nils-herrmann/clean_scirepeval huggingface dataset:
Mitigating Multi-target Attacks in Hash-Based Signatures

This work introduces XMSS-T, a new stateful hash-based signature scheme with tight security. Previous hash-based signatures are facing a loss of security, linear in performance parameters such as the total tree height. Our new scheme can achieve the same security level but using hash functions with a smaller output length, which immediately leads to a smaller signature size. The same techniques also apply directly to the recent stateless hash-based signature scheme SPHINCS (Eurocrypt 2015), and the signature size is reduced as well. Being a little more specific and technical, the tight security stems from new multi-target notions of hash-function properties which we define and analyze. We show precise complexity for breaking these security properties under both classical and quantum generic attacks, thus establishing a reliable estimate for the quantum security of XMSS-T. In particular, we prove quantum query complexity tailored for cryptographic applications, which overcomes some limitations of standard techniques in quantum query complexity such as only considering worst-case complexity. Our proof techniques may be useful elsewhere. We also implement XMSS-T and compare its performance to that of XMSS (PQCrypto 2011), the most recent stateful hash-based signature scheme before our work.
Attention Assisted Discovery of Sub-Utterance Structure in Speech Emotion Recognition

Recently, attention mechanism based deep learning has gained much popularity in speech recognition and natural language processing due to its flexibility at the decoding phase. Through the attention mechanism, the relevant encoding context vectors contribute a majority portion to the construction of the decoding context, while the effect of the irrelevant ones is minimized. Inspired by this idea, a speech emotion recognition system is proposed in this work for an active selection of sub-utterance representations to better compose a discriminative utterance representation. Compared to the baseline of a model based on the uniform attention, i.e. no attention at all, an attention based model improves the weighted accuracy by an absolute of 1.46% (and relative 57.87% to 59.33%) on the emotion classification task. Moreover, the selection distribution leads to a better understanding of the sub-utterance structure in an emotional utterance.
Processing Analytical Queries over Encrypted Data

MONOMI is a system for securely executing analytical workloads over sensitive data on an untrusted database server. MONOMI works by encrypting the entire database and running queries over the encrypted data. MONOMI introduces split client/server query execution, which can execute arbitrarily complex queries over encrypted data, as well as several techniques that improve performance for such workloads, including per-row precomputation, space-efficient encryption, grouped homomorphic addition, and pre-filtering. Since these optimizations are good for some queries but not others, MONOMI introduces a designer for choosing an efficient physical design at the server for a given workload, and a planner to choose an efficient execution plan for a given query at runtime. A prototype of MONOMI running on top of Postgres can execute most of the queries from the TPC-H benchmark with a median overhead of only 1.24× (ranging from 1.03× to 2.33×) compared to an un-encrypted Postgres database where a compromised server would reveal all data.
The Cloud is Not Enough: Saving IoT from the Cloud

The Internet of Things (IoT) represents a new class of applications that can benefit from cloud infrastructure. However, the current approach of directly connecting smart devices to the cloud has a number of disadvantages and is unlikely to keep up with either the growing speed of the IoT or the diverse needs of IoT applications. In this paper we explore these disadvantages and argue that fundamental properties of the IoT prevent the current approach from scaling. What is missing is a wellarchitected system that extends the functionality of the cloud and provides seamless interplay among the heterogeneous components in the IoT space. We argue that raising the level of abstraction to a data-centric design— focused around the distribution, preservation and protection of information—provides a much better match to the IoT. We present early work on such a distributed platform, called the Global Data Plane (GDP), and discuss how it addresses the problems with the cloud-centric architecture.
An Alternative to NCD for Large Sequences, Lempel-Ziv Jaccard Distance

The Normalized Compression Distance (NCD) has been used in a number of domains to compare objects with varying feature types. This flexibility comes from the use of general purpose compression algorithms as the means of computing distances between byte sequences. Such flexibility makes NCD particularly attractive for cases where the right features to use are not obvious, such as malware classification. However, NCD can be computationally demanding, thereby restricting the scale at which it can be applied. We introduce an alternative metric also inspired by compression, the Lempel-Ziv Jaccard Distance (LZJD). We show that this new distance has desirable theoretical properties, as well as comparable or superior performance for malware classification, while being easy to implement and orders of magnitude faster in practice.
Epidermoid cyst of the clitoris: an unusual cause of clitoromegaly in a patient without history of previous female circumcision.

To describe a rare cause of clitoromegaly.



University Medical Center.



Patient.



Magnetic resonance imaging, surgical resection of the cyst, clitoroplasty.



