caprion/mscinli_2_class huggingface dataset:
Domain: Software and its engineering
Premise: We can conclude that all the studied factors (pleasantness, unambiguousness, applicability) a.ect the understandability of the diagram.
Hypothesis: their guidelines suggest, e.g., that different graphics and icons should be used modestly, there should be clarity regarding the meaning of different symbols, and color should be preferred over black and white (but the number of di.er­ent colors should be restricted).
Relation: neutral
Domain: Hardware
Premise: The BDI method [14] leverages redundant patterns (e.g., zero blocks, narrow values and low dynamic range patterns) exhibited by adjacent data bytes/values within a cache block.
Hypothesis: bDI divides a cache block into several sub-blocks and represents the entire block as two base sub-blocks (a zero sub-block and the first non-zero sub-block) plus the difference information (Δ).
Relation: entailment
Domain: NeurIPS
Premise: Since the optimization problem is constrained on the unit sphere, the Euclidean space optimization methods such as SGD cannot be used to optimize our objective, because the Euclidean gradient provides the update direction in a non-curvature space, while the parameters in our model must be updated on a surface with constant positive curvature.
Hypothesis: we need to base our embedding training problem on Riemannian optimization.
Relation: reasoning
Domain: Networks
Premise: We agree with the general intuition of the principle in [9], but would reformulate it in the following way: Minimize the length of the symbol used to present a single bit as well as the processing time the transceiver needs for demodulating a single bit challenge and sending the response bit.
Hypothesis: distinguish the two possible trans­mitted bit values at the receiver within as short a time as is feasible and send an answer as fast as possible.
Relation: entailment
Domain: NeurIPS
Premise: We also performed a qualitative comparison of the latent structures inferred by the different models and found that the sparse variant of the PRGDS inferred some components that the other models did not.
Hypothesis: the sparse variant of the PRGDS is uniquely capable of inferring bursty latent structures that are highly localized in time; we visualize examples in Fig. 5.
Relation: entailment
Domain: NeurIPS
Premise: In medical trials [11], to identify the best drug for a disease, one can conduct tests in rounds, such that each round involves testing multiple candidate drugs on multiple clinical subjects (e.g., mice) simultaneously.
Hypothesis: after each round of testing, there is typically a waiting time (e.g., days) before the effects of drugs become observable to guide the design of the next round of testing.
Relation: contrasting
Domain: Security and privacy
Premise: The idea is to perform self-benchmarking of critical portions of code in order to ensure that debugging breakpoints have not been inserted, and an attacker has not tampered with critical portions of the virtual processor such as the native system calls, using timing analysis from within the V-OS firmware to detect such an attack in progress.
Hypothesis: while this concept works against some attacks, we have found this difficult to implement in production due to the native processor speed variation both between mobile devices as well as within an execution session due to multitasking.
Relation: contrasting
Domain: Software and its engineering
Premise: Patterns thus represent a conceptualization of problem solving behavior achieved by abstracting from implementations and the explicit decisions software designers have made in software development projects when dealing with many influential forces that constitute complex problems.
Hypothesis: software patterns to be newly discovered are successful mental design constructs, which have not yet been discovered as such, but are rather represented as unexplored and unconscious knowledge of expert software designers that is hidden in the software implementations.
Relation: entailment
Domain: NeurIPS
Premise: To demonstrate that ATMC achieves remarkably favorable trade-offs between robustness and model compactness, we carefully design experiments on a variety of popular datasets and models as summarized in Section 3.1.
Hypothesis: since no algorithm with exactly the same goal (adversarially robust compression) exists off-the-shelf, we craft various ablation baselines, by sequentially composing different compression strategies with the state-of-the-art adversarial training [32].
Relation: entailment
Domain: Networks
Premise: At the same time, there is a considerable amount of traffic from both B and C to D. By duplicating service D at both nodes where B and C reside, we can significantly reduce traffic footprint.
Hypothesis: if the Broker is aware of an intradomain instance of the service, the broker contacts the device that has the instance and requests that it migrates the service to the SEG using a specific transmission module that is suit­able for the underlying network topology.
