sadat2307/MSciNLI huggingface dataset:
Domain: Networks
Sentence 1: In systems larger than a couple of nodes and with a high enough number of channels, such steepest descent search leads to con.gurations in which the deviations from a complete solution to the problem are essentially point-like objects.
Sentence 2: the bulk of the system consists of nodes that do not con.ict with any of their neighbours, oc­casionally disrupted by an adjacent pair of nodes using the same channel.
Relationship: entailment
Domain: Networks
Sentence 1: The primary goal is to establish an intimate understanding of guessing, which is intuitive, yet provides a rigorous basis for guessing attacks.
Sentence 2: the new framework should be faithful (i.e., .ts the common sense of guessing attacks),  expressive (i.e., accounts for multiple guesses), and  complete (i.e., captures all guessing attacks in a symbolic setting).
Relationship: entailment
Domain: Networks
Sentence 1: On the other hand, the fraction of unidenti.ed P2P bytes depends on the .uctuation of the volume of speci.c .ows, which man­ifests itself in the time-varying lines of missed bytes.
Sentence 2: the shape of the plot of missed bytes vs. time is a.ected by the sizes of missed P2P .ows.
Relationship: entailment
Domain: Networks
Sentence 1: Therefore, multiple MPTCP sub.ows are inherently coupled with each other, resulting in potential side-channels that can be exploited to infer cross-path prop­erties.
Sentence 2: an ISP monitoring one or more paths used by an MPTCP connection can infer sensitive and pro­prietary information (e.g., level of network congestion, end­to-end TCP throughput, packet loss, network delay) about its competitors.
Relationship: entailment
Domain: Networks
Sentence 1: Let the IDs be estimated not smaller than L and not larger than R,where L and R are the extreme points of the interval of IDs in the reader range, with L, R .
Sentence 2: [0..2k - 1],L < R. the reader estimates to have in its range no tag with ID smaller than L or larger than R. This, however, does not rule out the possibility that some subinterval of [L...R] contain only IDs not present in the reader range.
Relationship: entailment
Domain: Networks
Sentence 1: We describe how the root of these problems is incomplete infor­mation sharing which we classify into scenarios of asymmet­ric information and perceived collisions.
Sentence 2: we next introduce a graph-theoretic formalism to explore the role of information sharing in ad hoc networks by gen­ 1the index must be computable using only .ow and node state and not the state of remote .ows.
Relationship: entailment
Domain: Networks
Sentence 1: But around i1 i 2 i 3 t = 0.5s , it shows that the error vector ee,, e entered the i1 i 2 i 3 synchronization stability region respectively, since then, the state variables of each node in the network is the same at any time.
Sentence 2: lorenz chaos dynamic system generated by BA scale-free model is stabilized to equilibrium point under apply adaptive feedback control at t = 0.5 s , achieve its control aim.
Relationship: entailment
Domain: Networks
Sentence 1: .i reaches a maximum value of ai when it receives the best quality qi = 0 and decreases to zero if the quality deteriorates in.nitely, i.e., qi tends to +8.
Sentence 2: we consider the following canonical form of the throughput function: -ßi qI .i(qI) = aie , (2) where AP i s throughput is characterized by a parameter ßi that captures its sensitivity to the received quality qI.
Relationship: entailment
Domain: Networks
Sentence 1: Note that this is also a reasonable assumption in the Internet, since a node has access to the destination of a given packet, and nodes are likely to have some knowledge of whether that destination is close by or not.
Sentence 2: we assume that each node Ni j has a predicate C such that if Ni j has distance of at most 2 log k + 1 hops from the victim j of the attack, then C(Ni )=TRUE, and if i is the node adjacent to the adversary, then C(Ni j )=FALSE (and hence m = 2log k + 2).
Relationship: entailment
Domain: Networks
Sentence 1: This network comes without cost on the in­frastructure and follows the idea of resource pooling by the people [2].
Sentence 2: the devices form opportunistic Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro.t or commercial advantage and that copies bear this notice and the full cita­tion on the .rst page.
Relationship: entailment
Domain: Networks
Sentence 1: Similarly, the reserved path bandwidth for tra.c class c RBc is de.ned as the minimum reserved link bandwidth for that tra.c class for all the links along the path.
Sentence 2: uB = min UBk k RBc = min RBck k  3.3 MQDSR Route Request: Admission Con­trol Bandwidth reservation and admission control is imple­mented during the route discovery process.
