first test-and-set. The calling process obtains the lock if the old value was 0, otherwise the while-loop spins waiting to acquir... |
epfl-collab | In which of the following cases does JOS acquire the big kernel lock? | ['Processor traps in user mode', 'Switching from kernel mode to user mode', 'Processor traps in kernel mode', 'Initialization of application processor'] | A | null | Document 1:::
Java Optimized Processor
Java Optimized Processor (JOP) is a Java processor, an implementation of Java virtual machine (JVM) in hardware. JOP is free hardware under the GNU General Public License, version 3. The intention of JOP is to provide a small hardware JVM for embedded real-time systems. The main f... |
epfl-collab | Assume a user program executes following tasks. Select all options that will use a system call. | ['Read the user\'s input "Hello world" from the keyboard.', 'Send "Hello world" to another machine via Network Interface Card.', 'Write "Hello world" to a file.', 'Encrypt "Hello world" by AES.'] | A | null | Document 1:::
System call
In computing, a system call (commonly abbreviated to syscall) is the programmatic way in which a computer program requests a service from the operating system on which it is executed. This may include hardware-related services (for example, accessing a hard disk drive or accessing the device's... |
epfl-collab | What are the drawbacks of non-preemptive scheduling compared to preemptive scheduling? | ['Bugs in one process can cause a machine to freeze up', 'It can lead to poor response time for processes', 'It can lead to starvation especially for those real-time tasks', 'Less computational resources need for scheduling and takes shorted time to suspend the running task and switch the context.'] | C | null | Document 1:::
Least slack time scheduling
This algorithm is also known as least laxity first. Its most common use is in embedded systems, especially those with multiple processors. It imposes the simple constraint that each process on each available processor possesses the same run time, and that individual processes d... |
epfl-collab | Select valid answers about file descriptors (FD): | ['FD is usually used as an argument for read and write.', 'The value of FD is unique for every file in the operating system.', 'FD is constructed by hashing the filename.', 'FDs are preserved after fork() and can be used in the new process pointing to the original files.'] | A | null | Document 1:::
Data descriptor
In computing, a data descriptor is a structure containing information that describes data. Data descriptors may be used in compilers, as a software structure at run time in languages like Ada or PL/I, or as a hardware structure in some computers such as Burroughs large systems. Data descri... |
epfl-collab | Suppose a file system used only for reading immutable files in random fashion. What is the best block allocation strategy? | ['Index allocation with Hash-table', 'Index allocation with B-tree', 'Linked-list allocation', 'Continuous allocation'] | D | null | Document 1:::
Block size (data storage and transmission)
Some newer file systems, such as Btrfs and FreeBSD UFS2, attempt to solve this through techniques called block suballocation and tail merging. Other file systems such as ZFS support variable block sizes.Block storage is normally abstracted by a file system or dat... |
epfl-collab | Which of the following operations would switch the user program from user space to kernel space? | ['Calling sin() in math library.', 'Jumping to an invalid address.', 'Invoking read() syscall.', 'Dividing integer by 0.'] | D | null | Document 1:::
OS kernel
In contrast, application programs such as browsers, word processors, or audio or video players use a separate area of memory, user space. This separation prevents user data and kernel data from interfering with each other and causing instability and slowness, as well as preventing malfunctioning... |
epfl-collab | Which flag prevents user programs from reading and writing kernel data? | ['PTE_P', 'PTE_W', 'PTE_U', 'PTE_D'] | C | null | Document 1:::
OS kernel
In contrast, application programs such as browsers, word processors, or audio or video players use a separate area of memory, user space. This separation prevents user data and kernel data from interfering with each other and causing instability and slowness, as well as preventing malfunctioning... |
epfl-collab | In which of the following cases does the TLB need to be flushed? | ['Inserting a new page into the page table for kernel.', 'Inserting a new page into the page table for a user-space application.', 'Changing the read/write permission bit in the page table.', 'Deleting a page from the page table.'] | D | null | Document 1:::
