nils-herrmann/clean_scirepeval huggingface dataset:
Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues

Intrusion Detection Systems (IDSs) are one of the key components for securing computing infrastructures. Their objective is to protect against attempts to violate defense mechanisms. Indeed, IDSs themselves are part of the computing infrastructure, and thus they may be attacked by the same adversaries they are designed to detect. This is a relevant aspect, especially in safety-critical environments, such as hospitals, aircrafts, nuclear power plants, etc. To the best of our knowledge, this survey is the first work to present an overview on adversarial attacks against IDSs. In particular, this paper will provide the following original contributions: (a) a general taxonomy of attack tactics against IDSs; (b) an extensive description of how such attacks can be implemented by exploiting IDS weaknesses at different abstraction levels (c) for each attack implementation, a critical investigation of proposed solutions and open points. Finally, this paper will highlight the most promising research directions for the design of adversary-aware, harder-to-defeat IDS solutions. To this end, we leverage on our research experience in the field of intrusion detection, as well as on a thorough investigation of the relevant related works published so far.
Highly efficient neural conversion of human ES and iPS cells by dual inhibition of SMAD signaling

Current neural induction protocols for human embryonic stem (hES) cells rely on embryoid body formation, stromal feeder co-culture or selective survival conditions. Each strategy has considerable drawbacks, such as poorly defined culture conditions, protracted differentiation and low yield. Here we report that the synergistic action of two inhibitors of SMAD signaling, Noggin and SB431542, is sufficient to induce rapid and complete neural conversion of >80% of hES cells under adherent culture conditions. Temporal fate analysis reveals the appearance of a transient FGF5+ epiblast-like stage followed by PAX6+ neural cells competent to form rosettes. Initial cell density determines the ratio of central nervous system and neural crest progeny. Directed differentiation of human induced pluripotent stem (hiPS) cells into midbrain dopamine and spinal motoneurons confirms the robustness and general applicability of the induction protocol. Noggin/SB431542-based neural induction should facilitate the use of hES and hiPS cells in regenerative medicine and disease modeling and obviate the need for protocols based on stromal feeders or embryoid bodies.
EzMail: using information visualization techniques to help manage email

Originally designed as a messaging application, email is now used for a wide range of functions. Email clients however have not progressed correspondingly to support users with email management. This work describes the design of EzMail, an email visualization tool that runs in conjunction with an email client and creates a multi-view interface to assist with email management. Our primary focus is to group and visualize messages as components of threads to provide contextual information and conversational history. We have developed visualizations for individual messages, message properties, and messages in a thread. Message annotation is incorporated, along with a capability for replying simultaneously to several messages. A small user survey and a user study provided information regarding users' management of their messages and threads, and a comparative evaluation of EzMail with respect to Microsoft Outlook.
An Overview on Evaluating and Predicting Scholarly Article Impact

Scholarly article impact reflects the significance of academic output recognised by academic peers, and it often plays a crucial role in assessing the scientific achievements of researchers, teams, institutions and countries. It is also used for addressing various needs in the academic and scientific arena, such as recruitment decisions, promotions, and funding allocations. This article provides a comprehensive review of recent progresses related to article impact assessment and prediction. The review starts by sharing some insight into the article impact research and outlines current research status. Some core methods and recent progress are presented to outline how article impact metrics and prediction have evolved to consider integrating multiple networks. Key techniques, including statistical analysis, machine learning, data mining and network science, are discussed. In particular, we highlight important applications of each technique in article impact research. Subsequently, we discuss the open issues and challenges of article impact research. At the same time, this review points out some important research directions, including article impact evaluation by considering Conflict of Interest, time and location information, various distributions of scholarly entities, and rising stars.
A polarimetric 76–79 GHz radar-frontend for target classification in automotive use

Autonomous driving requires precise detection of the environment and correct classification of all targets. W-band radar technology provides high accuracy object detection in all weather conditions, both day and night. Target classification can be improved by evaluating polarimetric scattering information, where it is in particular promising to work with cicularly polarized waves. Both circular polarizations, left-handed circular (LHC) and right-handed circular (RHC), are necessary with a high cross polarization discrimination (XPD). A particularly high XPD can be achieved by hollow waveguide technology. For that purpose, a radar-frontend with corrugated horn antennas has been designed, manufactured and tested. LHC and RHC data is then entered into an algorithm which takes into account switched transmitter technology, fast-ramp frequency-modulated continuous-wave (FMCW) and digital beamforming. Polarimetric radar images are generated, allowing for discrimination and classification of different targets.
Anatomical variation in arterial supply of the mandible with special regard to implant placement.