Relationship: entailment
Domain: Networks
Sentence 1: Even though the attack remains the same at each time interval, the normal traf.c demand matrix is time-varying, hence the observed variability in the drop rate time series.
Sentence 2: we found slightly greater packet drop rates at the network traf.c peak time, such as 12PM (GMT) in the Europe backbone and 6PM (GMT) in the US back­bone.
Relationship: entailment
Domain: Networks
Sentence 1: This paper is organized as follows: in Section B, we compare the Inktomi Climate Lab to the Web Polygvaph benchmarking too13; in Section C, we describe the designs of the log analysis, load generation, and hardware components of the Climate Lab; in Section D, we present several case studies that detail the application of our Climate Lab to various customer proxy deployments; finally, we present our conclusions in Section E. B. COMPARISONWITHPREVIOLJSWOFK  In this section, we compare our Climate Lab with Web Polygraph3, which is the most widely used benchmarking tool for proxy caches and is freely available as open­ source software.
Sentence 2: we explain why we chose not to use Web Polygraph for load generation purposes.
Relationship: entailment
Domain: Networks
Sentence 1: We examine how much geome­try information is nevertheless hidden in the communica­tion graph of the network: Assuming that the connectivity is determined by the well-known unit-disk graph model, we prove that using an extremely simple linear-time algorithm one can identify nodes on the boundaries of holes of the net­work.
Sentence 2: there is enough geometry information hidden in the connectivity structure to identify topological features in our example the holes in the network.
Relationship: entailment
Domain: Networks
Sentence 1: The main contribution of this work is showing that incorporating application awareness into tree management achieves to break the performance-to-cost gap that is inherent to methods known to date.
Sentence 2: the gap between the JIT approach (optimal in terms of state, though with potentially unacceptable interruption time) and the static approach, which does not suffer from service interruption, however maximizes state cost.
Relationship: entailment
Domain: Networks
Sentence 1: In this paper, we develop Xcast6Treemap islands(X6Ti) -ahierarchical architecture Overlay Multicast with the core is X6T.
Sentence 2: x6Ti allows interchange (without any con.gurations) be­tweenOverlay Multicast mode(end-hostsduplicateandfor- Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made ordistributedforpro.t or commercial advantage and that copies bearthisnoticeand thefull citation onthe .rstpage.Tocopy otherwise,to republish, topost on servers or to redistribute tolists, requiresprior speci.c permissionand/or afee.
Relationship: entailment
Domain: Networks
Sentence 1: Next, some cells in N0 , again chosen randomly, are moved from their TAs to new ones, until the consumed budget reaches B0.Moving acell i .N0 to a new TA is performed in a greedy man­ner.
Sentence 2: the cell is moved to the TA giving the largest improvement, if such TA exists, otherwise the cell is moved to the TA such that the increase in overhead is minimal.
Relationship: entailment
Domain: Networks
Sentence 1: Suf.ciently pervasive network scanning such as done by Con.cker reveals surprisingly detailed in­sights into global Internet behavior.
Sentence 2: geolocation of all IP source addresses of such scanning packets makes it easy to de­tect country-wide outages[10], since an entire country disappears from this category of traf.c.We identi.ed Con.cker scanning traf­.c by selecting TCP SYN packets with destination port 445 and packet size 48 bytes.
Relationship: entailment
Domain: Networks
Sentence 1: 3. a number of legacy correspondent hosts placed over the Internet.
Sentence 2: along the demonstration time, MH will connect to a WEB server CH1 at www.torvergata.tv, a backup file server CH2 at ubfsrl.ath.cx; a streaming server CH3 at vipnrj.yacast.net; a FTP server CH4 at ftp.archlinux.org and a laptop CH5 placed in another LAN running a skype client.
Relationship: entailment
Domain: Networks
Sentence 1: Environmental randomness As to environmental conditions, for each network operation (e.g., packet forwarding and routing), the GVG emulates the physical condition (e.g., air humidity and ob­stacles that affect wireless radio transmission) in a perfect manner, and precisely moves each packet from one forwarding node to an­other.
Sentence 2: the packet content is deleted from the sending node s moving tape, and the received packet content is added to the proper place of the receiving node s moving tape.
Relationship: entailment
Domain: Networks
Sentence 1: On the other hand, from our analysis of the event T, we get that the event t(ez+1)= d(ez+1) depends only of edges e with t(e) <d(ez+1), hence only of edges ei with 1 = i = z.
