sadat2307/MSciNLI huggingface dataset:
Domain: Security and privacy
Sentence 1: NL.L + NP .P ACC = (5) NL.L + NP .P + NL.P + NP .L where NL.L and NP .P are the numbers of correctly clas­si.ed URLs, and NL.P and NP .L are the numbers of mis­classi.ed URLs.
Sentence 2: accuracy is the total num­ber of correctly classi.ed URLs normalized by dividing by total number of URLs in a given dataset.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Policies for Identifiers (from [ROL]) By the multiple-roles requirement, no identifier should be sent without user consent.
Sentence 2: the easiest solution is that a wallet handles identifiers like all other attributes.6 identifiers would occur only in Step 9 of Figure 2, which underlies the policies, and nothing like a distinguished name should be mandatory in the assertion format.
Relationship: entailment
Domain: Security and privacy
Sentence 1: The .nal objective consists in re-constructing sequences of attack events that can be at­tributed with a high con.dence to the same root phenomenon in function of multiple criteria.
Sentence 2: we want to build an inference engine that takes as input the ex­tracted knowledge to classify incoming attack events into either known phenomena , or otherwise to identify a new phenomenon when needed (e.g., when we observe the .rst attack event of a new zombie army).
Relationship: entailment
Domain: Security and privacy
Sentence 1: 2.1 iOS Background Apple devices such as iPhone, iPod touch, and iPad are based on the iOS operating system that provides several security features, e.g., mandatory code signing, data en­cryption, or memory randomization.
Sentence 2: iOS en­forces application sandboxing to isolate applications from each other, i.e., an application cannot access .les in another application s directory.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Change A s sent messages to what A would have sent when using input x and the random tape sampled earlier.
Sentence 2: for every message m, every odd round j such that |u| < j = |u| + ., 's ' Pr Ch *t [j] | t [1..(j-1)]= m = Pr [pA (x, RA, t s [1..(j-1)]) = m] .
Relationship: entailment
Domain: Security and privacy
Sentence 1: we define its depth as the minimum number of states visited when moving from s0 to s in the DFA.
Sentence 2: the initial state s0 will have depth 0, the set of states S1 directly reachable from s0 will have depth 1, the set of states S2 directly reachable from any of the S1 (but not from s0) will have depth 2, and so on.
Relationship: entailment
Domain: Security and privacy
Sentence 1: To this end, one needs languages that let the programmer specify security re­quirements and reason about them using simple abstractions, as well as tools that can produce code to enforce these requirements.
Sentence 2: when considering the integrity and con.dential­ity of information, the veri.cation of distributed programs entan­gles different aspects of system implementations, ranging from application-level information-.ow control down to cryptographic algorithms and communication protocols (themselves depending on adequate integrity and con.dentiality control for their crypto­graphic keys).
Relationship: entailment
Domain: Security and privacy
Sentence 1: Ellison, Hall, Milbert, and Schneier [4] propose a recovery system based on what they refer to as personal entropy.In this system, the user is asked a number of questions about her personal history.
Sentence 2: the sequence of answers, which we denote by wc, is used to encrypt a randomly generated cryp­tographically strong key cc.the key cc is encrypted as a ciphertext cd = Encw [cc] under a collection wcof what may be thought of as ancillary passwords.
Relationship: entailment
Domain: Security and privacy
Sentence 1: CONCLUSION In this paper, we presented an SMM-based volatile mem­ory acquisition technique that overcomes many of the limi­tations a.ecting state-of-the-art solutions.
Sentence 2: we have shown how SMMDumper is able to atomically perform a live memory acquisition, while guaranteeing the on-system and in-transit integrity of the acquired information.
Relationship: entailment
Domain: Security and privacy
Sentence 1: If the work.ow information about the utility s remote disconnect process is considered, one may show that surreptitiously sending a large number of disconnect commands does not necessarily lead to a large­scale blackout.
Sentence 2: when the process of switching meters on and o. is implemented with a random delay on the order of hours [34, 35], that delay would cause some house­holds to be a.ected earlier than others.
Relationship: entailment
Domain: Security and privacy
Sentence 1: prm(Kds, R, c), pca(Kds,Kdu,C), contains(C, C'), arca(Kds, A, R, C').