The aim of this study was to explore the anatomical variation of the sublingual artery in relation to the mandible, and provide preoperative information to avoid hemorrhagic complications of implant placement. One hundred adult human cadavers injected with red latex were examined. The main arterial supply to the anterior mandible is the sublingual artery as a branch of the lingual or submental artery. This artery exhibited three notable branches, termed ascending, middle and descending. In 73% of cases the sublingual artery originated from the lingual artery to give ascending (72%), middle (98%) and descending (54%) branches. In the remaining 27 cases the sublingual artery originated from the submental artery, giving ascending (69%), middle (98%) and descending (50%) branches. It was possible to identify anastomotic patterns between the sublingual and inferior labial arteries in 52%, sublingual and submental arteries in 40%, and submental and inferior labial arteries in 54% of cases. Of the branches supplying the floor and gingival mucosa of the anterior mandible, mucosal branches of the sublingual were found in the territory of the lateral incisor in 72%, canine in 62%, and first premolar in 81% of cases. Disruption of these anastomoses by implant placement could cause life-threatening hemorrhages. These data should prove useful in the placement of mandibular implants.
Data-Driven Morphological Analysis and Disambiguation for Morphologically Rich Languages and Universal Dependencies

Parsing texts into universal dependencies (UD) in realistic scenarios requires infrastructure for morphological analysis and disambiguation (MA&D) of typologically different languages as a first tier. MA&D is particularly challenging in morphologically rich languages (MRLs), where the ambiguous space-delimited tokens ought to be disambiguated with respect to their constituent morphemes. Here we present a novel, language-agnostic, framework for MA&D, based on a transition system with two variants, word-based and morpheme-based, and a dedicated transition to mitigate the biases of variable-length morpheme sequences. Our experiments on a Modern Hebrew case study outperform the state of the art, and we show that the morpheme-based MD consistently outperforms our word-based variant. We further illustrate the utility and multilingual coverage of our framework by morphologically analyzing and disambiguating the large set of languages in the UD treebanks.
A Novel Design of $4 \times 4$ Butler Matrix With Relatively Flexible Phase Differences

This letter presents a novel topology of a 4 ×4 Butler matrix, which can realize relatively flexible phase differences at the output ports. The proposed Butler matrix employs couplers with arbitrary phase-differences to replace quadrature couplers in the conventional Butler matrix. By controlling the phase differences of the applied couplers, the progressive phase differences among output ports of the proposed Butler matrix can be relatively flexible. To facilitate the design, closed-form design equations are derived and presented. For verifying the design concept, a planar 4×4 Butler matrix with four unique progressive phase differences ( - 30<sup>°</sup>, + 150<sup>°</sup>, - 120<sup>°</sup>, and + 60<sup>°</sup>) is designed and fabricated. At the operating frequency, the amplitude imbalance is less than 0.75 dB, and the phase mismatch is within ±6<sup>°</sup>. The measured return loss is better than 16 dB, and the isolation is better than 18 dB. The bandwidth with 10 dB return loss is about 15%.
Solving job-shop scheduling problems by genetic algorithm

Job-shop Scheduling Problem (JSP) is one of extremely hard problems because it requires very large combinatorial search space and the precedence constraint between machines. The traditional algorithm used to solve the problem is the branch-andbound method, which takes considerable computing time when the size of problem is large. W e propose a new method for solving JSP using Genetic Algorithm (GA) and demonstrate its efficiency by the standard benchmark of job-shop scheduling problems. Some important points of G A are how t o represent the schedules as an individuals and to design the genetic operators for the representation in order t o produce better results.
Cloud Forensics: Concepts, Issues, and Challenges

Cloud Computing is becoming so popular among organizations, promising simplicity and delivering utilities based on virtualization technologies. Convenience, availability, elasticity, large storage capacity, speed, scalability, and on-demand network access are some of the attractions of the cloud computing. The adoption of cloud computing solution is increasing rapidly which makes it inevitable for digital forensics not to follow since major potential security risks are surrounded this new technology. This study provides an overview of cloud forensics including the issues and the existing challenges in order to give better future prospects and also offers some steps to be taken to overcome these challenges.
Semantics-Driven Recognition of Collocations Using Word Embeddings

L2 learners often produce “ungrammatical” word combinations such as, e.g., *give a suggestion or *make a walk. This is because of the “collocationality” of one of their items (the base) that limits the acceptance of collocates to express a specific meaning (‘perform’ above). We propose an algorithm that delivers, for a given base and the intended meaning of a collocate, the actual collocate lexeme(s) (make / take above). The algorithm exploits the linear mapping between bases and collocates from examples and generates a collocation transformation matrix which is then applied to novel unseen cases. The evaluation shows a promising line of research in collocation discovery.
Attacking the Elliptic Curve Discrete Logarithm Problem