Sentence 2: the event t(ez+1)= d(ez+1) is fully determined by H ' .
Relationship: entailment
Domain: Networks
Sentence 1: Although our previous study [2] focused on replication methods in unstructured P2P networks for .le sharing, our objective was not only to achieve good search performance, as in previous studies on unstructured P2P networks, but also to achieve storage load balancing, as in the previous studies on structured P2P networks.
Sentence 2: we proposed three different replication methods in our previous paper: Path Random Replication, Path Adaptive Replication, and Path Adaptive Replication with Priority Level.
Relationship: entailment
Domain: Networks
Sentence 1: The network operator plans to place macro-cell, micro-cell, and pico-cell BSs to serve these user devices, where the propagation of wireless com­munication is modeled by the log-distance path loss model (suggested by the LTE speci.cation [17]).
Sentence 2: the path-loss e.ect of a macro-cell is determined by Fma = 128.1+ 37.6 · log 10D(BSi,uj ), where D(BSi,uj ) indicates the distance from BSi toauser device uj (measured in km).
Relationship: entailment
Domain: Networks
Sentence 1: The method, least squares with snap-inducing shaped residuals, or snap-inducing shaped residuals (SISR) for short, emphasizes those computed node locations which match well with ranging measurements, while de-emphasizing others.
Sentence 2: localization based on SISR willfavor those computed locations which represent good matches for a majority of measurement data, evenif these locationsmay meanalargedeviationfroma minority.
Relationship: entailment
Domain: Networks
Sentence 1: It will be for­mally de.ned in Section 4, but informally, L-local barrier coverage guarantees the detection of all crossing paths whose trajectory is con.ned to a slice (of length L)of the belt re­gion of deployment.
Sentence 2: if the bounding box that contains the entire trajectory of a crossing path, has a length at most L, then this crossing path is guaranteed to be detected by at least one (or k) sensor(s).
Relationship: entailment
Domain: Networks
Sentence 1: This is because the compiled data plane rules can identify precisely those packets that complete the queried trajecto­ries5 using the packet state, without having to collect the packets at every hop.
Sentence 2: even if 100% of data plane packets (distinguished by location or payload) even­tually satisfy the query, only 33% of them will be sent to the collector, when they egress at S3 after traversing 3 network hops.
Relationship: entailment
Domain: Networks
Sentence 1: A value closer to one means DMMT-EQ approaching to the optimum.
Sentence 2: we can show that DMMT-EQ is a constant-factor approximation algorithm with an approximation ratios bounded by h -1 .= 1 - (2) L + (l +1) · (h -1) in which variable l is an integer within 0 = l = L and the constant number h = emax/emin characterizes the dissimilitude of energy distribution over the network.
Relationship: entailment
Domain: Networks
Sentence 1: i The other error model (what we call average error ) cal­culates at each time step the average distance from the par­ticles in the cloud to the true position.
Sentence 2: the average error is equal to n1 k E ' where k Ek ' =wi||r(k) - pi(k)|| i Second error model gives a bigger error showing a signif­icant variance within the particle cloud.
Relationship: entailment
Domain: Networks
Sentence 1: Setting up an 802.11 link requires several steps: scanning available channels for AP beacons, associating to an AP, which of­ten requires authentication, and .nally running a DHCP request to con.gure the network parameters.
Sentence 2: the authentication in networks that implement enterprise security requires multipleex­changes with a remote server, resulting in long delays, which can account for more than 33%of the total connection time.
Relationship: entailment
Domain: Networks
Sentence 1: Since a single malicious administrator can cause an undesired path to be used and because it is dif.cult to detect network rout­ing policy violations, this approach allows for the possibility of data ex.ltration.
Sentence 2: a malicious administrator may cause potentially sensitive traf.c from one host in the network to be redi­rected to a machine that he controls, allowing data to be extracted in spite of a multi-con.guration approach.
Relationship: entailment
Domain: Networks
Sentence 1: Finally, it selects ad­ ditional monitors as needed to ensure that the total number of monitors is at least three (lines 14 16).
Sentence 2: optimality: It is easy to see from rules (i) (iv) that MMP only deploys monitors when needed, and thus no al­ gorithm can achieve identi.ability with fewer monitors; on the other hand, we show that the monitor placement by MMP is also su.cient, i.e., all link metrics can be iden­ 10neither of the vertices are cut-vertices.
