nils-herrmann/clean_scirepeval huggingface dataset:
Gracewipe: Secure and Verifiable Deletion under Coercion

For users in possession of password-protected encrypted data in persistent storage (i.e., “data at rest”), an obvious problem is that the password may be extracted by an adversary through dictionary attacks or by coercing the user. Techniques such as multi-level hidden volumes with plausible deniability, or software/hardware-based full disk encryption (FDE) cannot adequately address such an attacker. For these threats, making data verifiably inaccessible in a quick fashion may be the preferred choice, specifically for users such as government/corporate agents, journalists, and human rights activists with highly confidential secrets, when caught and interrogated in a hostile territory. Using secure storage on a Trusted Platform Module (TPM) and modern CPU’s trusted execution mode (e.g., Intel TXT), we design Gracewipe to enable secure and verifiable deletion of encryption keys through a special deletion password. An attacker cannot distinguish between a deletion and real password. He can guess the real password to unlock the target encryption key only through the valid Gracewipe environment; guessing the deletion password will trigger deletion of the real key. When coerced, a user can fake compliance, and enter the deletion password; and then the user can prove to the attacker that Gracewipe has been executed and the real key is no longer available (through a TPM quote), hoping that a reasonable adversary then will find no reason to keep holding the victim, and may even release her. We implement two prototypes of Gracewipe: software-based FDE system with plausible deniability (using TrueCrypt with hidden volume), and hardware-based FDE (using a Seagate selfencrypting drive (SED)). Our choice of booting Windows at the end of a Gracewipe session (for the possibility of immediate adoption), poses some unique challenges. Through the design and prototypes of Gracewipe, we hope to raise awareness of a special but critical use-case of FDE systems.
Converting Pairing-Based Cryptosystems from Composite-Order Groups to Prime-Order Groups

We develop an abstract framework that encompasses the key properties of bilinear groups of composite order that are required to construct secure pairing-based cryptosystems, and we show how to use prime-order elliptic curve groups to construct bilinear groups with the same properties. In particular, we define a generalized version of the subgroup decision problem and give explicit constructions of bilinear groups in which the generalized subgroup decision assumption follows from the decision DiffieHellman assumption, the decision linear assumption, and/or related assumptions in prime-order groups. We apply our framework and our prime-order group constructions to create more efficient versions of cryptosystems that originally required composite-order groups. Specifically, we consider the Boneh-GohNissim encryption scheme, the Boneh-Sahai-Waters traitor tracing system, and the Katz-Sahai-Waters attribute-based encryption scheme. We give a security theorem for the prime-order group instantiation of each system, using assumptions of comparable complexity to those used in the composite-order setting. Our conversion of the last two systems to prime-order groups answers a problem posed by Groth and Sahai.
Reference Scope Identification in Citing Sentences

A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results.
A deep scattering spectrum — Deep Siamese network pipeline for unsupervised acoustic modeling

Recent work has explored deep architectures for learning acoustic features in an unsupervised or weakly-supervised way for phone recognition. Here we investigate the role of the input features, and in particular we test whether standard mel-scaled filterbanks could be replaced by inherently richer representations, such as derived from an analytic scattering spectrum. We use a Siamese network using lexical side information similar to a well-performing architecture used in the Zero Resource Speech Challenge (2015), and show a substantial improvement when the filterbanks are replaced by scattering features, even though these features yield similar performance when tested without training. This shows that unsupervised and weakly-supervised architectures can benefit from richer features than the traditional ones.
Salient Object Detection: A Survey

Detecting and segmenting salient objects in natural scenes, often referred to as salient object detection, has attracted a lot of interest in computer vision. While many models have been proposed and several applications have emerged, yet a deep understanding of achievements and issues is lacking. We aim to provide a comprehensive review of the recent progress in salient object detection and situate this field among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction. Covering 228 publications, we survey i) roots, key concepts, and tasks, ii) core techniques and main modeling trends, and iii) datasets and evaluation metrics in salient object detection. We also discuss open problems such as evaluation metrics and dataset bias in model performance and suggest future research directions.
Existential consistency: measuring and understanding consistency at Facebook