Relation: neutral
Domain: Security and privacy
Premise:  However, node B downloads additional files, some of which (nodes C and F) are detected as malicious, suggesting that node B, and potentially node D downloaded from the same domain as node B, as well as all of the nodes reachable from them in the downloader graph, are likely involved in malware distribution.
Hypothesis: by analyzing the downloader graphs on a host we can identify large parts of the malware download activity, which may otherwise remain undetected.
Relation: reasoning
Domain: NeurIPS
Premise: To address this apparent dichotomy between performance and interpretability [6], researchers have developed a number of attribution methods that provide estimates of the importance of input features towards a model's output for specific types of models [4,[7][8][9][10][11][12][13][14][15], and for any machine-learning model [6,16].
Hypothesis: providing fast and accurate feature importance estimates for any machine-learning model is challenging because there exists a wide variety of intricate machine-learning models with different underlying model structures, algorithms, and decision functions, which makes it difficult to develop an optimised and unified approach to importance attribution.
Relation: contrasting
Domain: Security and privacy
Premise: To estimate regression coefficients in this model, we use a variant of Lasso called L1-regularized logistic regression [23], whose effectiveness has been established in several empirical studies across multiple domains (e.g., [5, 28]).
Hypothesis: they also show that favoring high preci­sion algorithms can yield better recall at high confidence, and that scaling output numbers may require to accept lower sta­tistical guarantees to find sufficient hypotheses.
Relation: neutral
Domain: Security and privacy
Premise: We also use Dijkstra from MiBench [8], and parallel versions of Breadth First Search (BFS), Prefix Scan, and Matrix Multiply.
Hypothesis: the SoftBound version has a much higher off-chip access rate be­cause the shadow data structure for bounds metadata resides in the off-chip memory, and must be fetched each time a bound check is performed.
Relation: neutral
Domain: NeurIPS
Premise: Intuitively, if the model knows "where" in the 2-dim data space an example lives (as defined by SFs), it can condition on slice-specific features as it makes a final, slice-aware prediction.
Hypothesis: we consider two text-based relation extraction datasets: Chemical-Disease Relations (CDR), [41], in which we identify causal links between chemical and disease entities in a dataset of PubMed abstracts, and Spouses [9], in which we identify mentions of spousal relationships using preprocessed pairs of person mentions from news articles (via Spacy [17]).
Relation: neutral
Domain: NeurIPS
Premise: In order to ensure the training and test data have different category distributions, we intentionally assign different weights to the two components.
Hypothesis: during training, the examples are drawn from N 1 with probability p, and during test, the examples are drawn from N 1 with probability 1 âˆ’ p. We examine the effectiveness of our self-critical approach varying p from 0.05 to 0.5 (i.e. 0.05, 0.1, 0.2, 0.5) (0.5 means no train/test difference).
Relation: entailment
Domain: Software and its engineering
Premise: We face a trade off in choosing Tthreshold: with a smaller value of Tthreshold, the software approach can infer in a more timely manner if a device has pending tasks and can capture shorter periods in which a device has no pending task.
Hypothesis: a smaller Tthreshold leads to more aggressive PM; however, to avoid false report of no pending task, Tthreshold needs to be greater than the largest possible interval between register accesses when a device has pending tasks.
Relation: reasoning
Domain: Software and its engineering
Premise: When a Design Pattern is detected, it is possible to identify a well defined set of software requirements it satisfies: being able to determine the Cloud Pattern that fulfills the same (or similar) requirement can lead to an automatic porting of the examined software system to a Cloud platform.
Hypothesis: using patterns characteristic expressed through a semantic based formalism, we propose a tentative procedure to assess the equivalence between Design and Cloud patterns, aiming at providing a set of possible mappings between the components belonging to different patterns.
Relation: entailment
Domain: Security and privacy
Premise: We consider an exception han­dler s address activated when the function where the exception han­dler resides gets executed for the first time.