Translation look-aside buffer
A translation lookaside buffer (TLB) is a memory cache that stores the recent translations of virtual memory to physical memory. It is used to reduce the time taken to access a user memory location. It can be called an address-translation cache. It is a part of the chip's mem... |
epfl-collab | In x86, select all synchronous exceptions? | ['Divide error', 'Page Fault', 'Timer', 'Keyboard'] | A | null | Document 1:::
Triple fault
On the x86 computer architecture, a triple fault is a special kind of exception generated by the CPU when an exception occurs while the CPU is trying to invoke the double fault exception handler, which itself handles exceptions occurring while trying to invoke a regular exception handler. x86... |
epfl-collab | Which of the execution of an application are possible on a single-core machine? | ['Both concurrent and parallel execution', 'Parallel execution', 'Neither concurrent or parallel execution', 'Concurrent execution'] | D | null | Document 1:::
Superscalar execution
A superscalar processor is a CPU that implements a form of parallelism called instruction-level parallelism within a single processor. In contrast to a scalar processor, which can execute at most one single instruction per clock cycle, a superscalar processor can execute more than on... |
epfl-collab | In an x86 multiprocessor system with JOS, select all the correct options. Assume every Env has a single thread. | ['One Env could run on two different processors at different times.', 'Two Envs could run on the same processor simultaneously.', 'Two Envs could run on two different processors simultaneously.', 'One Env could run on two different processors simultaneously.'] | C | null | Document 1:::
Java Optimized Processor
Java Optimized Processor (JOP) is a Java processor, an implementation of Java virtual machine (JVM) in hardware. JOP is free hardware under the GNU General Public License, version 3. The intention of JOP is to provide a small hardware JVM for embedded real-time systems. The main f... |
epfl-collab | In JOS, suppose a value is passed between two Envs. What is the minimum number of executed system calls? | ['2', '4', '1', '3'] | A | null | Document 1:::
Virtual Execution System
The Virtual Execution System (VES) is a run-time system of the Common Language Infrastructure CLI which provides an environment for executing managed code. It provides direct support for a set of built-in data types, defines a hypothetical machine with an associated machine model ... |
epfl-collab | What strace tool does? | ['To remove wildcards from the string.', 'It prints out system calls for given program. These systems calls are called only for that particular instance of the program.', 'To trace a symlink. I.e. to find where the symlink points to.', 'It prints out system calls for given program. These system calls are always called ... | B | null | Document 1:::
Strace
strace is a diagnostic, debugging and instructional userspace utility for Linux. It is used to monitor and tamper with interactions between processes and the Linux kernel, which include system calls, signal deliveries, and changes of process state. The operation of strace is made possible by the ke... |
epfl-collab | What is a good distance metric to be used when you want to compute the similarity between documents independent of their length?A penalty will be applied for any incorrect answers. | ['Chi-squared distance', 'Manhattan distance', 'Euclidean distance', 'Cosine similarity'] | D | null | Document 1:::
Similarity measure
In statistics and related fields, a similarity measure or similarity function or similarity metric is a real-valued function that quantifies the similarity between two objects. Although no single definition of a similarity exists, usually such measures are in some sense the inverse of d... |
epfl-collab | For this question, one or more assertions can be correct. Tick only the correct assertion(s). There will be a penalty for wrong assertions ticked.Which of the following associations can be considered as illustrative examples for inflectional
morphology (with here the simplifying assumption that canonical forms are rest... | ['(hypothesis, hypotheses)', '(to go, went)', '(speaking, talking)', '(activate, action)'] | A | null | Document 1:::
Inflection
In linguistic morphology, inflection (or inflexion) is a process of word formation in which a word is modified to express different grammatical categories such as tense, case, voice, aspect, person, number, gender, mood, animacy, and definiteness. The inflection of verbs is called conjugation, ... |