Clitoroplasty with removal of the intradermal cyst and resolution of pain.



A 17-year-old immigrant female presented with clitoral pain associated with clitoromegaly. Epidermoid cysts are usually solitary, asymptomatic, slow-growing, proliferations of epidermal cells that are commonly present on the neck, scalp, face, or trunk. There have only been four reported cases of epidermoid cysts of the clitoris not associated with female genital mutilation. The cyst in this case was removed by local excision, and the patient's pain has resolved.
4 RFID-enabled Supply Chain Traceability : Existing Methods , Applications and Challenges

Radio Frequency Identification (RFID) technology promises to revolutionize various areas in supply chain. Recently, many researchers have investigated on how to improve the ability to track and trace a specific product along the supply chain in terms of both effectiveness and efficiency with the help of this technology. To enable traceability over the entire lifecycle a robust and seamless traceability system has to be constructed. This requires for the following three elements: (1) data model and storage scheme that allows unique identification and scalable database, (2) system framework which enables to share the traceability data between trading partners while maintaining a sovereignty over what is shared and with whom, and (3) a tracing mechanism in order to achieve end-to-end traceability and to provide the history information of products in question. Along with the studies addressing the requirements and design of traceability system architecture, applications in the real environment have also been reported. Due to the strong regulation in EU which states that food business operators shall be able to identify any person who supplied them and any business which takes food from them, RFID-enabled traceability systems are well implemented in the food supply chain. However, there exist other industries adopting RFID to enhance traceability such as pharmaceutical and aviation and even in the continuous process industry like iron ore refining. Despite the promising nature of RFID in tracking, there are several challenges to be addressed. Since an RFID tag does not require line-of-sight, multiple tags can be read simultaneously but also tag collisions may occur. Therefore there is no guarantee that a tag will be continuously detected on consecutive scans. Moreover, the use of RFID tags can be of serious threat to the privacy of information. This may facilitate the espionage of unauthorized personnel. In this chapter, we analyze the main issues of RFID-enabled traceability along the supply chain mentioned above: existing methods, applications and future challenges. Section 2 starts with pointing out the characteristics of RFID data and the requirements for RFIDenabled traceability. Subsequently, we introduce data types, storage schemes and system frameworks proposed in the existing literatures. Then, we discuss tracing methods based on the traceability system architecture. Section 3 presents current applications in real settings of both discrete and continuous production. We also discuss challenges that are preventing companies from adopting RFID for their traceability solutions in section 4. Finally, we conclude our study in section 5.
User Models for Adaptive Hypermedia and Adaptive Educational Systems

One distinctive feature of any adaptive system is the user model that represents essential information about each user. This chapter complements other chapters of this book in reviewing user models and user modeling approaches applied in adaptive Web systems. The presentation is structured along three dimensions: what is being modeled, how it is modeled, and how the models are maintained. After a broad overview of the nature of the information presented in these various user models, the chapter focuses on two groups of approaches to user model representation and maintenance: the overlay approach to user model representation and the uncertainty-based approach to user
A statistical method for robust 3D surface reconstruction from sparse data

General information about a class of objects, such as human faces or teeth, can help to solve the otherwise ill-posed problem of reconstructing a complete surface from sparse 3D feature points or 2D projections of points. We present a technique that uses a vector space representation of shape (3D morphable model) to infer missing vertex coordinates. Regularization derived from a statistical approach makes the system stable and robust with respect to noise by computing the optimal tradeoff between fitting quality and plausibility. We present a direct, noniterative algorithm to calculate this optimum efficiently, and a method for simultaneously compensating unknown rigid transformations. The system is applied and evaluated in two different fields: (1) reconstruction of 3D faces at unknown orientations from 2D feature points at interactive rates, and (2) restoration of missing surface regions of teeth for CAD-CAM production of dental inlays and other medical applications.
Place-Its: A Study of Location-Based Reminders on Mobile Phones

Context-awareness can improve the usefulness of automated reminders. However, context-aware reminder applications have yet to be evaluated throughout a person’s daily life. Mobile phones provide a potentially convenient and truly ubiquitous platform for the detection of personal context such as location, as well as the delivery of reminders. We designed Place-Its, a location-based reminder application that runs on mobile phones, to study people using location-aware reminders throughout their daily lives. We describe the design of Place-Its and a two-week exploratory user study. The study reveals that location-based reminders are useful, in large part because people use location in
Tri-state median filter for image denoising

