nils-herrmann/clean_scirepeval huggingface dataset:
Security through amnesia: a software-based solution to the cold boot attack on disk encryption

Disk encryption has become an important security measure for a multitude of clients, including governments, corporations, activists, security-conscious professionals, and privacy-conscious individuals. Unfortunately, recent research has discovered an effective side channel attack against any disk mounted by a running machine [23]. This attack, known as the cold boot attack, is effective against any mounted volume using state-of-the-art disk encryption, is relatively simple to perform for an attacker with even rudimentary technical knowledge and training, and is applicable to exactly the scenario against which disk encryption is primarily supposed to defend: an adversary with physical access.
 While there has been some previous work in defending against this attack [27], the only currently available solution suffers from the twin problems of disabling access to the SSE registers and supporting only a single encrypted volume, hindering its usefulness for such common encryption scenarios as data and swap partitions encrypted with different keys (the swap key being a randomly generated throw-away key). We present Loop-Amnesia, a kernel-based disk encryption mechanism implementing a novel technique to eliminate vulnerability to the cold boot attack. We contribute a novel technique for shielding multiple encryption keys from RAM and a mechanism for storing encryption keys inside the CPU that does not interfere with the use of SSE. We offer theoretical justification of Loop-Amnesia's invulnerability to the attack, verify that our implementation is not vulnerable in practice, and present measurements showing our impact on I/O accesses to the encrypted disk is limited to a slowdown of approximately 2x. Loop-Amnesia is written for x86-64, but our technique is applicable to other register-based architectures. We base our work on loop-AES, a state-of-the-art open source disk encryption package for Linux.
Mining Opinion Features in Customer Reviews

It is a common practice that merchants selling products on the Web ask their customers to review the products and associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds. This makes it difficult for a potential customer to read them in order to make a decision on whether to buy the product. In this project, we aim to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we are only interested in the specific features of the product that customers have opinions on and also whether the opinions are positive or negative. We do not summarize the reviews by selecting or rewriting a subset of the original sentences from the reviews to capture their main points as in the classic text summarization. In this paper, we only focus on mining opinion/product features that the reviewers have commented on. A number of techniques are presented to mine such features. Our experimental results show that these techniques are highly effective.
Recurrent Attention Models for Depth-Based Person Identification

We present an attention-based model that reasons on human body shape and motion dynamics to identify individuals in the absence of RGB information, hence in the dark. Our approach leverages unique 4D spatio-temporal signatures to address the identification problem across days. Formulated as a reinforcement learning task, our model is based on a combination of convolutional and recurrent neural networks with the goal of identifying small, discriminative regions indicative of human identity. We demonstrate that our model produces state-of-the-art results on several published datasets given only depth images. We further study the robustness of our model towards viewpoint, appearance, and volumetric changes. Finally, we share insights gleaned from interpretable 2D, 3D, and 4D visualizations of our model's spatio-temporal attention.
Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI

High-dimensional pattern classification was applied to baseline and multiple follow-up MRI scans of the Alzheimer's Disease Neuroimaging Initiative (ADNI) participants with mild cognitive impairment (MCI), in order to investigate the potential of predicting short-term conversion to Alzheimer's Disease (AD) on an individual basis. MCI participants that converted to AD (average follow-up 15 months) displayed significantly lower volumes in a number of grey matter (GM) regions, as well as in the white matter (WM). They also displayed more pronounced periventricular small-vessel pathology, as well as an increased rate of increase of such pathology. Individual person analysis was performed using a pattern classifier previously constructed from AD patients and cognitively normal (CN) individuals to yield an abnormality score that is positive for AD-like brains and negative otherwise. The abnormality scores measured from MCI non-converters (MCI-NC) followed a bimodal distribution, reflecting the heterogeneity of this group, whereas they were positive in almost all MCI converters (MCI-C), indicating extensive patterns of AD-like brain atrophy in almost all MCI-C. Both MCI subgroups had similar MMSE scores at baseline. A more specialized classifier constructed to differentiate converters from non-converters based on their baseline scans provided good classification accuracy reaching 81.5%, evaluated via cross-validation. These pattern classification schemes, which distill spatial patterns of atrophy to a single abnormality score, offer promise as biomarkers of AD and as predictors of subsequent clinical progression, on an individual patient basis.
A Survey of Modeling and Analysis Approaches for Architecting Secure Software Systems