Definition 1.1. Given a finite abelian group G written multiplicatively and elements b and g in G, the discrete logarithm problem (DLP) consists of finding an integer n such that bn = g, if such an n exists. The difficulty involved in computing the discrete logarithm varies with the choice of G. For example, in the additive group of integers modulo n, (Z/nZ)+, the problem can be solved efficiently. Given b and g such that 0 ≤ b, g ≤ n − 1 and kb ≡ g mod n for some integer k, then the Extended Euclidean Algorithm can be used to quickly compute a such that ba ≡ 1 mod n, so that akb ≡ k mod n. See  for a more detailed explanation. For the purposes of cryptography, a group in which the discrete logoarithm is difficult to compute is desirable. In such a context, the discrete logarithm becomes a trapdoor, or one-way, function, since exponentiation (the inverse operation) can always be performed efficiently through repeated squaring. There are two widely used groups in public key cryptosystems based on the discrete logarithm problem, the multiplicative group of integers (Z/pZ)∗ and the elliptic curve group E(Z/nZ). The first popular public key cryptosystems were based on the DLP in (Z/nZ)∗. Systems based on E(Z/nZ) have drawn substantial interest more recently due to the DLP being harder in that context. In this paper, we focus on the DLP in E(Z/nZ).
ITIL as common practice reference model for IT service management: formal assessment and implications for practice

Due to enhanced focus on the customer in the planning, development and delivery of information services, IT service management has become increasingly important. These days IT management is focussing particularly on the de facto standard ITIL (IT Infrastructure Library) for implementing IT service management. In doing this, deficits of the ITIL reference model are often being overlooked, the benefits are merely assumed and misunderstandings spread. This results in uncertainties, unfulfilled expectations and problems in the execution of ITIL transformation projects. Against this background the article critically assesses the ITIL reference model. A formal analysis of the model on the basis of established criteria according to the principles for proper modelling is undertaken and implications for IT management are deduced from this. Four case studies of ITIL transformation projects conducted for this purpose, as well as an analysis of the ITIL documents, serve as basis for the assessment.
Real Time 3 D Face Alignment with Random Forests-based Active Appearance Models

Many desirable applications dealing with automatic face analysis rely on robust facial feature localization. While extensive research has been carried out on standard 2D imagery, recent technological advances made the acquisition of 3D data both accurate and affordable, opening new ways to more accurate and robust algorithms. We present a modelbased approach to real time face alignment, fitting a 3D model to depth and intensity images of unseen expressive faces. We use random regression forests to drive the fitting in an Active Appearance Model framework. We thoroughly evaluated the proposed approach on publicly available datasets and show how adding the depth channel boosts the robustness and accuracy of the algorithm.
A learning environment for augmented reality mobile learning

There are many tools that enable the development of the augmented reality (AR) activities, some of them can even create and generate an AR experience where the incorporation of 3D objects is simple. However AR tools used in education are different from the general tools limited to the reproduction of virtual content. The purpose of this paper is to present a learning environment based on augmented reality, which can be used both by teachers to develop quality AR educational resources and by students to acquire knowledge in an area. Common problems teachers have in applying AR have been taken into account by producing an authoring tool for education which includes the following characteristics: (1) the ability to incorporate diverse multimedia resources such as video, sound, images and 3D objects in an easy manner, 2) the ability to incorporate tutored descriptions into elements which are being displayed (thus, the student is provided with an additional information, description or narrative about the resource, while the content gets adapted and personalized), (3) the possibility for the teacher to incorporate multiple choice questions (MCQ) into the virtual resource (useful for instant feedback to students on their understanding, it can be useful for the student to know what are the most important points of that issue and for the teacher to assess whether the student distinguishes different concepts) and (4) a library of virtual content where all resources are available in a simple and transparent way for any user. In this study ARLE is used to add AR technologies into notes or books created by the teacher, thereby supplementing both the theoretical and practical content without any programming skills needed on the designers' behalf. In addition to presenting system architecture and the examples of its educational use, a survey concerning use of AR amongst teachers in Spain has been conducted.
A Survey of Model Comparison Approaches and Applications

This survey paper presents the current state of model comparison as it applies to Model-Driven Engineering. We look specifically at how model matching is accomplished, the application of the approaches, and the types of models that approaches are intended to work with. Our paper also indicates future trends and directions. We find that many of the latest model comparison techniques are geared towards facilitating arbitrary meta models and use similarity-based matching. Thus far, model versioning is the most prevalent application of model comparison. Recently, however, work on comparison for versioning has begun to stagnate, giving way to other applications. Lastly, there is wide variance among the tools in the amount of user effort required to perform model comparison, as some require more effort to facilitate more generality and expressive power.
Enhancing the trust-based recommendation process with explicit distrust