In this work, a novel nonlinear filter, called tri-state median (TSM) filter, is proposed for preserving image details while effectively suppressing impulse noise. We incorporate the standard median (SM) filter and the center weighted median (CWM) filter into a noise detection framework to determine whether a pixel is corrupted, before applying filtering unconditionally. Extensive simulation results demonstrate that the proposed filter consistently outperforms other median filters by balancing the tradeoff between noise reduction and detail preservation.
SURVEY ON FAULT TOLERANCE IN GRID COMPUTING

Grid computing is defined as a hardware and software infrastructure that enables coordinated resource sharing within dynamic organizations. In grid computing, the probability of a failure is much greater than in traditional parallel computing. Therefore, the fault tolerance is an important property in order to achieve reliability, availability and QOS. In this paper, we give a survey on various fault tolerance techniques, fault management in different systems and related issues. A fault tolerance service deals with various types of resource failures, which include process failure, processor failure and network failures. This survey provides the related research results about fault tolerance in distinct functional areas of grid infrastructure and also gave the future directions about fault tolerance techniques, and it is a good reference for researcher.
cGAN Based Facial Expression Recognition for Human-Robot Interaction

As an emerging research topic for proximity service (ProSe), automatic emotion recognition enables the machines to understand the emotional changes of human beings which can not only facilitate natural, effective, seamless, and advanced human–robot interaction or human–computer interface but also promote emotional health. Facial expression recognition (FER) is a vital task for emotion recognition. However, significant gap between human and machine exists in FER task. In this paper, we present a conditional generative adversarial network-based approach to alleviate the intra-class variations by individually controlling the facial expressions and learning the generative and discriminative representations simultaneously. The proposed framework consists of a generator G and three discriminators (<italic>Di</italic>, <italic>Da</italic>, and <italic>Dexp</italic>). The generator G transforms any query face image into another prototypic facial expression image with other factors preserved. Armed with action units condition, the generator G pays more attention to information relevant to facial expression. Three loss functions (<inline-formula> <tex-math notation="LaTeX">$L_{I}$ </tex-math></inline-formula>, <italic>La</italic>, and <italic>Lexp</italic>) corresponding to the three discriminators (<italic>Di</italic>, <italic>Da</italic>, and <italic>Dexp</italic>) were designed to learn generative and discriminative representations. Moreover, after rendering the generated expression back to its original facial expression, cycle consistency loss is also applied to guarantee the identity and produce more constrained visual representations. Optimized by combining both synthesis and classification loss functions, the learnt representation is explicitly disentangled from other variations such as identity, head pose, and illumination. Qualitative and quantitative experimental results demonstrate the proposed FER system is effective for expression recognition.
Parallel Processing Systems for Big Data: A Survey

The volume, variety, and velocity properties of big data and the valuable information it contains have motivated the investigation of many new parallel data processing systems in addition to the approaches using traditional database management systems (DBMSs).  pioneered this paradigm change and rapidly became the primary big data processing system for its simplicity, scalability, and fine-grain fault tolerance. However, compared with DBMSs, MapReduce also arouses controversy in processing efficiency, low-level abstraction, and rigid dataflow. Inspired by MapReduce, nowadays the big data systems are blooming. Some of them follow MapReduce's idea, but with more flexible models for general-purpose usage. Some absorb the advantages of DBMSs with higher abstraction. There are also specific systems for certain applications, such as machine learning and stream data processing. To explore new research opportunities and assist users in selecting suitable processing systems for specific applications, this survey paper will give a high-level overview of the existing parallel data processing systems categorized by the data input as batch processing, stream processing, graph processing, and machine learning processing and introduce representative projects in each category. As the pioneer, the original MapReduce system, as well as its active variants and extensions on dataflow, data access, parameter tuning, communication, and energy optimizations will be discussed at first. System benchmarks and open issues for big data processing will also be studied in this survey.
Harvesting Mid-level Visual Concepts from Large-Scale Internet Images

Obtaining effective mid-level representations has become an increasingly important task in computer vision. In this paper, we propose a fully automatic algorithm which harvests visual concepts from a large number of Internet images (more than a quarter of a million) using text-based queries. Existing approaches to visual concept learning from Internet images either rely on strong supervision with detailed manual annotations or learn image-level classifiers only. Here, we take the advantage of having massive well organized Google and Bing image data, visual concepts (around 14, 000) are automatically exploited from images using word-based queries. Using the learned visual concepts, we show state-of-the-art performances on a variety of benchmark datasets, which demonstrate the effectiveness of the learned mid-level representations: being able to generalize well to general natural images. Our method shows significant improvement over the competing systems in image classification, including those with strong supervision.
CBSE as Novel Approach for IDS

