nils-herrmann/clean_scirepeval huggingface dataset:
Financial fraud detection using social media crowdsourcing

Detecting financial fraud is a daunting challenge for banks and credit card companies due to massive amount of transaction data and wide diversity of user behaviors . In recent years social media has demonstrated the capability of crowdsourcing in a broad range of applications, e.g., disseminating breaking news, launching marketing campaigns, and tracking the flu , , . Inspired by these novel applications, this paper explores the benefits of social media crowdsourcing, in particularly the tweets, re-tweets and comments from Twitter online social network for effectively detecting financial fraud events.
Analyzing inter-application communication in Android

Modern smartphone operating systems support the development of third-party applications with open system APIs. In addition to an open API, the Android operating system also provides a rich inter-application message passing system. This encourages inter-application collaboration and reduces developer burden by facilitating component reuse. Unfortunately, message passing is also an application attack surface. The content of messages can be sniffed, modified, stolen, or replaced, which can compromise user privacy. Also, a malicious application can inject forged or otherwise malicious messages, which can lead to breaches of user data and violate application security policies.
 We examine Android application interaction and identify security risks in application components. We provide a tool, ComDroid, that detects application communication vulnerabilities. ComDroid can be used by developers to analyze their own applications before release, by application reviewers to analyze applications in the Android Market, and by end users. We analyzed 20 applications with the help of ComDroid and found 34 exploitable vulnerabilities; 12 of the 20 applications have at least one vulnerability.
Soft robotics : Biological inspiration , state of the art , and future research

Soft robotics: Biological inspiration, state of the art, and future research Deepak Trivedi a; Christopher D. Rahn a; William M. Kier b; Ian D. Walker c a Department of Mechanical and Nuclear Engineering, The Pennsylvania State University, University Park, PA, USA b Department of Biology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA c Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA
Comprehensive Database for Facial Expression Analysis

Within the past decade, significant effort has occurred in developing methods of facial expression analysis. Because most investigators have used relatively limited data sets, the generalizability of these various methods remains unknown. We describe the problem space for facial expression analysis, which includes level of description, transitions among expression, eliciting conditions, reliability and validity of training and test data, individual differences in subjects, head orientation and scene complexity, image characteristics, and relation to non-verbal behavior. We then present the CMU-Pittsburgh AU-Coded Face Expression Image Database, which currently includes 2105 digitized image sequences from 182 adult subjects of varying ethnicity, performing multiple tokens of most primary FACS action units. This database is the most comprehensive test-bed to date for comparative studies of facial expression
Deep-based Ingredient Recognition for Cooking Recipe Retrieval

Retrieving recipes corresponding to given dish pictures facilitates the estimation of nutrition facts, which is crucial to various health relevant applications. The current approaches mostly focus on recognition of food category based on global dish appearance without explicit analysis of ingredient composition. Such approaches are incapable for retrieval of recipes with unknown food categories, a problem referred to as zero-shot retrieval. On the other hand, content-based retrieval without knowledge of food categories is also difficult to attain satisfactory performance due to large visual variations in food appearance and ingredient composition. As the number of ingredients is far less than food categories, understanding ingredients underlying dishes in principle is more scalable than recognizing every food category and thus is suitable for zero-shot retrieval. Nevertheless, ingredient recognition is a task far harder than food categorization, and this seriously challenges the feasibility of relying on them for retrieval. This paper proposes deep architectures for simultaneous learning of ingredient recognition and food categorization, by exploiting the mutual but also fuzzy relationship between them. The learnt deep features and semantic labels of ingredients are then innovatively applied for zero-shot retrieval of recipes. By experimenting on a large Chinese food dataset with images of highly complex dish appearance, this paper demonstrates the feasibility of ingredient recognition and sheds light on this zero-shot problem peculiar to cooking recipe retrieval.
Toward discovery science of human brain function.

Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's "functional connectome." Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/.
Event Tactic Analysis Based on Broadcast Sports Video

Most existing approaches on sports video analysis have concentrated on semantic event detection. Sports professionals, however, are more interested in tactic analysis to help improve their performance. In this paper, we propose a novel approach to extract tactic information from the attack events in broadcast soccer video and present the events in a tactic mode to the coaches and sports professionals. We extract the attack events with far-view shots using the analysis and alignment of web-casting text and broadcast video. For a detected event, two tactic representations, aggregate trajectory and play region sequence, are constructed based on multi-object trajectories and field locations in the event shots. Based on the multi-object trajectories tracked in the shot, a weighted graph is constructed via the analysis of temporal–spatial interaction among the players and the ball. Using the Viterbi algorithm, the aggregate trajectory is computed based on the weighted graph. The play region sequence is obtained using the identification of the active field locations in the event based on line detection and competition network. The interactive relationship of aggregate trajectory with the information of play region and the hypothesis testing for trajectory temporal–spatial distribution are employed to discover the tactic patterns in a hierarchical coarse-to-fine framework. Extensive experiments on FIFA World Cup 2006 show that the proposed approach is highly effective.
Some attacks against vehicles' passive entry security systems and their solutions

Recently, several companies have introduced passive entry systems for automotive applications. These systems are intended to increase user comfort by eliminating the requirement that the user has to reach for the customer identification device (CID), a credit card like tool, to gain access to the vehicle compartment. While this extra level of comfort is a desirable feature, especially in luxury vehicles, it introduces several key attacks against the system. This paper describes several techniques of potential attacks against the passive entry system and proposes solutions to protect the vehicle from such attacks.
Cryptobotics: Why Robots Need Cyber Safety

With the expected introduction of robots into our daily lives, providing mechanisms to avoid undesired attacks and exploits in robot communication software is becoming increasingly required. Just as during the beginnings of the computer age (Pfleeger and Pfleeger, 2002), robotics is established in a “happy naivety,” where security rules against external attacks are not adopted, assuming that robotics knowledgeable people are well intended. While this may have been true in the past, the mass adoption of robots will increase the possibilities of attacks. This fact is especially relevant in defense, medical and other critical fields involving humans, where tampering can result in serious bodily harm and/or privacy invasions. For these reasons, we consider that researchers and industry should deploy efforts in cyber safety and acquire good practices when developing and distributing robot software. We propose the term Cryptobotics as a unifying term for research and applications of computer and microcontrollers’ security measures in robotics.
PH2 - A dermoscopic image database for research and benchmarking

The increasing incidence of melanoma has recently promoted the development of computer-aided diagnosis systems for the classification of dermoscopic images. Unfortunately, the performance of such systems cannot be compared since they are evaluated in different sets of images by their authors and there are no public databases available to perform a fair evaluation of multiple systems. In this paper, a dermoscopic image database, called PH2, is presented. The PH2 database includes the manual segmentation, the clinical diagnosis, and the identification of several dermoscopic structures, performed by expert dermatologists, in a set of 200 dermoscopic images. The PH2 database will be made freely available for research and benchmarking purposes.
Understanding Availability

This paper addresses a simple, yet fundamental question in the design of peer-to-peer systems: What does it mean when we say “availability” and how does this understanding impact the engineering of practical systems? We argue that existing measurements and models do not capture the complex timevarying nature of availability in today’s peer-to-peer environments. Further, we show that unforeseen methodological shortcomings have dramatically biased previous analyses of this phenomenon. As the basis of our study, we empirically characterize the availability of a large peer-to-peer system over a period of 7 days, analyze the dependence of the underlying availability distributions, measure host turnover in the system, and discuss how these results may affect the design of high-availability peer-to-peer services.
Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition

Face recognition has witnessed great progress in recent years, mainly attributed to the high-capacity model designed and the abundant labeled data collected. However, it becomes more and more prohibitive to scale up the current million-level identity annotations. In this work, we show that unlabeled face data can be as effective as the labeled ones. Here, we consider a setting closely mimicking the real-world scenario, where the unlabeled data are collected from unconstrained environments and their identities are exclusive from the labeled ones. Our main insight is that although the class information is not available, we can still faithfully approximate these semantic relationships by constructing a relational graph in a bottom-up manner. We propose Consensus-Driven Propagation (CDP) to tackle this challenging problem with two modules, the “committee” and the “mediator”, which select positive face pairs robustly by carefully aggregating multi-view information. Extensive experiments validate the effectiveness of both modules to discard outliers and mine hard positives. With CDP, we achieve a compelling accuracy of 78.18% on MegaFace identification challenge by using only 9% of the labels, comparing to 61.78% when no unlabeled data are used and 78.52% when all labels are employed.
Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking

The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. These limitations add significantly to development costs and make cross-domain, multi-lingual dialogue systems intractable. Moreover, human languages are context-aware. The most natural response should be directly learned from data rather than depending on predefined syntaxes or rules. This paper presents a statistical language generator based on a joint recurrent and convolutional neural network structure which can be trained on dialogue act-utterance pairs without any semantic alignments or predefined grammar trees. Objective metrics suggest that this new model outperforms previous methods under the same experimental conditions. Results of an evaluation by human judges indicate that it produces not only high quality but linguistically varied utterances which are preferred compared to n-gram and rule-based systems.
A Novel Bayesian Framework for Discriminative Feature Extraction in Brain-Computer Interfaces

As there has been a paradigm shift in the learning load from a human subject to a computer, machine learning has been considered as a useful tool for Brain-Computer Interfaces (BCIs). In this paper, we propose a novel Bayesian framework for discriminative feature extraction for motor imagery classification in an EEG-based BCI in which the class-discriminative frequency bands and the corresponding spatial filters are optimized by means of the probabilistic and information-theoretic approaches. In our framework, the problem of simultaneous spatiospectral filter optimization is formulated as the estimation of an unknown posterior probability density function (pdf) that represents the probability that a single-trial EEG of predefined mental tasks can be discriminated in a state. In order to estimate the posterior pdf, we propose a particle-based approximation method by extending a factored-sampling technique with a diffusion process. An information-theoretic observation model is also devised to measure discriminative power of features between classes. From the viewpoint of classifier design, the proposed method naturally allows us to construct a spectrally weighted label decision rule by linearly combining the outputs from multiple classifiers. We demonstrate the feasibility and effectiveness of the proposed method by analyzing the results and its success on three public databases.
A Novel AC – DC Step-Up Converter For Energy Harvesting

The conventional two-stage power converters with bridge rectifiers are inefficient and may not be practical for the low-voltage microgenerators. This paper presents an efficient ac-to-dc power converter that avoids the bridge rectification and directly converts the low ac input voltage to the required high dc output voltage at a higher efficiency. The proposed converter consists of a boost converter in parallel with a buck– boost converter, which are operated in the positive half cycle and negative half cycle, respectively. Detailed analysis of the converter is carried out to obtain relations between the power, circuit parameters, and duty cycle of the converter. Based on the analysis, control schemes are proposed to operate the converter. Design guidelines are presented for selecting the converter component and control parameters. A self-starting circuit is proposed for independent operation of the converter. Detailed loss calculation of the converter is carried out. Simulation results are presented to validate the proposed converter topology and control schemes.
The “New” Science of Networks

In recent years, the analysis and modeling of networks, and also networked dynamical systems, have been the subject of considerable interdisciplinary interest, yielding several hundred papers in physics, mathematics, computer science, biology, economics, and sociology journals (Newman 2003c), as well as a number of books (Barabasi 2002, Buchanan 2002, Watts 2003). Here I review the major findings of this emerging field and discuss briefly their relationship with previous work in the social and mathematical sciences.
Design and Evaluation of a Compiler Algorithm for Prefetching

Software-controlled data prefetching is a promising technique for improving the performance of the memory subsystem to match today’s high-performance processors. While prefetching is useful in hiding the latency, issuing prefetches incurs an instruction overhead and can increase the load on the memory subsystem. As a result, care must be taken to ensure that such overheads do not exceed the benefits. This paper proposes a compiler algorithm to insert prefetch instructions into code that operates on dense matrices. Our algorithm identifies those references that are likely to be cache misses, and issues prefetches only for them. We have implemented our algorithm in the SUIF (Stanford University Intermediate Form) optimizing compiler. By generating fully functional code, we have been able to measure not only the improvements in cache miss rates, but also the overall performance of a simulated system. We show that our algorithm significantly improves the execution speed of our benchmark programs—some of the programs improve by as much as a factor of two. When compared to an algorithm that indiscriminately prefetches all array accesses, our algorithm can eliminate many of the unnecessary prefetches without any significant decrease in the coverage of the cache misses.
Pseudoangiomatous Stromal Hyperplasia: A Rare Cause of Idiopathic Gigantomastia