Replicated storage for large Web services faces a trade-off between stronger forms of consistency and higher performance properties. Stronger consistency prevents anomalies, i.e., unexpected behavior visible to users, and reduces programming complexity. There is much recent work on improving the performance properties of systems with stronger consistency, yet the flip-side of this trade-off remains elusively hard to quantify. To the best of our knowledge, no prior work does so for a large, production Web service.
 We use measurement and analysis of requests to Facebook's TAO system to quantify how often anomalies happen in practice, i.e., when results returned by eventually consistent TAO differ from what is allowed by stronger consistency models. For instance, our analysis shows that 0.0004% of reads to vertices would return different results in a linearizable system. This in turn gives insight into the benefits of stronger consistency; 0.0004% of reads are potential anomalies that a linearizable system would prevent. We directly study local consistency models---i.e., those we can analyze using requests to a sample of objects---and use the relationships between models to infer bounds on the others.
 We also describe a practical consistency monitoring system that tracks &phis;-consistency, a new consistency metric ideally suited for health monitoring. In addition, we give insight into the increased programming complexity of weaker consistency by discussing bugs our monitoring uncovered, and anti-patterns we teach developers to avoid.
Multi-source deep learning for information trustworthiness estimation

In recent years, information trustworthiness has become a serious issue when user-generated contents prevail in our information world. In this paper, we investigate the important problem of estimating information trustworthiness from the perspective of correlating and comparing multiple data sources. To a certain extent, the consistency degree is an indicator of information reliability--Information unanimously agreed by all the sources is more likely to be reliable. Based on this principle, we develop an effective computational approach to identify consistent information from multiple data sources. Particularly, we analyze vast amounts of information collected from multiple review platforms (multiple sources) in which people can rate and review the items they have purchased. The major challenge is that different platforms attract diverse sets of users, and thus information cannot be compared directly at the surface. However, latent reasons hidden in user ratings are mostly shared by multiple sources, and thus inconsistency about an item only appears when some source provides ratings deviating from the common latent reasons. Therefore, we propose a novel two-step procedure to calculate information consistency degrees for a set of items which are rated by multiple sets of users on different platforms. We first build a Multi-Source Deep Belief Network (MSDBN) to identify the common reasons hidden in multi-source rating data, and then calculate a consistency score for each item by comparing individual sources with the reconstructed data derived from the latent reasons. We conduct experiments on real user ratings collected from Orbitz, Priceline and TripAdvisor on all the hotels in Las Vegas and New York City. Experimental results demonstrate that the proposed approach successfully finds the hotels that receive inconsistent, and possibly unreliable, ratings.
Performance assessment of Multilateration Systems - a solution to nextgen surveillance

Multilateration (MLAT) surveillance is now being used in all types of airspace for air traffic management. MLAT can be used for airport surface movement surveillance as well as for terminal and en route surveillance, using Wide Area Multilateration (WAM). MLAT is a low-cost technology that not only has major advantages in operations and maintenance, but also provides excellent performance under all conditions, especially for countries with large geographic areas or mountainous terrain to cover. This paper focuses on performance metrics from operational system data to demonstrate that the performance of Era's MLAT systems meet surveillance requirements for all surveillance applications. This includes the key performance requirements for the various surveillance applications that are evaluated, including accuracy, coverage, update rate, integrity and availability.
Fisher Vector Faces in the Wild

Overview. The face verification and recognition domain has mostly been dominated by carefully designed representations based on features computed around numerous facial landmarks [1, 3]. On the other hand, for image classification, image representations are capable of capturing discriminative image information without any domain-specific knowledge by using densely computed features, coupled with a non-linear encoding. For instance, the high-dimensional Fisher vector encoding [6] of SIFT features  achieves state-of-the-art performance on several image classification benchmarks . In this paper, we address this discrepancy and make two contributions. First, we show that Fisher vector encoding of dense SIFT, an off-the-shelf image representation, achieves state-of-the-art face verification performance on the challenging “Labeled Faces in the Wild” (LFW) benchmark. Second, we show that the high-dimensional face representation using Fisher vector encoding is amenable to discriminative dimensionality reduction. The resulting compact descriptor has equal or better recognition accuracy and is very well suited to large-scale face recognition tasks. The overview of our face descriptor computation pipeline is shown in Fig. 2. The learnt dimensionality reduction model is visualised in Fig. 3. It is able to automatically extract the discriminative regions of the face.
The Logic of Plausible Reasoning: A Core Theory

1. o formal representation of plausible inference patterns; such OS deductions, inductions, ond analogies, thot ore frequently employed in answering every doy questions: 2. a set of parameters, such OS conditional likelihood, typicality, and similarity, that affect the certainty of people’s onswers to such questions; ond 3. a system relating the different plorisible inference patterns and the different certainty parameters.
A support vector method for optimizing average precision