This paper gives a new approach called Component based software engineering for designing an intrusion detection system. This paper also tells that before this component based software approach, other traditional approaches were used for designing intrusion detection system. But due to various drawbacks we proposed this component based approach for intrusion detection system. A comparison table is also illustrated in this paper on the basis of class diagram designed using UML language in a simulated environment. This comparison table will illustrate main difference between the traditional and component based approach and also shows that which parameters make this approach a better approach. Component based software engineering, intrusion detection system, Classes etc.
Transforming to a Quality Culture: The Henry Ford Production System

We describe the cultural transformation of the surgical pathology laboratory at Henry Ford Hospital, Detroit, MI, to one that has adopted an expectation for empowered workers to see their daily work in the context of continually learning and making effective process improvements that are designed and tested by the scientific method. This transformation has been achieved by creating an organizational and educational framework for implementing guiding principles originally systematized as the basis of lean manufacturing by our founder, Henry Ford, at the turn of the century, and incorporating the innovations of the Toyota Production System. We present novel data collection techniques to establish baseline states by which to gauge the success of changes and lessons from rapid process improvement studies. Herein, we share our experiences, lessons learned, and successes to date in the pathology-based Henry Ford Production System. We do not make changes for the sake of making them, but we never fail to make a change once it is demonstrated that the new way is better than the old way. We hold it our duty to permit nothing to stand in the way of progress. —Henry Ford1 The Laboratory as Production Line In a sense, the pathology laboratory is a production line dedicated as much to reverse manufacturing, that is, reducing human parts into their basic components for analysis, as to then manufacturing or synthesizing those pieces of data back into an informational product for subsequent therapeutic decision making. Yet, surgical pathology laboratories struggle to consistently meet 2-day turnaround times for this only product. Not even 80% of the simplest of routine biopsy specimens were completed in 1 working day in the 1993 College of American Pathologists Q-Probes study performed by 525 surgical pathology laboratories.2 Despite the throughput and efficiency pressures of modern health care delivery, that performance level in biopsy turnaround time in our own laboratory has remained largely unchanged in the subsequent 13 years. The focus on quality improvement in anatomic pathology during the last 17 years has been on the identification of benchmarks of performance that describe current practice and has attempted to satisfy accreditation requirements for continuous quality  This often has meant striving to perform at median or mean benchmark performance levels. Multi-institutional, peer-derived goals, like Q-Probes or QTracks, often set the bar too low, aiming to match mediocre performance, and are without granular data allowing insight for laboratory leaders to recognize and adopt best practices.4 Transforming to a Quality Culture The Henry Ford Production System Richard J. Zarbo, MD, DMD, and Rita D’Angelo, MS, ASQE,
Wavelet analysis of EEG for seizure detection: Coherence and phasesynchrony estimation.

This paper deals with the wavelet analysis method f or seizure detection in EEG time series and coherence estimation. The main part of the paper pr esents the basic principles of signal decomposition in connection with the EEG frequency ba nds. Wavelet analysis method has been used for detection of seizure onset. The wavelet fi ltered signal is used for the computation of spectral power ratio. The results show that our met hod can identify pre seizure, seizure, post seizure and non seizure phases. When dealing with s eizure detection and prediction problems it is important to identify seizure precursor dynamics and necessary to identify information about onset and spread of seizures. Therefore in th e second part the coherence and phase synchrony during pre seizure, seizure, post seizure an d non seizure are computed. We expect this method to provide more insight into dynamic aspects of the seizure generating process.
Improving Back-Propagation by Adding an Adversarial Gradient

The back-propagation algorithm is widely used for learning in artificial neural networks. A challenge in machine learning is to create models that generalize to new data samples not seen in the training data. Recently, a common flaw in several machine learning algorithms was discovered: small perturbations added to the input data lead to consistent misclassification of data samples. Samples that easily mislead the model are called adversarial examples. Training a ”maxout” network on adversarial examples has shown to decrease this vulnerability, but also increase classification performance. This paper shows that adversarial training has a regularizing effect also in networks with logistic, hyperbolic tangent and rectified linear units. A simple extension to the back-propagation method is proposed, that adds an adversarial gradient to the training. The extension requires an additional forward and backward pass to calculate a modified input sample, or mini batch, used as input for standard back-propagation learning. The first experimental results on MNIST show that the ”adversarial back-propagation” method increases the resistance to adversarial examples and boosts the classification performance. The extension reduces the classification error on the permutation invariant MNIST from 1.60% to 0.95% in a logistic network, and from 1.40% to 0.78% in a network with rectified linear units. Based on these promising results, adversarial back-propagation is proposed as a stand-alone regularizing method that should be further investigated.