There has been a growing interest in investigating methodologies to support the development of secure systems in the software engineering research community. Recently, much attention has been focused on the modelling and analysis of security properties for systems at the software architecture design level. The potential benefits of this architecture level work are substantial: security flaws can be detected and removed earlier in the software development life-cycle. This reduces development time, reduces development cost, and improves the quality of the resulting system. As a result of this attention, a wide variety of approaches have been proposed in the literature. At this point, a survey for researchers involved in the problem of systematically modelling and analyzing software architecture design that have security properties would be of value to the community. This paper presents such a survey; it includes a discussion of semi-formal, formal, integrated semi-formal and formal, and aspect-oriented approaches. Comparison criteria are defined including: the kinds of notations used to model the security properties (e.g., Petri nets, temporal logic, etc.), whether the approach supports the manual or automated analysis of security properties, the specific security property modelled (e.g., authentication, role-based access control, etc.), and the kind of example system that has been used to illustrate the approach (information, distributed, etc.).
Large Scale Business Discovery from Street Level Imagery

Search with local intent is becoming increasingly useful due to the popularity of the mobile device. The creation and maintenance of accurate listings of local businesses world wide is time consuming and expensive. In this paper, we propose an approach to automatically discover businesses that are visible on street level imagery. Precise business store-front detection enables accurate geo-location of bu sinesses, and further provides input for business categoriza tion, listing generation,etc. The large variety of business categories in different countries makes this a very challen ging problem. Moreover, manual annotation is prohibitive due to the scale of this problem. We propose the use of a MultiBox  based approach that takes input image pixels and directly outputs store front bounding boxes. This end-to-end learning approach instead preempts the need for hand modelling either the proposal generation phase or the post-processing phase, leveraging large labelled trai ning datasets. We demonstrate our approach outperforms the state of the art detection techniques with a large margin in terms of performance and run-time efficiency. In the evaluation, we show this approach achieves human accuracy in the low-recall settings. We also provide an end-to-end eval uation of business discovery in the real world.
A Secure Biometrics-Based Multi-Server Authentication Protocol Using Smart Cards

Recently, in 2014, He and Wang proposed a robust and efficient multi-server authentication scheme using biometrics-based smart card and elliptic curve cryptography (ECC). In this paper, we first analyze He-Wang's scheme and show that their scheme is vulnerable to a known session-specific temporary information attack and impersonation attack. In addition, we show that their scheme does not provide strong user's anonymity. Furthermore, He-Wang's scheme cannot provide the user revocation facility when the smart card is lost/stolen or user's authentication parameter is revealed. Apart from these, He-Wang's scheme has some design flaws, such as wrong password login and its consequences, and wrong password update during password change phase. We then propose a new secure multi-server authentication protocol using biometric-based smart card and ECC with more security functionalities. Using the Burrows-Abadi-Needham logic, we show that our scheme provides secure authentication. In addition, we simulate our scheme for the formal security verification using the widely accepted and used automated validation of Internet security protocols and applications tool, and show that our scheme is secure against passive and active attacks. Our scheme provides high security along with low communication cost, computational cost, and variety of security features. As a result, our scheme is very suitable for battery-limited mobile devices as compared with He-Wang's scheme.
A brain tumor segmentation framework based on outlier detection

This paper describes a framework for automatic brain tumor segmentation from MR images. The detection of edema is done simultaneously with tumor segmentation, as the knowledge of the extent of edema is important for diagnosis, planning, and treatment. Whereas many other tumor segmentation methods rely on the intensity enhancement produced by the gadolinium contrast agent in the T1-weighted image, the method proposed here does not require contrast enhanced image channels. The only required input for the segmentation procedure is the T2 MR image channel, but it can make use of any additional non-enhanced image channels for improved tissue segmentation. The segmentation framework is composed of three stages. First, we detect abnormal regions using a registered brain atlas as a model for healthy brains. We then make use of the robust estimates of the location and dispersion of the normal brain tissue intensity clusters to determine the intensity properties of the different tissue types. In the second stage, we determine from the T2 image intensities whether edema appears together with tumor in the abnormal regions. Finally, we apply geometric and spatial constraints to the detected tumor and edema regions. The segmentation procedure has been applied to three real datasets, representing different tumor shapes, locations, sizes, image intensities, and enhancement.
Compact FPGA-based True and Pseudo Random Number Generators