Hypothesis: same as how pCFI instruments and patches C++ constructors, pCFI also instru­ments those functions that have exception handlers when loading the code into memory and patches the code back to its original bytes when such functions are executed for the first time.
Relation: reasoning
Domain: NeurIPS
Premise: Instead, our approach is to leverage our variational reformulation in Lemma 1 and replace the class of functions F with a rich (non-subspace) set in this new formulation, which is distinct from GMM and avoids these issues.
Hypothesis: as long as F has bounded complexity, even if its ambient dimension may be infinite, we can guarantee the consistency of our approach.
Relation: entailment
Domain: NeurIPS
Premise: Although the distinction is not fully unambiguous, there exist two prototypical families of algorithms: those that learn without an explicit model of the environment (model free), and those that first learn a model and then use it to plan a solution (model based).
Hypothesis: this has implications for Dyna-style learning, as well as for replay methods [cf.. van Hasselt et al., 2018].
Relation: neutral
Domain: Networks
Premise:  Despite the impact of the traffic arrival distributions on the goodput performance, there is no impact on the goodput-efficient routing strategy.
Hypothesis: multi-hop routing under moderate traffic loads and direct transmission under light and heavy traffic loads is the optimum choice for enhancing goodput performance in multi-hop wireless networks, independent from the traffic arrival distribution, generalizing the results in [5, 23] for the considered traffic arrival distributions.
Relation: entailment
Domain: Hardware
Premise: Unsurprisingly, ShiDianNao significantly outperforms the general purpose architectures and is, on average, 46.38 Ã— faster than the CPU and 28.94Ã— faster than the GPU.
Hypothesis: the GPU cannot take full advantage of its high computational power because the small computational kernels of the visual recognition tasks listed in Table 1 map poorly on its 2,496 hardware threads.
Relation: entailment
Domain: Networks
Premise: We set the alpha threshold to 0.05 (i.e., the model should describe 95% of the data), and we find that indeed the p-value is always lower than alpha, validating that multi­ple linear regression can properly .t our data, and therefore our multiple linear regression results can be trusted.
Hypothesis: we do that so to determine if 802.11ac is a standard that could possibly support low latency show con­trol systems in the future (Fig.
Relation: neutral
Domain: Hardware
Premise: Although cover traffic hides the exact number of dead drops accessed once or twice, an adversary can still tell roughly how many people are talking, by looking at the number of dead drops accessed twice and subtracting the average amount of noise.
Hypothesis: in the above configuration, clients use an average of 12 KB/sec (adding up to 30 GB over a month of continuous use, which may be high for a mobile phone with metered data service).
Relation: neutral
Domain: NeurIPS
Premise: Such weight-tying is generally considered to come with four major benefits: 1) it acts as a form of regularization that stabilizes training and supports generalization; 2) it significantly reduces the model size; 3) it is trivial to show that any deep network can be represented by a weight-tied deep network of equal depth and only a linear increase in width (see Appendix C); and 4) the network can be unrolled to any depth, typically with improved feature abstractions as depth increases [8,18].
Hypothesis: in practice almost all such models (and deep nets in general) are stacked, trained and evaluated by unrolling a pre-determined, fixed number of layers.
Relation: contrasting
Domain: NeurIPS
Premise: Since our low-level policy training is related to goal-conditioned HRL, we can benefit from algorithmic advances in multi-goal reinforcement learning [29,55,4,50].
Hypothesis: we extend the recently popularized goal relabeling strategy [29,4] to instructions, allowing us to relabel based on achieving a language statement that describes a region of state space, rather than relabeling based on reaching an individual state.
Relation: entailment
Domain: Hardware
Premise: The non-intrusive sensors consist of dummy analog stages and single components extracted directly from the topology of the LNA.
Hypothesis: we consider a dummy bias stage identical to the bias stage of the LNA formed by transistor M3 and resistor R1, a dummy gain stage identical to the gain stage of the LNA formed by transistors M1 and M2, a dummy diode-connected transistor identical to the transistor M1, and a dummy capacitor identical to the capacitor Cin.