Machine learning is commonly used to improve ranked retrieval systems. Due to computational difficulties, few learning techniques have been developed to directly optimize for mean average precision (MAP), despite its widespread use in evaluating such systems. Existing approaches optimizing MAP either do not find a globally optimal solution, or are computationally expensive. In contrast, we present a general SVM learning algorithm that efficiently finds a globally optimal solution to a straightforward relaxation of MAP. We evaluate our approach using the TREC 9 and TREC 10 Web Track corpora (WT10g), comparing against SVMs optimized for accuracy and ROCArea. In most cases we show our method to produce statistically significant improvements in MAP scores.
Discovering the invisible city: Location-based games for learning in smart cities

In this paper we discuss how location-based mobile games can be designed for learning in modern technology enhanced public spaces. We start with the description of the design process and we identify the main challenges faced. We elaborate the case of the game Invisible City: Rebels vs. Spies, a game to be played in a city centre using mobile devices. Through this case we highlight the adaptation of an original party game into a mobile form, the issues we faced and the key aspects conductive to learning in a smart city. It is claimed that creating mobile city games for learning is a new challenge, as our city landscapes are augmented with an increasing number of layers of digital information in which a new generation of city games are played.
Brain organization for music processing.

Research on how the brain processes music is emerging as a rich and stimulating area of investigation of perception, memory, emotion, and performance. Results emanating from both lesion studies and neuroimaging techniques are reviewed and integrated for each of these musical functions. We focus our attention on the common core of musical abilities shared by musicians and nonmusicians alike. Hence, the effect of musical training on brain plasticity is examined in a separate section, after a review of the available data regarding music playing and reading skills that are typically cultivated by musicians. Finally, we address a currently debated issue regarding the putative existence of music-specific neural networks. Unfortunately, due to scarcity of research on the macrostructure of music organization and on cultural differences, the musical material under focus is at the level of the musical phrase, as typically used in Western popular music.
A Balancing Control Strategy for a One-Wheel Pendulum Robot Based on Dynamic Model Decomposition: Simulations and Experiments

A dynamics-based posture-balancing control strategy for a new one-wheel pendulum robot (OWPR) is proposed and verified. The OWPR model includes a rolling wheel, a robot body with a steering axis, and a pendulum for lateral balancing. In constructing the dynamic model, three elements are generalized in comparison with existing robotic systems: the mass and inertia of the robot body, the “I”-type pendulum, and the steering motion. The dynamics of the robot are derived using a Lagrangian formulation to represent the torques of the wheel during the rolling, yawing, and pitching of the robot body, in terms of the control inputs. The OWPR dynamics are decomposed into state-space models for lateral balancing and steering, and the corresponding controller is designed to be adaptive to changes in the state variables. Simulations and experimental studies are presented that demonstrate and verify the efficiency of the proposed models and the control algorithm.
Privacy in electronic commerce and the economics of immediate gratification

Dichotomies between privacy attitudes and behavior have been noted in the literature but not yet fully explained. We apply lessons from the research on behavioral economics to understand the individual decision making process with respect to privacy in electronic commerce. We show that it is unrealistic to expectindividual rationality in this context. Models of self-control problems and immediate gratification offer more realistic descriptions of the decision process and are more consistent with currently available data. In particular, we show why individuals who may genuinely want to protect their privacy might not do so because of psychological distortions well documented in the behavioral literature; we show that these distortions may affect not only 'naive' individuals but also 'sophisticated' ones; and we prove that this may occur also when individuals perceive the risks from not protecting their privacy as significant.
Global Software Engineering: The Future of Socio-technical Coordination

Globally-distributed projects are rapidly becoming the norm for large software systems, even as it becomes clear that global distribution of a project seriously impairs critical coordination mechanisms. In this paper, I describe a desired future for global development and the problems that stand in the way of achieving that vision. I review research and lay out research challenges in four critical areas: software architecture, eliciting and communicating requirements, environments and tools, and orchestrating global development. I conclude by noting the need for a systematic understanding of what drives the need to coordinate and effective mechanisms for bringing it about.
Highly Efficient Mining of Overlapping Clusters in Signed Weighted Networks

In many practical contexts, networks are weighted as their links are assigned numerical weights representing relationship strengths or intensities of inter-node interaction. Moreover, the links' weight can be positive or negative, depending on the relationship or interaction between the connected nodes. The existing methods for network clustering however are not ideal for handling very large signed weighted networks. In this paper, we present a novel method called LPOCSIN (short for "Linear Programming based Overlapping Clustering on Signed Weighted Networks") for efficient mining of overlapping clusters in signed weighted networks. Different from existing methods that rely on computationally expensive cluster cohesiveness measures, LPOCSIN utilizes a simple yet effective one. Using this measure, we transform the cluster assignment problem into a series of alternating linear programs, and further propose a highly efficient procedure for solving those alternating problems. We evaluate LPOCSIN and other state-of-the-art methods by extensive experiments covering a wide range of synthetic and real networks. The experiments show that LPOCSIN significantly outperforms the other methods in recovering ground-truth clusters while being an order of magnitude faster than the most efficient state-of-the-art method.
Risk indicators and psychopathology in traumatised children and adolescents with a history of sexual abuse