Relationship: entailment
Domain: Networks
Sentence 1: show in [4] that there is a huge potential to reduce the energy consumption of LTE networks by using the pub­licly accessible residential femto base stations (which they name as pico base stations) to supplement the capacity of the macro network.
Sentence 2: they show that up to 70% energy saving can be achieved by a joint deployment of macro and femto base stations in the network compared to the case when only macro base stations are deployed.
Relationship: entailment
Domain: Networks
Sentence 1: However, in the context of WLAN, although multiple novel wireless access technolo­gies have been proposed and developed to o.er high band­width and guarantee quality of transmission, some de.cien­cies still remain due to the original design of WLAN-MAC layer.
Sentence 2: the performance anomaly of 802.11 is a serious issue which induces a potentially dramatic reduc­tion of the global bandwidth when one or several mobile nodes downgrade their transmission rates following the sig­nal degradation.
Relationship: entailment
Domain: Networks
Sentence 1: The results in this PDR section show that a channel-hopping MAC protocol achieves performances which are better than a single-channel solution operating over any of the 16 channels.
Sentence 2: we show that channel hopping requires 26% less nodes to be deployed in order to cover a given region (Section 4.1), and achieve up to 63% lower average ETX (Section 4.2) and 47% lower churn (Section 4.3).
Relationship: entailment
Domain: Networks
Sentence 1: Where possible, our analysis is inclusive of the entire 50 month period, and when not, we explicitly indicate sub-periods.
Sentence 2: our longitudi­nal results focus on two periods with the largest number of tests: a three-month window surrounding the 2005 coverage of the project in Slashdot [11] (approximately 1700 tests) and a comparable time window corresponding to our addi­tion of the 30 CAIDA ark [21] destination nodes in February 2009 (approximately 800 tests).
Relationship: entailment
Domain: Networks
Sentence 1: An AS is unlikely to have a peer-to-peer relationship with one of its (direct or indirect) providers.
Sentence 2: more generally, we say that AS u is a peer-provider of v if there exists an AS w E peer(v) such that u is a (direct or indirect) provider of w. u is a (direct or indirect) provider of one of v's peers.
Relationship: entailment
Domain: Networks
Sentence 1: The online heuristics are simple in structure and demonstrate excellent performance in comparison to traditional resource alloca-tion schemes such as processor sharing.
Sentence 2: our experimental results indicate that (i) using a combination of request sizes and air interface data rates is desirable, (ii) time multiplexing users in continuous bandwidth conditions is superior to code multiplexing, and (iii) when user rates are discretized, a combination of time and code multiplex- ing with a dominant time muliplexing component, results in broadly satisfactory performance with various measures.
Relationship: entailment
Domain: Networks
Sentence 1: to Sd and change at least one next-hop between the initial and the .nal con.guration (lines 7-15).
Sentence 2: among the routers that change one or more next-hops during the ¯ migration (set Vd at line 5), each router is forced to migrate after all its successors in the actual path towards d (line 11).
Relationship: entailment
Domain: Networks
Sentence 1: To capitalize on this observation, Disclosure extracts a set of temporal features that characterize the variability of client flow volume as a function of time, such that the system can discriminate between uniform client flow distributions indicative of C&#38;C servers and benign traffic that follows well-known diurnal patterns.
Sentence 2: disclosure segments a time series of client and flow volume by hour-long intervals per server si,and calculates statistical features over these.
Relationship: entailment
Domain: Networks
Sentence 1: CaptureFoundry is still under de­velopment, and is expected to support a greater variety of additional functionality in the future.
Sentence 2: future work will focus on re.ning and extending the clas­si.cation engine to support additional functionality and simplify the construction of .lters, and on improving cap­ture navigation and exploration through visualisation and GPU accelerated metric calculation.
Relationship: entailment
Domain: Networks
Sentence 1: As the number of gateways increases, the average path length between a node and its nearest gate­way decreases signi.cantly, thereby reducing path overhead and increasing aggregate throughput.
Sentence 2: for the  CDF 1 0.8 0.6 0.4 0.2 0  0 1 2 3 4 5 6 Throughput (Mbps) Figure 11: UDP throughput for paths with = 2hops for the single gateway, single .ow experiments.
Relationship: entailment
Domain: Networks
Sentence 1: The exact reduction in the tail and the corresponding capacity savings depend on the percentile of the load distribution chosen for provisioning networks and servers-- the higher the percentile, the smaller the savings.