Relation: entailment
Domain: NeurIPS
Premise: Let f be a lower semi-continuous convex function such that f(1) = 0, and let P and Q be two distributions over a probability space (Ω, Σ) such that P is absolutely continuous with respect to Q.
Hypothesis: we can show that for any H and for any Î“, (H, Î“)-capacity bounded differential privacy is preserved after post-processing if certain conditions about the function classes hold: Theorem 1.
Relation: neutral
Domain: Hardware
Premise: Since it is a general consensus that cleaning cost of GC algorithms may be reduced when data can be differenti­ated [11, 14] and a workload includes n + 1 types of data pages, it is of interest to study the performance of GC that exploits data locality using data grouping, which differenti­ates different types of data pages and stores them separately in different regions in an SSD.
Hypothesis: we analyze how data grouping influences the performance of GC in SSD, and how much is the influence for workloads with different degrees of locality?
Relation: entailment
Domain: Security and privacy
Premise: The results of this case study show that optimization and static obfuscating transformations (e.g., replacing with fun­damental instructions, embedding a specialized interpreter, flattening control flow and encoding arithmetic/data) have little effect on artificiality.
Hypothesis: dynamic obfuscating transfor­mations (e.g., instruction camouflage and code encryption), or a technique that inserts junk code fragments into the program, tend to increase the artificiality, which may have a significant impact on the stealth of the code.
Relation: contrasting
Domain: Software and its engineering
Premise: We assume that the ownship has the capability to perfectly estimate the geometrical configuration of the encounter, i.e., the relative position of the intruder, as well as the speed and direction of both aircraft; in practice such information is provided by different sensors as well as communication data exchanged be­tween aircraft.
Hypothesis: we discard all sources of noise and uncertainty related to the state estimation.
Relation: entailment
Domain: Software and its engineering
Premise: Patients can self-manage their drug use behavior by participating in self-help support groups [15, 18], such as Narcotics Anonymous (NA) [7], or through contact with experts and individuals with similar experience, such as the online forums on Reddit [9].
Hypothesis: many patients might still suffer from mild withdrawal symptoms in the early phase of this treatment stage and probably experience periods of relapse.
Relation: contrasting
Domain: NeurIPS
Premise: Inspired by our linear model in Section 3.1, we conjecture that the weights w learned on a dataset S can be expressed as w 1 +w 2 , where w T 1 Ï†(x) dominates the output on most test inputs and induces a simple decision boundary.
Hypothesis: it may be possible to apply uniform convergence on the function w T 1 Ï†(x) to obtain a small generalization bound.
Relation: entailment
Domain: Hardware
Premise: Rather than relying on conventional strategies, better-than worst­case design approaches, which relax timing and manage performance, will be required, spanning DRAM design, architecture (mem­ory controller) and even the system software.
Hypothesis: we advocate in this paper for a new soft yield strategy in which memory timing constraints are allowed to vary by location to rescue chips that would have failed under strict global timing constraints.
Relation: entailment
Domain: Software and its engineering
Premise: While GAs may provide acceptable performance, other al­gorithms may be better at exploiting the particular structure of a testing problem: the focus on GAs may be limiting our creativity as researchers in finding much better .ts between the testing problem and the search algorithm.
Hypothesis: gAs can scale poorly to high-dimensional problems and scalability is sensitive to the representation of the problem (see, for example, [4]).
Relation: entailment
Domain: Software and its engineering
Premise: The reason why AppScale s performance is more stable over time is because it is deployed on a set of closely con­trolled and monitored cluster of virtual machines (VMs) that use a private Infrastructure-as-a-Service (IaaS) cloud to im­plement isolation.
Hypothesis: the VMs assigned to App-Scale do not share nodes with noisy neighbors in our test environment.
Relation: entailment
Domain: Security and privacy
Premise: In the running example, a regular vehicle component may not want to share sensitive owner information with everyone the access should only be granted to police vehicles.
Hypothesis: alternatively, the parameter may be required to be constant, which means that the value must be set at the time of deployment (lines 1, 4) in this case, the certificate for the component states not only its role, but also permitted values of the parameter.