epfl-collab | Which of the following statements are true? | ['A $k$-nearest-neighbor classifier is sensitive to outliers.', 'k-nearest-neighbors cannot be used for regression.', 'The more training examples, the more accurate the prediction of a $k$-nearest-neighbor classifier.', 'Training a $k$-nearest-neighbor classifier takes more computational time than applying it / using i... | C | null | Document 1:::
Markov property (group theory)
In the mathematical subject of group theory, the Adian–Rabin theorem is a result that states that most "reasonable" properties of finitely presentable groups are algorithmically undecidable. The theorem is due to Sergei Adian (1955) and, independently, Michael O. Rabin (1958... |
epfl-collab | In Text Representation learning, which of the following statements is correct? | ['FastText performs unsupervised learning of word vectors.', 'If you fix all word vectors, and only train the remaining parameters, then FastText in the two-class case reduces to being just a linear classifier.', 'Learning GloVe vectors can be done using SGD in a streaming fashion, by streaming through the input text o... | D | null | Document 1:::
Sequence labeling
In machine learning, sequence labeling is a type of pattern recognition task that involves the algorithmic assignment of a categorical label to each member of a sequence of observed values. A common example of a sequence labeling task is part of speech tagging, which seeks to assign a pa... |
epfl-collab | Consider a matrix factorization problem of the form $\mathbf{X}=\mathbf{W Z}^{\top}$ to obtain an item-user recommender system where $x_{i j}$ denotes the rating given by $j^{\text {th }}$ user to the $i^{\text {th }}$ item . We use Root mean square error (RMSE) to gauge the quality of the factorization obtained. Selec... | ['Given a new item and a few ratings from existing users, we need to retrain the already trained recommender system from scratch to generate robust ratings for the user-item pairs containing this item.', 'For obtaining a robust factorization of a matrix $\\mathbf{X}$ with $D$ rows and $N$ elements where $N \\ll D$, the... | C | null | Document 1:::
Maximum inner-product search
Maximum inner-product search (MIPS) is a search problem, with a corresponding class of search algorithms which attempt to maximise the inner product between a query and the data items to be retrieved. MIPS algorithms are used in a wide variety of big data applications, includi... |
epfl-collab | You are doing your ML project. It is a regression task under a square loss. Your neighbor uses linear regression and least squares. You are smarter. You are using a neural net with 10 layers and activations functions $f(x)=3 x$. You have a powerful laptop but not a supercomputer. You are betting your neighbor a beer at... | ['Because I should have used only one layer.', 'Because I should have used more layers.', 'Because it is almost impossible to train a network with 10 layers without a supercomputer.', 'Because we use exactly the same scheme.'] | D | null | Document 1:::
Learning rule
Depending on the complexity of actual model being simulated, the learning rule of the network can be as simple as an XOR gate or mean squared error, or as complex as the result of a system of differential equations. The learning rule is one of the factors which decides how fast or how accura... |
epfl-collab | Which of the following is correct regarding Louvain algorithm? | ['Modularity is always maximal for the communities found at the top level of the community hierarchy', 'Clique is the only topology of nodes where the algorithm detects the same communities, independently of the starting point', 'If n cliques of the same order are connected cyclically with n-1 edges, then the algorithm... | C | null | Document 1:::
Suurballe's algorithm
In theoretical computer science and network routing, Suurballe's algorithm is an algorithm for finding two disjoint paths in a nonnegatively-weighted directed graph, so that both paths connect the same pair of vertices and have minimum total length. The algorithm was conceived by Joh... |
epfl-collab | Let the first four retrieved documents be N N R R, where N denotes a non-relevant and R a relevant document. Then the MAP (Mean Average Precision) is: | ['3/4', '5/12', '7/24', '1/2'] | B | null | Document 1:::
Precision and recall
In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction... |
epfl-collab | Which of the following is true? | ['High recall implies low precision', 'High recall hurts precision', 'High precision implies low recall', 'High precision hurts recall'] | D | null | Document 1:::
P value
57, No 3, 171–182 (with discussion). For a concise modern statement see Chapter 10 of "All of Statistics: A Concise Course in Statistical Inference," Springer; 1st Corrected ed. 20 edition (September 17, 2004). Larry Wasserman.