Childhood sexual abuse (CSA) is widespread amongst South African (SA) children, yet data on risk factors and psychiatric consequences are limited and mixed. Traumatised children and adolescents referred to our Youth Stress Clinic were interviewed to obtain demographic, sexual abuse, lifetime trauma and psychiatric histories. Data for 94 participants (59 female, 35 male; mean age 14.25 [8.25–19] years) exposed to at least one lifetime trauma were analysed. Sexual abuse was reported in 53% of participants (42.56% females, 10.63% males) with 64% of violations committed by perpetrators known to them. Multinomial logistic regression analysis revealed female gender (P = 0.002) and single-parent families (P = 0.01) to be significant predictors of CSA (62.5%). CSA did not predict exposure to other traumas. Sexually abused children had significantly higher physical and emotional abuse subscale scores and total CTQ scores than non-abused children. Depression (33%, X 2 = 10.89, P = 0.001) and PTSD (63.8%, X 2 = 4.79, P = 0.034) were the most prevalent psychological consequences of trauma and both were significantly associated with CSA. High rates of CSA predicted high rates of PTSD in this traumatised sample. Associations we found appear consistent with international studies of CSA and, should be used to focus future social awareness, prevention and treatment strategies in developing countries.
DeepSecure: Scalable Provably-Secure Deep Learning

This paper presents DeepSecure, the an scalable and provably secure Deep Learning (DL) framework that is built upon automated design, efficient logic synthesis, and optimization methodologies. DeepSecure targets scenarios in which neither of the involved parties including the cloud servers that hold the DL model parameters or the delegating clients who own the data is willing to reveal their information. Our framework is the first to empower accurate and scalable DL analysis of data generated by distributed clients without sacrificing the security to maintain efficiency. The secure DL computation in DeepSecure is performed using Yao's Garbled Circuit (GC) protocol. We devise GC-optimized realization of various components used in DL. Our optimized implementation achieves up to 58-fold higher throughput per sample compared with the best prior solution. In addition to the optimized GC realization, we introduce a set of novel low-overhead pre-processing techniques which further reduce the GC overall runtime in the context of DL. Our extensive evaluations demonstrate up to two orders-of-magnitude additional runtime improvement achieved as a result of our pre-processing methodology.
60-GHz Circularly Polarized U-Slot Patch Antenna Array on LTCC

This communication presents a 60-GHz wideband circularly polarized (CP) U-slot patch antenna array of 4 × 4 elements on low temperature cofired ceramic (LTCC). A CP U-slot patch antenna is used as the array element to enhance the impedance bandwidth and a stripline sequential rotation feeding scheme is applied to achieve wide axial ratio (AR) bandwidth. Meanwhile, a grounded coplanar waveguide (GCPW) to stripline transition is designed for probe station measurement. The fabricated antenna array has a dimension of 14 ×16 × 1.1 mm3 . The simulated and measured impedance bandwidths, AR bandwidths, and radiation patterns are investigated and compared. Measured results show that the proposed antenna array has a wide impedance bandwidth from 50.5 GHz to 67 GHz for |S11| <; -10 dB, and a wide AR bandwidth from 54 GHz to 65.5 GHz for AR <; 3 dB. In addition, it exhibits a peak gain of 16 dBi and a beam-shaped pattern with 3-dB beam width of 20°. Moreover, its AR keeps below 3 dB within the 3-dB beam width.
Improving MQTT by Inclusion of Usage Control

Due to the increasing pervasiveness of Internet of Things (IoT) and Internet of Everything (IoE) devices, securing both their communications and operations has become of capital importance. Among the several existing IoT protocols, Message Queue Telemetry Transport (MQTT) is a widely-used general purpose one, usable in both constrained and powerful devices, which coordinates data exchanges through a publish/subscribe approach. In this paper, we propose a methodology to increase the security of the MQTT protocol, by including Usage Control in its operative workflow. The inclusion of usage control enables a fine-grained dynamic control of the rights of subscribers to access data and data-streams over time, by monitoring mutable attributes related to the subscriber, the environment or data itself. We will present the architecture and workflow of MQTT enhanced through Usage Control, also presenting a real implementation on Raspberry Pi 3 for performance