Sentence 2: a data user (requester) Kdu has A access on resource R if a data subject Kds, which controls access to personal data in R, says that Kdu is assigned to a category C (in this case a role) that inherits the A privilege on R, to which a closed meta-policy on access applies, from a category C' (a role) that is junior to C. Only positive authorizations may be speci.ed in ANSI Hierarchical RBAC and so a closed meta-policy is represented in the speci.ca­tion above.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Moreover, adversaries could use evidences such as identi­cal email addresses, screen names, similar posts, and at­tribute/structural re-identi.cation attacks to bridge pro.les across di.erent social networks [32, 25].
Sentence 2: [38, 13, 25] have shown that people are highly identi.able with very little information, which make cross-network aggrega­tions quite feasible.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Furthermore, the patient should be able to assign dif­ferent weights v1,...,v. to the conditions based on their importance to the patient and obtain a recom­mendation that takes into account these weights.
Sentence 2: the output will consist of best-ranked physicians where the ranks are determined using the weighted sum of the physicians scores for the individual con­ditions, weighted by the patient-provided importance values vi s, i.e., the combined scores are computed as  P. sjK = i=1 visjki ,where sjki is the physician j s score for condition ki .
Relationship: entailment
Domain: Security and privacy
Sentence 1: Then TT is a t-collusion resistant traitor­tracing system.
Sentence 2: using the notation of Appendix A, for all t> 0,n>t, and all polynomial time adversaries A,there exist polynomial time semantic security adversaries B1 and B2 attacking E such that MH Adv[A,TT (n)](.)
Relationship: entailment
Domain: Security and privacy
Sentence 1: In a GSCS for which its spatial network has a containment relation, policies should be formulated to promote the dec­laration of .ne-grained locations: i.e., if l1 contains l2, then we prefer users to declare her current location as l2 rather than l1.
Sentence 2: a reasonable policy shall grant more access to requesters who declare a .ner-grained location.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Speci.cally, we show that there exists a program of which the control .ow graph would ac­cept the same language as the execution graph.
Sentence 2: if the execution graph were to accept any other sys­tem call sequence s, then there is a program that can emit exactly the same training sequences but for which the con­trol .ow graph would not accept s. In some sense, this is the best one can hope to achieve toward using a gray-box technique to mimic the power of a control .ow model obtained via white-box analysis.
Relationship: entailment
Domain: Security and privacy
Sentence 1: After retrieving its execution context (i.e., those register values), we then examine the page table (from the given base address) to determine which virtual addresses in the suspect process have pages allocated or present.
Sentence 2: it determines the gfn for each virtual address and then invokes gfn to pfn() to determine the corresponding pfn in the pro­duction VM s EPT.
Relationship: entailment
Domain: Security and privacy
Sentence 1: To achieve this keys and decoys should both be ran­domly selected from larger, .xed portfolios where the same ratio exists between images selected for login and the size of the port­folio.
Sentence 2: if kl and dl refer to the number of keys and decoys (respectively) displayed at any given login challenge, and k and d refer to the total number of key and decoy images (respec­tively) being assigned to a user, then kl dl = kd To use this formula in practice a system administrator would choose values for k, kl,dl, then calculate d = dl kk l .
Relationship: entailment
Domain: Security and privacy
Sentence 1: In order to get the bytes of the instruc­tion to become alphanumeric, other registers have to be added to the list.
Sentence 2: the following code MOV r0, r3 MOV r1, r4 MOV r2, r6 has to be transformed as following to be alphanumeric: STMPLDB r5, {r3, r4, r6, r8, r9, lr}^ RSBPL r3, r8, #72 SUBPL r5, r5, r3, ROR #2 LDMPLDA r5!, {r0, r1, r2, r6, r9, lr} In the example above, the registers r3, r4 and r6 are stored on the stack using the STM instruction and then read from the stack into registers r0, r1, r2 using the LDM instruction.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Another related issue is how we prevent DMA from being abused to subvert the HookSafe s memory protection.
Sentence 2: to limit the physical memory accessible to a DMA engine of an I/O de­vice, we utilize the hardware-based IOMMU support (already im­plemented in Xen) in the recent CPU/chipsets to map the address space of DMA engine to a safe place.