Document 2:::
Principle of contradiction
In logic, the law of non-cont... |
epfl-collab | The inverse document frequency of a term can increase | ['by adding a document to the document collection that contains the term', 'by adding a document to the document collection that does not contain the term', 'by adding the term to a document that contains the term', 'by removing a document from the document collection that does not contain the term'] | B | null | Document 1:::
Inverted index
In computer science, an inverted index (also referred to as a postings list, postings file, or inverted file) is a database index storing a mapping from content, such as words or numbers, to its locations in a table, or in a document or a set of documents (named in contrast to a forward ind... |
epfl-collab | Which of the following is wrong regarding Ontologies? | ['Ontologies support domain-specific vocabularies', 'Ontologies help in the integration of data expressed in different models', 'We can create more than one ontology that conceptualize the same real-world entities', 'Ontologies dictate how semi-structured data are serialized'] | D | null | Document 1:::
Class (knowledge representation)
The first definition of class results in ontologies in which a class is a subclass of collection. The second definition of class results in ontologies in which collections and classes are more fundamentally different. Classes may classify individuals, other classes, or a c... |
epfl-collab | In a Ranked Retrieval result, the result at position k is non-relevant and at k+1 is relevant. Which of the following is always true (P@k and R@k are the precision and recall of the result set consisting of the k top ranked documents)? | ['R@k-1 < R@k+1', 'P@k-1 = P@k+1', 'R@k-1 = R@k+1', 'P@k-1 > P@k+1'] | A | null | Document 1:::
Precision and recall
In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction... |
epfl-collab | What is true regarding Fagin's algorithm? | ['It never reads more than (kn)½ entries from a posting list', 'It provably returns the k documents with the largest aggregate scores', 'Posting files need to be indexed by TF-IDF weights', 'It performs a complete scan over the posting files'] | B | null | Document 1:::
Fagin's theorem
Fagin's theorem is the oldest result of descriptive complexity theory, a branch of computational complexity theory that characterizes complexity classes in terms of logic-based descriptions of their problems rather than by the behavior of algorithms for solving those problems. The theorem ... |
epfl-collab | Which of the following is WRONG for Ontologies? | ['Different information systems need to agree on the same ontology in order to interoperate.', 'They help in the integration of data expressed in different models.', 'They give the possibility to specify schemas for different domains.', 'They dictate how semi-structured data are serialized.'] | D | null | Document 1:::
Class (knowledge representation)
While extensional classes are more well-behaved and well understood mathematically, as well as less problematic philosophically, they do not permit the fine grained distinctions that ontologies often need to make. For example, an ontology may want to distinguish between th... |
epfl-collab | What is the benefit of LDA over LSI? | ['LSI is based on a model of how documents are generated, whereas LDA is not', 'LDA has better theoretical explanation, and its empirical results are in general better than LSI’s', 'LSI is sensitive to the ordering of the words in a document, whereas LDA is not', 'LDA represents semantic dimensions (topics, concepts) a... | B | null | Document 1:::
Discriminant function analysis
Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics and other fields, to find a linear combination of features that characterizes or separat... |
epfl-collab | Maintaining the order of document identifiers for vocabulary construction when partitioning the document collection is important | ['in both', 'in neither of the two', 'in the index merging approach for single node machines', 'in the map-reduce approach for parallel clusters'] | C | null | Document 1:::
Text categorization
Document classification or document categorization is a problem in library science, information science and computer science. The task is to assign a document to one or more classes or categories. This may be done "manually" (or "intellectually") or algorithmically. The intellectual cl... |
epfl-collab | Which of the following is correct regarding Crowdsourcing? | ['It is applicable only for binary classification problems', 'The output of Majority Decision can be equal to the one of Expectation-Maximization', 'Random Spammers give always the same answer for every question', 'Honey Pot discovers all the types of spammers but not the sloppy workers'] | B | null | Document 1:::
Crowd sourcing
Daren C. Brabham defined crowdsourcing as an "online, distributed problem-solving and production model." Kristen L. Guth and Brabham found that the performance of ideas offered in crowdsourcing platforms are affected not only by their quality, but also by the communication among users about... |