Two FPGA based implementations of random number generators intended for embedded cryptographic applications are presented. The first is a true random number generator (TRNG) which employs oscillator phase noise, and the second is a bit serial implementation of a Blum Blum Shub (BBS) pseudorandom number generator (PRNG). Both designs are extremely compact and can be implemented on any FPGA or PLD device. They were designed specifically for use as FPGA based cryptographic hardware cores. The TRNG and PRNG were tested using the NIST and Diehard random number test suites.
Intelligence and security informatics for homeland security: information, communication, and transportation

Intelligence and security informatics (ISI) is an emerging field of study aimed at developing advanced information technologies, systems, algorithms, and databases for national- and homeland-security-related applications, through an integrated technological, organizational, and policy-based approach. This paper summarizes the broad application and policy context for this emerging field. Three detailed case studies are presented to illustrate several key ISI research areas, including cross-jurisdiction information sharing; terrorism information collection, analysis, and visualization; and "smart-border" and bioterrorism applications. A specific emphasis of this paper is to note various homeland-security-related applications that have direct relevance to transportation researchers and to advocate security informatics studies that tightly integrate transportation research and information technologies.
Interactive Learning for Sequential Decisions and Predictions

Sequential prediction problems arise commonly in many areas of robotics and information processing: e.g., predicting a sequence of actions over time to achieve a goal in a control task, interpreting an image through a sequence of local image patch classifications, or translating speech to text through an iterative decoding procedure. Learning predictors that can reliably perform such sequential tasks is challenging. Specifically, as predictions influence future inputs in the sequence, the datageneration process and executed predictor are inextricably intertwined. This can often lead to a significant mismatch between the distribution of examples observed during training (induced by the predictor used to generate training instances) and test executions (induced by the learned predictor). As a result, naively applying standard supervised learning methods – that assume independently and identically distributed training and test examples – often leads to poor test performance and compounding errors: inaccurate predictions lead to untrained situations where more errors are inevitable. This thesis proposes general iterative learning procedures that leverage interactions between the learner and teacher to provably learn good predictors for sequential prediction tasks. Through repeated interactions, our approaches can efficiently learn predictors that are robust to their own errors and predict accurately during test executions. Our main approach uses existing no-regret online learning methods to provide strong generalization guarantees on test performance. We demonstrate how to apply our main approach in various sequential prediction settings: imitation learning, model-free reinforcement learning, system identification, structured prediction and submodular list predictions. Its efficiency and wide applicability are exhibited over a large variety of challenging learning tasks, ranging from learning video game playing agents from human players and accurate dynamic models of a simulated helicopter for controller synthesis, to learning predictors for scene understanding in computer vision, news recommendation and document summarization. We also demonstrate the applicability of our technique on a real robot, using pilot demonstrations to train an autonomous quadrotor to avoid trees seen through its onboard camera (monocular vision) when flying at low-altitude in natural forest environments. Our results throughout show that unlike typical supervised learning tasks where examples of good behavior are sufficient to learn good predictors, interaction is a fundamental part of learning in sequential tasks. We show formally that some level of interaction is necessary, as without interaction, no learning algorithm can guarantee good performance in general.
RELATIONAL CONTRACTS AND THE THEORY OF THE FIRM

Relational contracts—informal agreements sustained by the value of future relationships—are prevalent within and between firms. We develop repeated-game models showing why and how relational contracts within firms (vertical integration) differ from those between (non-integration). We show that integration affects the parties’ temptations to renege on a given relational contract, and hence affects the best relational contract the parties can sustain. In this sense, the integration decision can be an instrument in the service of the parties’ relationship. Our approach also has implications for joint ventures, alliances, and networks, and for the role of management within and between firms. We are grateful for research support from: the Division of Research at Harvard Business School (Baker); MIT’s Sloan School, MIT’s Center for Innovation in Product Development, and the NSF (through grant SBR9809107) (Gibbons); and USC’s Marshall School (Murphy). We thank Alexander Dyck, Oliver Hart, Rebecca Henderson, Bengt Holmstrom, Edward Lazear, Patrick Legros, Jonathan Levin, Bentley MacLeod, John Matsusaka, Kevin Murdoch, Canice Prendergast, Julio Rotemberg, Steven Tadelis, Mike Waldman, Mike Whinston, the referees, and especially Benjamin Klein for helpful comments. RELATIONAL CONTRACTS AND THE THEORY OF THE FIRM
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments. This is challenging because human motion is inherently multimodal: given a history of human motion paths, there are many socially plausible ways that people could move in the future. We tackle this problem by combining tools from sequence prediction and generative adversarial networks: a recurrent sequence-to-sequence model observes motion histories and predicts future behavior, using a novel pooling mechanism to aggregate information across people. We predict socially plausible futures by training adversarially against a recurrent discriminator, and encourage diverse predictions with a novel variety loss. Through experiments on several datasets we demonstrate that our approach outperforms prior work in terms of accuracy, variety, collision avoidance, and computational complexity.
Mental animation: inferring motion from static displays of mechanical systems.