Relationship: entailment
Domain: Security and privacy
Sentence 1: As described above Message Key Issuing Facilities are provided to cater for each of these acquisition methods.
Sentence 2: these facilities are as follows: Outbound Message Insertion,  An AJAX Web Service, and  Manual Key Issuing Facility  9.2 How Message Keys Reduce Reply Lost Messages Replies are the most common form of email message and fortunately ReplyLost Messages are the easiest to avoid.
Relationship: entailment
Domain: Security and privacy
Sentence 1: In theory, there could be a stronger correlated energy cancellation attack which requires more advanced hardware.
Sentence 2: in addition to a pair of directional an­tennas, the attacker may use a wideband software de.ned radio (SDR) device which has a bandpass .lter, and in real­time it computes and injects a phase delay corresponding to the legitimate signal s frequency.
Relationship: entailment
Domain: Security and privacy
Sentence 1: For these functionalities, com­bining multiple interactive operations into a single batch results in tremendous performance improvement.
Sentence 2: using a loop with concurrent iterations in arithmetic operations program and using public int main() { public int i, j, S; smcinput(S, 1, 1); int A[S][S], B[S][S], C[S][S]; smcinput(A, 1, S*S); //assume B is given as transpose of original B smcinput(B, 1, S*S); for (i = 0; i < S; i++) [ for (j = 0; j < S; j++) [ C[i][j] = A[i] @ B[j]; ] ] smcoutput(C, 1, S*S); return 0; } Figure 3: Matrix multiplication program that uses concurrent computation.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Some of such ranges are given by the de­veloper, while others can be automatically identi.ed.
Sentence 2: those associated with widget elements, such as check boxes and list boxes, can be analyzed to .nd out their legitimate contents, ei­ther directly from their properties (e.g., check boxes), or from their hosting HTML pages (e.g., List boxes).
Relationship: entailment
Domain: Security and privacy
Sentence 1: As we do not want that the set-up phase in.uences the evaluation of the general cost of the pro­tocol (that in practice could last more that the simulated time), we start measuring the performance of the protocols from the 84,000th second onwards.
Sentence 2: the messages sent before the 84,000th second are not considered in the .nal mean value of sent messages.
Relationship: entailment
Domain: Security and privacy
Sentence 1: From Algotithm 1, we conclude that h = k. For q < h, there ex­ 1+;(1+k ) ists ch+1 such that ch+1 < wloga .
Sentence 2: 1+;k user h + 1 can increase its utility by unilaterally changing its strategy from N to Y , contradicting the NE assumption.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Considering security for IMDs is necessary because: (1) they deal with sensitive patient information, (2) laws such as the Health Insurance Portability and Accountabil­ity Act (HIPAA) [14], mandate it, and (3) security attacks can have serious safety consequences for the patients.
Sentence 2: a malicious entity can now easily suppress legiti­mate information and introduce bogus information between the devices and the middleware, leading to untimely or un­wanted actuation or loss of privacy.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Stage 2: Key discovery between mobile node and stationary node To establish a direct pairwise key between stationary node u and mobile node v, a sensor node u needs to find a stationary access node a in its neighborhood, such that node a can establish pairwise keys with both mobile node v and stationary node u.
Sentence 2: a stationary access node needs to establish pairwise keys with both a mobile node and a stationary node.
Relationship: entailment
Domain: Security and privacy
Sentence 1: ·2-k = B ' We reveal the value of B ' to the .rst player (the second player already knows this value).
Sentence 2: we condition the game G(Y ,X |V1,B,W,E)TV1 on the value of B ' , and consider the  game G(Y,X|V1,B,W,E)TV1 |B1 = G(Y,X |V1,B,W,E,B1)TV1 , with parameters (k, d-1, P(Y |V1,B,W,E,B1)TV1 , P(X|V1,B,W,E,B1)TV1 ).
Relationship: entailment
Domain: Security and privacy
Sentence 1: The N-gram Characterization of Program Behavior It is important to note that the self database for a program consists of all N-grams that occur during training.
Sentence 2: we do not slice each trace into segments of length N. Instead, we collect the contents of a window of width N as it slides along the trace--hence Forrest's detection algorithm is called the sliding window algorithm.