epfl-collab | When computing PageRank iteratively, the computation ends when... | ['The norm of the difference of rank vectors of two subsequent iterations falls below a predefined threshold', 'All nodes of the graph have been visited at least once', 'The difference among the eigenvalues of two subsequent iterations falls below a predefined threshold', 'The probability of visiting an unseen node fal... | A | null | Document 1:::
PageRank
PageRank (PR) is an algorithm used by Google Search to rank web pages in their search engine results. It is named after both the term "web page" and co-founder Larry Page. PageRank is a way of measuring the importance of website pages. According to Google: PageRank works by counting the number an... |
epfl-collab | How does LSI querying work? | ['The query vector is treated as an additional term; then cosine similarity is computed', 'The query vector is multiplied with an orthonormal matrix; then cosine similarity is computed', 'The query vector is transformed by Matrix S; then cosine similarity is computed', 'The query vector is treated as an additional docu... | D | null | Document 1:::
Data retrieval
The retrieved data may be stored in a file, printed, or viewed on the screen. A query language, like for example Structured Query Language (SQL), is used to prepare the queries. SQL is an American National Standards Institute (ANSI) standardized query language developed specifically to writ... |
epfl-collab | Suppose that an item in a leaf node N exists in every path. Which one is correct? | ['For every node P that is a parent of N in the fp tree, confidence(P->N) = 1', 'N’s minimum possible support is equal to the number of paths.', 'N co-occurs with its prefix in every transaction.', 'The item N exists in every candidate set.'] | B | null | Document 1:::
Tree (automata theory)
If every node of a tree has finitely many successors, then it is called a finitely, otherwise an infinitely branching tree. A path π is a subset of T such that ε ∈ π and for every t ∈ T, either t is a leaf or there exists a unique c ∈ N {\displaystyle \mathbb {N} } such that t.c ∈ π... |
epfl-collab | In a Ranked Retrieval result, the result at position k is non-relevant and at k+1 is relevant. Which of the following is always true (P@k and R@k are the precision and recall of the result set consisting of the k top ranked documents)? | ['P@k-1 = P@k+1', 'R@k-1 < R@k+', 'R@k-1 = R@k+1', 'P@k-1 > P@k+1'] | B | null | Document 1:::
Precision and recall
In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction... |
epfl-collab | For the number of times the apriori algorithm and the FPgrowth algorithm for association rule mining are scanning the transaction database the following is true | ['apriori cannot have fewer scans than fpgrowth', 'fpgrowth and apriori can have the same number of scans', 'all three above statements are false', 'fpgrowth has always strictly fewer scans than apriori'] | B | null | Document 1:::
Apriori algorithm
Apriori is an algorithm for frequent item set mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in... |
epfl-collab | Given the following teleporting matrix (Ε) for nodes A, B and C:[0 ½ 0][0 0 0][0 ½ 1]and making no assumptions about the link matrix (R), which of the following is correct:(Reminder: columns are the probabilities to leave the respective node.) | ['A random walker can never leave node A', 'A random walker can always leave node B', 'A random walker can never reach node A', 'A random walker can always leave node C'] | B | null | Document 1:::
Transition rate matrix
In probability theory, a transition-rate matrix (also known as a Q-matrix, intensity matrix, or infinitesimal generator matrix) is an array of numbers describing the instantaneous rate at which a continuous-time Markov chain transitions between states. In a transition-rate matrix Q ... |
epfl-collab | Which of the following methods does not exploit statistics on the co-occurrence of words in a text? | ['Vector space retrieval\n\n\n', 'Transformers\n\n\n', 'Word embeddings\n\n\n', 'Fasttext'] | A | null | Document 1:::
Random indexing
In Euclidean spaces, random projections are elucidated using the Johnson–Lindenstrauss lemma.The TopSig technique extends the random indexing model to produce bit vectors for comparison with the Hamming distance similarity function. It is used for improving the performance of information r... |
epfl-collab | Which attribute gives the best split?A1PNa44b44A2PNx51y33A3PNt61j23 | ['A1', 'All the same', 'A3', 'A2'] | C | null | Document 1:::
Split (graph theory)
In graph theory, a split of an undirected graph is a cut whose cut-set forms a complete bipartite graph. A graph is prime if it has no splits. The splits of a graph can be collected into a tree-like structure called the split decomposition or join decomposition, which can be construct... |
epfl-collab | Suppose that q is density reachable from p. The chain of points that ensure this relationship are {t,u,g,r} Which one is FALSE? | ['p has to be a core point', 'q has to be a border point', 'p and q will also be density-connected', '{t,u,g,r} have to be all core points.'] | B | null | Document 1:::
Density point