Relation: neutral
Domain: NeurIPS
Premise: Although these methods are promising, Gonen and Goldberg [13] show that the gender bias encoded in word embeddings is mostly hidden from the defined metric of projecting onto the "he-she" vector direction.
Hypothesis: words still receive implicit gender from their associations (e.g. "receptionist" is no longer gendered with respect to "he", but is still gendered with respect to "captain"). 
Relation: entailment
Domain: Security and privacy
Premise: The write_buffer argument points to a buffer that encodes all the information related to the requested operations.
Hypothesis: the Request type field encodes the type of the request (BC_TRANSACTION, in this example), the InterfaceToken field specifies the remote service (com.android.internal.telephony.ISms), and the code field specifies which of its exported functions must be invoked (sendText).
Relation: entailment
Domain: Software and its engineering
Premise: For example, assuming the aggregator has 10 ISNs, if we want to process 90% of user requests within 100 ms, then each ISN needs to reply within 100 ms with probability around 0.99.  
Hypothesis: for a total latency of 100 ms at the 90th-percentile response time, the response time of each ISN must be at most 100 ms at the 99th-percentile.
Relation: entailment
Domain: Security and privacy
Premise: In this work, we study the in­trinsic hardware characteristics of modern graphics process­ing units (GPUs) due to random manufacturing variations, and exploits the inherent randomness to generate device­specific signatures.
Hypothesis: we present a novel GPU­based hardware fingerprint scheme to generate a unique, sta­ble, physically unclonable, unpredictable, and random bit string from the inherent hardware features of a general pur­pose GPU (GPGPU).
Relation: entailment
Domain: Hardware
Premise: To address this issue, we propose to adopt wear leveling for containers with the integration of the proposed two-class bucket list.
Hypothesis: when a container is selected by procedure C ONTAINE R -ALLO C (in Algorithm 3), this container is compared with the first container of each bucket in both the compact and non-compact lists by checking their age .eld in the their container metadata records.
Relation: entailment
Domain: Software and its engineering
Premise: The distcheck tool is also being used to schedule package rebuilds within Debian in a way that is aware of unsatisfiable build-time dependencies (as opposed to deploy-time dependencies, which are the main concern of this paper).
Hypothesis: no build will be scheduled by the wanna-build scheduler, unless distcheck can show that the build-time dependencies of a given source package are currently satisfiable in the origin repository.
Relation: entailment
Domain: Security and privacy
Premise: However, in contrast to the re-registration attack which involved addition and deletion of Sybil identities, the attacker does not fully control the process of obtaining new attack edges, since an honest user must accept or interact with a Sybil identity to establish a new edge.
Hypothesis: careful attention is needed to model the capabilities of an attacker aiming to induce and exploit churn in attack edges (see Sec­tion 3.2 for a formal description of our dynamic attack edge model ).
Relation: reasoning
Domain: Security and privacy
Premise: In the ROS, data messages exchanged between the master and the node, called a Bag, Bag file can be used to play this back when you need it and repeat the same action to the Robot.
Hypothesis: the configuration environment for the ROS authentication scheme is ROS Indigo version, and Fig. 1 shows the Raspberry pi 2 environment running the master, and Fig. 2 shows the attacker's environment. 
Relation: neutral
Domain: Software and its engineering
Premise: They contain essential knowledge for developing products encompassing: the knowledge for designing concrete architectures of systems of a given application domain; development best practices; and software elements.
Hypothesis: this paper discussed the importance of an architectural standardization for variability management tools based on reference architectures in order to provide several benefits at developing these tools, such as, promoting interoperability and integration, increasing reuse of projects experiences, and establishing development best practices.
Relation: reasoning
Domain: Software and its engineering
Premise: This value is the total number of movements invoked by placing SSi at SLOTj .
Hypothesis: orion not only improves performance up to 1.61 times speedup but also resource &#38; energy efficiency, with up to 62.5% memory resource saving, and 6.7% energy reduction over the highly optimized code gen­erated by nvcc (Section 4).
Relation: neutral