Relationship: entailment
Domain: Security and privacy
Sentence 1: When exploiting these bugs, the attack script always holds at least one reference to the object in order to avoid garbage collection, because the bottom line of any garbage­collector is that it cannot free such a object if the garbage-collection algorithm is based on refcounting, the refcount of the object is at least 1, and thus it is not collectable; if the algorithm is based on object reachability, the object is reachable from a persistent data structure, and thus not collectable.
Sentence 2: the bugs above the memory level are a problem orthogonal to the choice of the underlying garbage collection algorithms.
Relationship: entailment
Domain: Security and privacy
Sentence 1: We recognise that the total elimination of an age-related performance decrement for authentication will be difficult, given the well-known decline of long-term memory with age [11], but our intention is to explore ways in which we can ameliorate this problem.
Sentence 2: in this paper we have shown that a face­based GA system using old faces was associated with a significant improvement in older adults authentication performance.
Relationship: entailment
Domain: Security and privacy
Sentence 1: For our purposes, a voting system insider is any person or process (hereinafter entity) in whom intentional trust has been granted.
Sentence 2: an insider is an entity whose voting system-relevant behavior may reasonably be expected to be other than the most malicious possible and in whom the specific trust is codified in assignment of a designated voting system privilege, usually an access privilege to data or a process.
Relationship: entailment
Domain: Security and privacy
Sentence 1: In a proof-of-concept work published on the web [21], Shamir and Tromer explore inferring of CPU activities (e.g., patterns of CPU operations and memory access) via acoustic emanations.
Sentence 2: they investigate how acoustic emanations associated with RSA decryption and signing operations produce unique signatures per RSA private key, and how they can be used to learn the keys.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Large numbers of conclusions in privacy-preserving data mining have been obtained by researchers, most of which are based on an assumption that each party is semi-honest.
Sentence 2: a party is deemed semi-honest when that the party follows the protocol properly with the exception that it keeps a record of all its intermediate computation results and then tries to deduce further information in addition to the protocol result.
Relationship: entailment
Domain: Security and privacy
Sentence 1: The drawback of black-box server analysis is that we may not have suf.cient information to eliminate false positives and false negatives.
Sentence 2: we may not be able to reasonably generate all of the inputs the server should be tested on, and even for those inputs that we do generate, there is no reli­able way to know if the server accepts them.
Relationship: entailment
Domain: Security and privacy
Sentence 1: In practice, it is not di.cult to, on an incremental basis, compute hashes of the ciphertexts sent and received over the record layer in that phase.
Sentence 2: it is not neces­sary to store all record layer messages to input to the hash function all at once, as common programming APIs for hash functions allow the hash value to be provided incrementally.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Thus, the procedure to resolve a classi.cation problem using SVMs is: (1) choosing a kernel function, (2) setting the penalty parameter C and kernel parameters as well, if any, (3) resolving the quadratic programming problem, and (4) constructing the discriminant function from the support vectors.
Sentence 2: we view the user veri.cation problem as a two-class classi.cation problem, and the learning task is to build a classi.er based on the user mouse movements.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Table 2 and Figure 6 show the detections results of di.er­ent ensembles constructed by di.erent combinations of the feature representations, training sets and base classi.ers.
Sentence 2: e1-E4 are the ensemble methods constructed by a single classi.er with a single feature representation on dif­ferent training sets; E5-E6 are the ensemble methods con­structed by a single classi.er with diverse feature represen­tations on di.erent training sets.
Relationship: entailment
Domain: Security and privacy
Sentence 1: At runtime, TrustDroid monitors all appli­cation communications, access to common shared databases, as well as .le-system and network access, and denies any data exchange or application communication between dif­ferent domains.
Sentence 2: our framework provides the following features: Mediating IPC: We extend the Android middleware and the underlying Linux kernel to deny IPC among applications belonging to di.erent domains.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Next, we reveal the value of W to both players.
Sentence 2: we condition the game G(Y,X |V1,B)TV1 on the value of W , and consider the game G(Y,X |V1,B)TV1 |W = G(Y ,X |V1,B,W )TV1 , with parameters (k, d - 1, P(Y |V1,B,W )TV1 , P(X|V1,B,W )TV1 ) (see Subsection 6.1).