In mathematics, Lebesgue's density theorem states that for any Lebesgue measurable set A ⊂ R n {\displaystyle A\subset \mathbb {R} ^{n}} , the "density" of A is 0 or 1 at almost every point in R n {\displaystyle \mathbb {R} ^{n}} . Additionally, the "density" of A is 1 at almost every point ... |
epfl-collab | In User-Based Collaborative Filtering, which of the following is correct, assuming that all the ratings are positive? | ['Pearson Correlation Coefficient and Cosine Similarity have the same value range, but can return different similarity ranking for the users', 'Pearson Correlation Coefficient and Cosine Similarity have different value range, but return the same similarity ranking for the users', 'If the variance of the ratings of one ... | D | null | Document 1:::
Precision and recall
For classification tasks, the terms true positives, true negatives, false positives, and false negatives (see Type I and type II errors for definitions) compare the results of the classifier under test with trusted external judgments. The terms positive and negative refer to the class... |
epfl-collab | The term frequency of a term is normalized | ['by the maximal frequency of the term in the document collection', 'by the maximal frequency of all terms in the document', 'by the maximal term frequency of any document in the collection', 'by the maximal frequency of any term in the vocabulary'] | B | null | Document 1:::
Cycles per sample
In digital signal processing (DSP), a normalized frequency is a ratio of a variable frequency (f) and a constant frequency associated with a system (such as a sampling rate, fs). Some software applications require normalized inputs and produce normalized outputs, which can be re-scaled t... |
epfl-collab | Which is an appropriate method for fighting skewed distributions of class labels in classification? | ['Construct the validation set such that the class label distribution approximately matches the global distribution of the class labels', 'Use leave-one-out cross validation', 'Generate artificial data points for the most frequent classes', 'Include an over-proportional number of samples from the larger class'] | B | null | Document 1:::
Multi-label classification
In machine learning, multi-label classification or multi-output classification is a variant of the classification problem where multiple nonexclusive labels may be assigned to each instance. Multi-label classification is a generalization of multiclass classification, which is th... |
epfl-collab | Thang, Jeremie and Tugrulcan have built their own search engines. For a query Q, they got precision scores of 0.6, 0.7, 0.8 respectively. Their F1 scores (calculated by same parameters) are same. Whose search engine has a higher recall on Q? | ['Thang', 'Jeremie', 'We need more information', 'Tugrulcan'] | A | null | Document 1:::
Average precision
Evaluation measures for an information retrieval (IR) system assess how well an index, search engine or database returns results from a collection of resources that satisfy a user's query. They are therefore fundamental to the success of information systems and digital platforms. The suc... |
epfl-collab | When compressing the adjacency list of a given URL, a reference list | ['Is chosen from neighboring URLs that can be reached in a small number of hops', 'All of the above', 'May contain URLs not occurring in the adjacency list of the given URL', 'Lists all URLs not contained in the adjacency list of given URL'] | C | null | Document 1:::
Adjacency list
In graph theory and computer science, an adjacency list is a collection of unordered lists used to represent a finite graph. Each unordered list within an adjacency list describes the set of neighbors of a particular vertex in the graph. This is one of several commonly used representations ... |
epfl-collab | Data being classified as unstructured or structured depends on the: | ['Degree of abstraction', 'Level of human involvement', 'Type of physical storage', 'Amount of data '] | A | null | Document 1:::
Unstructured data
Unstructured data (or unstructured information) is information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well. This results in... |
epfl-collab | Suppose you have a search engine that retrieves the top 100 documents and
achieves 90% precision and 20% recall. You modify the search engine to
retrieve the top 200 and mysteriously, the precision stays the same. Which one
is CORRECT? | ['The F-score stays the same', 'This is not possible', 'The number of relevant documents is 450', 'The recall becomes 10%'] | C | null | Document 1:::
Precision and recall
In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction... |
epfl-collab | In the χ2 statistics for a binary feature, we obtain P(χ2 | DF = 1) > 0.05. This means in this case, it is assumed: | ['That the class label correlates with the feature', 'That the class label is independent of the feature', 'That the class labels depends on the feature', 'None of the above'] | B | null | Document 1:::
5 sigma
In the case where X takes random values from a finite data set x1, x2, ..., xN, with each value having the same probability, the standard deviation is or, by using summation notation, If, instead of having equal probabilities, the values have different probabilities, let x1 have probability p1, x2... |