Sentence 2: compared to over a 50% reduction in the average load, the sav- ings in network and server capacity is only 20-35% for the 99 th percentile of the load distribution.
Relationship: entailment
Domain: Networks
Sentence 1: In this paper, we present Disclosure, a large-scale, wide-area botnet detection system that incorporates a combination of novel techniques to overcome the challenges imposed by the use of NetFlow data.
Sentence 2: we identify several groups of features that allow Disclosure to reliably distinguish C&#38;C channels from benign traffic using NetFlow records (i.e., flow sizes, client access patterns, and temporal behavior).
Relationship: entailment
Domain: Networks
Sentence 1: Mean Task Completion Time (sec) 180 145  220 145 140 160 115 140 200 Worst TCT (sec) 135 Worst TCT (sec) 135180130 Mean TCT (sec) 130125 125120 140 120115  110 120 100 110 105  105 1 2 3 4 5 Number of Guest Users 1 2 3 4 5 0 Number of Guest-users 1 2 3 4 5 1 2 3 4 5Number of Guest-users DropTail UPN Protocols BTFON  Number of Guest Users DropTail UPN Protocols BTFON (a)Guest-User Mean TCT(b)Home-User Worst TCT Figure 5: Topology 1 -Increasing Guests Wego one step furthertoinvestigatethe scalabilityprop­erties of the algorithms with regard to the guest-user s .le size.
Sentence 2: we simulate one walking guest-user and we vary the amount of data to be transferred.
Relationship: entailment
Domain: Networks
Sentence 1: These tree-routing ideas are then shown to give better routing protocols for planar graphs as well.
Sentence 2: we show how to route along near-shortest paths (of length at most (1+1)times the shortest-path) with O(log2ni1) *This work was done while the author at Lucent Bell Labs and was partly supported by a DIMACS grant.
Relationship: entailment
Domain: Networks
Sentence 1: Di.erently to the literature we lever­age the concept of VMs cluster and we propose a algorithm that .nd a strong connected cluster of VMs inside the DDC and migrate them to another DC to reduce the WAN link utilization and to solve the congestion event.
Sentence 2: the key observation behind our approach is that the migration of single VMs, completely ignores pos­sible information about amount of tra.c between the VM and the others within the DCs.
Relationship: entailment
Domain: Networks
Sentence 1: The disk centered at a node v of radius r is denoted by B(v,r).
Sentence 2: for a .nite planar point set V and a number r> 0, the r-disk graph on V is a simplegeometricgraph on V in which there is an edge between two nodes if and only if their distance is at most r. a 1-disk graph is referred to as unit-disk graph.
Relationship: entailment
Domain: Networks
Sentence 1: That is, all snapshot data s utility drops out exponentially after generated.4 Based on above, we can as­sociate each snapshot i symbol decoded at time t a marginal utility gain a t-ti ui(ri(t)) where ti is the time the snapshot i symbol is .rst generated.
Sentence 2: a new decoded symbol s contribution on utility is simply a function of the number of decoded symbols of that snapshot multiplying a discount factor.
Relationship: entailment
Domain: Networks
Sentence 1: The energy consumed by a sensor node was calculated by using the model presented in [4], which is based on the Chipcon CC2420 radio transceiver [5].
Sentence 2: the model assumes the following radio states: transmit, receive, idle (the transceiver is on, but it is not transmitting nor receiving, i.e., it is monitoring the channel) and sleep (the transceiver is off and can be switched back on quickly).
Relationship: entailment
Domain: Networks
Sentence 1: Since this leakage can be characterized for a given hardware, the adjacent subcar­rier detection can be tackled with proper thresholding.
Sentence 2: the adjacent subcarrier would be considered ac­tive only if the SNR on this subcarrier is greater than 10 + t , where t is an estimate derived from sensing the channel in the preceding DIFS duration.
Relationship: entailment
Domain: Networks
Sentence 1: Note, however, that even though we increased the rate of wire growth, the total number of switches per node remain asymptotically constant (See Table 1): 1111 Tsw =2+1+1+ + + + ... < 6 (24) 2244 Which makes: Bsw(p =0.75) < (2 · Fc · I + 13) · C (25) This property holds for any p< 1.0.
Sentence 2: given su.­ciently large N, we can approximate any p by programming the stage-to-stage growth rate, and the number of switches per compute block remains asymptotically constant.
Relationship: entailment