Relationship: entailment
Domain: Security and privacy
Sentence 1: The privacy property of TIS implies that Ais unable to perceive if the two tags are swapped.
Sentence 2: if Aobserves a sequence of outputs from tag j and is then given an output value B randomly extracted from tag j or tag k, A cannot determine with probability non­negligibly greater than 1/2 which tag has output the value B.
Relationship: entailment
Domain: Security and privacy
Sentence 1: MORE DISCUSSIONS Our attacks on e-banking CAPTCHAs raise the ques­tion of whether .nancial institutions should continue to use CAPTCHAs for their e-banking services or they should leave them for more secure solutions.
Sentence 2: the following ques­tion needs to be answered: can we enhance the broken e­banking CAPTCHA schemes so that they are immune to the proposed attacks?
Relationship: entailment
Domain: Security and privacy
Sentence 1: However, although our approach is based on a normal processor, this type of memory authentication can be applied to ensure the integrity of backup storage, because the memory authentication only verifies the backup storage, where is only accessed by the RAS, and the hash value is only updated by the RAS spill operation.
Sentence 2: rAS spill operation is trusted because it is transparent for software and independent from other running programs.
Relationship: entailment
Domain: Security and privacy
Sentence 1: x0,x1,...,xt,xt+1,xt+2 Denote by e1,e2 the two shortest (independent and non­zero) vectors in .. As we said above, with high probability and set X = {x0,x1,...,xt},thenwehave one of these vectors is ±(g * -h * ) and the other is the shorter of g * or h * .
Sentence 2: with high probability the original t L(X, xt+1,i) · fi = 0 and (1) Pt ft+1 - ft+2 - i=0 vectors g * ,h * belong to the set {e1,e2, ±(e1 ± e2)'}.We can using the fact that g * ,h * .
Relationship: entailment
Domain: Security and privacy
Sentence 1: However, the method with which we choose to aggregate updates into sets is different.
Sentence 2: assume for now that a router in AS a knows in advance all of the routes it will send dur­ing time interval I =[ts,te].let TI be the set of all tuples (r : a ' ) where the route r was sent by the given router to a router in a ' within the interval I.
Relationship: entailment
Domain: Security and privacy
Sentence 1: hIPSweep hSadmindBOF hDDOSDaemon hIPSweep hSadmindBOF hSadmindPing (a) A hyper-alert correlation graph HG (b) P G = precedent(hSadmindBOF , HG) hIPSweep hSadmindBOF hDDOSDaemon   hSadmindBOF hDDOSDaemon hSadmindPing   (c) SG = subsequent(hSadmindBOF ,HG) (d) CG = correlated(hSadmindBOF ,HG) Figure 1: Hyper-alerts correlation graphs However, on the negative side, our method may correlate alerts incorrectly when one attack seemingly prepares for another.
Sentence 2: our method has both a higher true correlation rate and a higher false correlation rate than JIG-SAW.
Relationship: entailment
Domain: Security and privacy
Sentence 1: So it makes sense that adding the frame condition would necessarily complicate our semantics by introducing second­result in the classical semantics of D. order quanti.ers solely for the sake of de.ning speaksfor.
Sentence 2: =p =p = = =p =p =p =p IT ID =p =p = = = = =p =p F1 F2 Figure 4: Frame conditions for Kripke semantics the accessibility relation of t1 must be a superset of the accessibility relation of t2.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Then, for any possible strategy cooperatively employed by Quentin and Wendy in the .rst i iterations, the values Ri+1,Ri+2,...,Rt look uniformly random to Quentin (and, symmetrically, Si+1,Si+2,...,St look random to Wendy).
Sentence 2: quentin and Wendy acting together cannot speed up the process in some clever way so that Quentin would learn Rj (or even distinguish it from random) in fewer than j iterations.
Relationship: entailment
Domain: Security and privacy
Sentence 1: bj is the best action that the agent can take if the state of the world is sj then the expected value of perfect information is p.·b -max p.·v.i i this is an upper limit on the total value of all possible utterances with respect to the application of criterion c. Utilitarian measures of information are expressed in terms of: if you know information x when applying criterion y to determine which action to perform then you will gain utility z over not knowing x [26].
Sentence 2: they are de.ned in the context of some decision making act they do not place an intrinsic value on information.
Relationship: entailment