When a Web application with a built-in recommender offers a social networking component which enables its users to form a trust network, it can generate more personalized recommendations by combining user ratings with information from the trust network. These are the so-called trust-enhanced recommendation systems. While research on the incorporation of trust for recommendations is thriving, the potential of explicitly stated distrust remains almost unexplored. In this article, we introduce a distrust-enhanced recommendation algorithm which has its roots in Golbeck's trust-based weighted mean. Through experiments on a set of reviews from Epinions.com, we show that our new algorithm outperforms its standard trust-only counterpart with respect to accuracy, thereby demonstrating the positive effect that explicit distrust can have on trust-based recommendations.
The delay/Doppler radar altimeter

The key innovation in the delay/Doppler radar altimeter is delay compensation, analogous to range curvature correction in a burst-mode synthetic aperture radar (SAR). Following delay compensation, height estimates are sorted by Doppler frequency, and integrated in parallel. More equivalent looks are accumulated than in a conventional altimeter. The relatively small along-track footprint size is a constant of the system, typically on the order of 250 m for a Ku-band altimeter. The flat-surface response is an impulse rather than the more familiar step function produced by conventional satellite radar altimeters. The radar equation for the delay/Doppler radar altimeter has an h (c ) dependence on heighth and compressed pulse length , which is more efficient than the correspondingh c factor for a pulse-limited altimeter. The radiometric response obtained by the new approach would be 10 dB stronger than that of the TOPEX/Poseidon altimeter, for example, if the same hardware were used in the delay/Doppler altimeter mode. This new technique leads to a smaller instrument that requires less power, yet performs better than a conventional radar altimeter. The concept represents a new generation of altimeter for earth observation, with particular suitability for coastal ocean regions and polar ice sheets as well as open oceans.
Assembled Dual-Band Broadband Quadrifilar Helix Antennas With Compact Power Divider Networks for CNSS Application

Assembled dual-band quadrifilar helix antennas (QHAs) are proposed for applications in Compass Navigation Satellite System (CNSS) of China. The assembled CNSS antennas proposed are designed specifically to meet the requirements of the system: Duplex communication service on two frequency bands (1.615 GHz and 2.492 GHz); Broad beamwitdh on radiation pattern and broad bandwidth for circularly polarization (CP) applications. In this paper, the structure of assembled CNSS QHAs is demonstrated first, which comprises of emitting and receiving QHAs with novel compact power divider networks. Then the method to control the radiation pattern of QHAs is analyzed. Moreover, the design principle of the compact power divider network to achieve broadband application is presented. In the end, the antenna was fabricated and measured. Experimental results show that the proposed assembled CNSS dual-band QHAs exhibit duplex operation, broad 3 dB beamwidth (>;180 deg), and broad 10 dB return loss bandwidth (emitting antenna 39%; receiving antenna 28%). In addition, the axial ratio is less than 1.5 on two frequency bands of the system (Uplink: 1615.68±7 MHz; Downlink: 2491.75±15 MHz).
Hiding Images in Plain Sight: Deep Steganography

Steganography is the practice of concealing a secret message within another, ordinary, message. Commonly, steganography is used to unobtrusively hide a small message within the noisy regions of a larger image. In this study, we attempt to place a full size color image within another image of the same size. Deep neural networks are simultaneously trained to create the hiding and revealing processes and are designed to specifically work as a pair. The system is trained on images drawn randomly from the ImageNet database, and works well on natural images from a wide variety of sources. Beyond demonstrating the successful application of deep learning to hiding images, we carefully examine how the result is achieved and explore extensions. Unlike many popular steganographic methods that encode the secret message within the least significant bits of the carrier image, our approach compresses and distributes the secret image’s representation across all of the available bits.
Modeling problem difficulty and expertise in stackoverflow

Supporting expert communities is becoming a 'must-have' capability whenever users are helping each other solve problems. Examples of these expert communities abound in the form of enthusiast communities, both inside and outside of organizations. In order to achieve success, these systems have to connect several different actors. In this paper we aim to inform the design of these Hybrid Intelligence Systems through the investigation of StackOverflow. Our focus in this paper is to develop indicators for hard problems and experts. The long-term goal of our study is to examine how complex problems are handled and dispatched across multiple experts. We outline implications for modeling these attributes and how they might inform better design in the future.