Reaction-time and eye-fixation data are analyzed to investigate how people infer the kinematics of simple mechanical systems (pulley systems) from diagrams showing their static configuration. It is proposed that this mental animation process involves decomposing the representation of a pulley system into smaller units corresponding to the machine components and animating these components in a sequence corresponding to the causal sequence of events in the machine's operation. Although it is possible for people to make inferences against the chain of causality in the machine, these inferences are more difficult, and people have a preference for inferences in the direction of causality. The mental animation process reflects both capacity limitations and limitations of mechanical knowledge.
Fast Single-image Defogging

Bad weather conditions such as fog, haze, and dust often reduce the performance of outdoor cameras. To improve the effectiveness of surveillance and in-vehicle cameras under such conditions, we propose a method based on a dark channel prior for quickly defogging images. It first estimates the intensity of the atmospheric light by searching the sky area in the foggy image. Then it estimates the transmission map by refining a coarse map from a fine map. Finally, it produces a clearer image from the foggy image by using the estimated intensity and the transmission map. When implemented on a notebook PC with a graphics processing unit (GPU), it was able to process 50 images (720 × 480 pixels) per second. It can thus be used for real-time processing of surveillance and in-vehicle system images.
e-Tourism: A Tourist Recommendation and Planning Application

e-Tourism is a tourist recommendation and planning application to assist users on the organization of a leisure and tourist agenda. First, a recommender system offers the user a list of the city places that are likely of interest to the user. This list takes into account the user demographic classification, the user likes in former trips and the preferences for the current visit. Second, a planning module schedules the list of recommended places according to their temporal characteristics as well as the user restrictions; that is the planning system determines how and when to perform the recommended activities. This is a very relevant feature that most recommender systems lack as it allows the user to have the list of recommended activities organized as an agenda, i.e. to have a totally executable plan.
Parallel Algorithms for Hierarchical Clustering

Hierarchical clustering is a common method used to determine clusters of similar data points in multidimensional spaces. O(n*) algorithms are known for this problem [3,4,11,19]. This paper reviews important results for sequential algorithms and describes previous work on parallel algorithms for hierarchical clustering. Parallel algorithms to perform hierarchical clustering using several distance metrics are then described. Optimal PRAM algorithms using n/log n processors are given for the average link, complete link, centroid, median, and minimum variance metrics. Optimal butterfly and tree algorithms using n/log n processors are given for the centroid, median, and minimum variance metrics. Optimal asymptotic speedups are achieved for the best practical algorithm to perform clustering using the single link metric on a n/log n processor PRAh4, butterfly, or tree.
DC Arc Flash Studies for Solar Photovoltaic Systems: Challenges and Recommendations

A dc arc flash hazard exists in solar photovoltaic (PV) power systems, but there is no widely accepted methodology for characterizing the severity of the hazard. Calculation methods have been proposed, and most rely on the nameplate I-V characteristic of the PV modules at standard test conditions to determine the worst case incident energy. This paper proposes to consider other factors in performing a dc arc flash hazard analysis, including possible weather conditions and variations of PV module characteristics from the datasheet ratings. It is recommended to consider two conditions when determining the worst case incident energy from a PV system: 1) the failure of all protective devices to trip within 2 s due to insufficient current and 2) the array output power exceeding the nameplate rating due to technological and environmental factors.
A Wakeup Call for Internet Monitoring Systems : The Case for Distributed Triggers

There have been a number of recent proposals for distributed monitoring infrastructures. We argue that a vital missing component from these proposals is a distributed triggering mechanism, by which nodes in the system can proactively work together to maintain invariants and constraints over the system-wide behavior. Without such a triggering component, existing proposals are doomed to poor performance, high detection latency, or both. A key research challenge in building such a triggering mechanism lies in devising solutions whereby individual nodes can independently detect (and react to) violations of constraints that are defined in terms of the aggregate behavior of nodes in the system. We discuss our early efforts in designing such a triggering system and the research challenges that lie ahead.