1 Adam J. Oppenheimer, MD Fiala Aesthetics Orlando, Fla. Daniel C. Oppenheimer, MD Department of Imaging Sciences, University of Rochester Rochester, N.Y. Thomas G. S. Fiala, MD Fiala Aesthetics, Altamonte Springs, Fla. Sharon Noori, MD Health First, Department of General Surgery, Holmes Regional Medical Center Melbourne,  W read with interest Roy et al’s1 case report of a patient with pseudoangiomatous stromal hyperplasia (PASH) causing gigantomastia. We report a similar case in a 29-year-old woman with rapidly progressing macromastia , who also complained of cyclical breast swelling and multiple palpable breast masses, with biopsy proven PASH. Although we commend the aesthetic result achieved by Roy et al1, we differ in our algorithm. Because of the propensity for PASH to recur and owing to its inherently diffuse involvement within the breast tissues, we opted for a more aggressive approach: bilateral Wise-pattern mastectomy with immediate tissue expander reconstruction. At the time of the mastectomy, several large (5–7 cm) discrete breast masses were identified, one of which was histopathologically consistent with a benign phyllodes tumor (a fibroepithelial stromal tumor with malignant potential).2 Reconstruction proceeded using the Wise pattern: the lower pole of the breast was deepithelialized and used as soft-tissue reinforcement, reapproximating the dermal edge against the cut edge of the pectoralis major muscle, in place of an alloplastic dermal tissue matrix.3 The patient’s expansion proceeded uneventfully, and no chemoradiation was recommended by the institutional tumor board. She underwent expander–implant exchange using 425-cc Sientra (Santa Barbara, Calif.) textured round base anatomic devices bilaterally and is without complications now 3 months postoperatively . Although PASH is rare, and most commonly a benign clinical entity, there have been reports of malignancy arising within PASH.4 Indeed, in our patient, a phyllodes tumor was found as a synchronous mass. Although benign, this lesion does harbor malignant potential. Furthermore, recurrence rates up to 22% have been reported in the literature for PASH.5 Therefore, we feel that a more aggressive treatment plan is indicated when PASH is identified. Given the abundance of breast skin in these patients, a Wisepattern mastectomy with expander–implant based reconstruction is a viable and aesthetically pleasing option, which also obviates the burdensome need for ongoing oncological surveillance in these patients.
CHERI: A Hybrid Capability-System Architecture for Scalable Software Compartmentalization

CHERI extends a conventional RISC Instruction-Set Architecture, compiler, and operating system to support fine-grained, capability-based memory protection to mitigate memory-related vulnerabilities in C-language TCBs. We describe how CHERI capabilities can also underpin a hardware-software object-capability model for application compartmentalization that can mitigate broader classes of attack. Prototyped as an extension to the open-source 64-bit BERI RISC FPGA soft-core processor, Free BSD operating system, and LLVM compiler, we demonstrate multiple orders-of-magnitude improvement in scalability, simplified programmability, and resulting tangible security benefits as compared to compartmentalization based on pure Memory-Management Unit (MMU) designs. We evaluate incrementally deployable CHERI-based compartmentalization using several real-world UNIX libraries and applications.
Design of Multiple-Level Hybrid Classifier for Intrusion Detection System

As the number of networked computers grows, intrusion detection is an essential component in keeping networks secure. However, constructing and maintaining a misuse detection system is very labor-intensive since attack scenarios and patterns need to be analyzed and categorized, and the corresponding rules and patterns need to be carefully hand-coded. Thus, data mining can be used to ease this inconvenience. This paper proposes a multiple-level hybrid classifier, an intrusion detection system that uses a combination of tree classifiers and clustering algorithms to detect intrusions. Performance of this new algorithm is compared to other popular approaches such as MADAM ID and 3-level tree classifiers, and significant improvement has been achieved from the viewpoint of both high intrusion detection rate and reasonably low false alarm rate