epfl-collab | Which of the following is correct regarding the use of Hidden Markov Models (HMMs) for entity recognition in text documents? | ['The cost of predicting a word is linear in the lengths of the text preceding the word.', 'The label of one word is predicted based on all the previous labels', 'An HMM model can be built using words enhanced with morphological features as input.', 'The cost of learning the model is quadratic in the lengths of the tex... | C | null | Document 1:::
Sequence labeling
Most sequence labeling algorithms are probabilistic in nature, relying on statistical inference to find the best sequence. The most common statistical models in use for sequence labeling make a Markov assumption, i.e. that the choice of label for a particular word is directly dependent o... |
epfl-collab | 10 itemsets out of 100 contain item A, of which 5 also contain B. The rule A -> B has: | ['5% support and 10% confidence', '5% support and 50% confidence', '10% support and 50% confidence', '10% support and 10% confidence'] | B | null | Document 1:::
Inclusion-exclusion principle
In combinatorics, a branch of mathematics, the inclusion–exclusion principle is a counting technique which generalizes the familiar method of obtaining the number of elements in the union of two finite sets; symbolically expressed as | A ∪ B | = | A | + | B | − | A ∩ B | {\di... |
epfl-collab | Which of the following is correct regarding the use of Hidden Markov Models (HMMs) for entity recognition in text documents? | ['When computing the emission probabilities, a word can be replaced by a morphological feature (e.g., the number of uppercase first characters)', 'HMMs cannot predict the label of a word that appears only in the test set', 'If the smoothing parameter λ is equal to 1, the emission probabilities for all the words in the ... | A | null | Document 1:::
Sequence labeling
Most sequence labeling algorithms are probabilistic in nature, relying on statistical inference to find the best sequence. The most common statistical models in use for sequence labeling make a Markov assumption, i.e. that the choice of label for a particular word is directly dependent o... |
epfl-collab | A basic statement in RDF would be expressed in the relational data model by a table | ['with three attributes', 'with one attribute', 'with two attributes', 'cannot be expressed in the relational data model'] | C | null | Document 1:::
Relational Model
The relational model (RM) is an approach to managing data using a structure and language consistent with first-order predicate logic, first described in 1969 by English computer scientist Edgar F. Codd, where all data is represented in terms of tuples, grouped into relations. A database o... |
epfl-collab | Which of the following statements is wrong regarding RDF? | ['The object value of a type statement corresponds to a table name in SQL', 'Blank nodes in RDF graphs correspond to the special value NULL in SQL', 'RDF graphs can be encoded as SQL databases', 'An RDF statement would be expressed in SQL as a tuple in a table'] | B | null | Document 1:::
SPARQL
SPARQL (pronounced "sparkle" , a recursive acronym for SPARQL Protocol and RDF Query Language) is an RDF query language—that is, a semantic query language for databases—able to retrieve and manipulate data stored in Resource Description Framework (RDF) format. It was made a standard by the RDF Data... |
epfl-collab | The number of non-zero entries in a column of a term-document matrix indicates: | ['how relevant a term is for a document ', 'how many terms of the vocabulary a document contains', 'none of the other responses is correct', 'how often a term of the vocabulary occurs in a document'] | B | null | Document 1:::
Zero matrix
In mathematics, particularly linear algebra, a zero matrix or null matrix is a matrix all of whose entries are zero. It also serves as the additive identity of the additive group of m × n {\displaystyle m\times n} matrices, and is denoted by the symbol O {\displaystyle O} or 0 {\displaystyle 0... |
epfl-collab | What is TRUE regarding Fagin's algorithm? | ['It performs a complete scan over the posting files', 'It provably returns the k documents with the largest aggregate scores', 'Posting files need to be indexed by TF-IDF weights', 'It never reads more than (kn)1⁄2 entries from a posting list'] | B | null | Document 1:::
Fagin's theorem
Fagin's theorem is the oldest result of descriptive complexity theory, a branch of computational complexity theory that characterizes complexity classes in terms of logic-based descriptions of their problems rather than by the behavior of algorithms for solving those problems. The theorem ... |
epfl-collab | A false negative in sampling can only occur for itemsets with support smaller than | ['p*s', 'p*m', 'the threshold s', 'None of the above'] | D | null | Document 1:::
Multiple comparisons problem
However, if 100 tests are each conducted at the 5% level and all corresponding null hypotheses are true, the expected number of incorrect rejections (also known as false positives or Type I errors) is 5. If the tests are statistically independent from each other (i.e. are perf... |
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