nils-herrmann/clean_scirepeval huggingface dataset:
Toward developing a systematic approach to generate benchmark datasets for intrusion detection

In network intrusion detection, anomaly-based approaches in particular suffer from accurate evaluation, comparison, and deployment which originates from the scarcity of adequate datasets. Many such datasets are internal and cannot be shared due to privacy issues, others are heavily anonymized and do not reflect current trends, or they lack certain statistical characteristics. These deficiencies are primarily the reasons why a perfect dataset is yet to exist. Thus, researchers must resort to datasets that are often suboptimal. As network behaviors and patterns change and intrusions evolve, it has very much become necessary to move away from static and one-time datasets toward more dynamically generated datasets which not only reflect the traffic compositions and intrusions of that time, but are also modifiable, extensible, and reproducible. In this paper, a systematic approach to generate the required datasets is introduced to address this need. The underlying notion is based on the concept of profiles which contain detailed descriptions of intrusions and abstract distribution models for applications, protocols, or lower level network entities. Real traces are analyzed to create profiles for agents that generate real traffic for HTTP, SMTP, SSH, IMAP, POP3, and FTP. In this regard, a set of guidelines is established to outline valid datasets, which set the basis for generating profiles. These guidelines are vital for the effectiveness of the dataset in terms of realism, evaluation capabilities, total capture, completeness, and malicious activity. The profiles are then employed in an experiment to generate the desirable dataset in a testbed environment. Various multi-stage attacks scenarios were subsequently carried out to supply the anomalous portion of the dataset. The intent for this dataset is to assist various researchers in acquiring datasets of this kind for testing, evaluation, and comparison purposes, through sharing the generated datasets and profiles. a 2011
Enhancing Supervised Learning with Unlabeled Data

In many practical learning scenarios, there is a small amount of labeled data along with a large pool of unlabeled data. Many supervised learning algorithms have been developed and extensively studied. We present a new \co-training" strategy for using un-labeled data to improve the performance of standard supervised learning algorithms. Unlike much of the prior work, such as the co-training procedure of Blum and Mitchell (1998), we do not assume there are two redundant views both of which are suucient for classiication. The only requirement our co-training strategy places on each supervised learning algorithm is that its hypothesis partitions the example space into a set of equivalence classes (e.g. for a decision tree each leaf deenes an equivalence class). We evaluate our co-training strategy via experiments using data from the UCI repository.
Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study

Influence maximization (IM) on social networks is one of the most active areas of research in computer science. While various IM techniques proposed over the last decade have definitely enriched the field, unfortunately, experimental reports on existing techniques fall short in validity and integrity since many comparisons are not based on a common platform or merely discussed in theory. In this paper, we perform an in-depth benchmarking study of IM techniques on social networks. Specifically, we design a benchmarking platform, which enables us to evaluate and compare the existing techniques systematically and thoroughly under identical experimental conditions. Our benchmarking results analyze and diagnose the inherent deficiencies of the existing approaches and surface the open challenges in IM even after a decade of research. More fundamentally, we unearth and debunk a series of myths and establish that there is no single state-of-the-art technique in IM. At best, a technique is the state of the art in only one aspect.
An Improved Control Strategy for Grid-Connected Voltage Source Inverters With an LCL Filter

A novel current control strategy based on a new current feedback for grid-connected voltage source inverters with an LCL-filter is proposed in this paper. By splitting the capacitor of the LCL-filter into two parts, each with the proportional division of the capacitance, the current flowing between these two parts is measured and used as the feedback to a current regulator to stabilize and improve the system performances. Consequently, the V-1 transfer function of the grid-connected inverter system with the LCL-filter is degraded from a third-order function to a first-order one, therefore the closed-loop current feedback control system can be optimized easily for minimum steady-state error and current harmonic distortion, as well as the system stability. The characteristics of the inverter system with the proposed controller are investigated and compared with those using traditional control strategies. Experimental results are provided, and the new current control strategy has been verified on a 5 kW fuel cell inverter.
A 10-Gb/s receiver with a continuous-time linear equalizer and 1-tap decision-feedback equalizer

This paper presents a wireline receiver design of CMOS I/O at 10-Gb/s data rate. A power efficient continuous-time linear equalizer (CTLE) and 1-tap lookahead decision feedback equalizer (DFE) are designed and implemented in a 45 nm CMOS technology. The DFE employs a sampler including a current injection section that makes no use of summer as a separated block. In addition, cascode structure increases kick-back noise immunity and reduces power consumption by 11%. The PLL used in the proposed receiver drives 5 GHz clock frequency with 12.62 pspk-pk jitter. The core receiver circuit consumes 14.3 mW at a 1.1 V supply voltage when processing 10 Gb/s data rate with 15 dB of channel loss at Nyquist frequency.
Phase control through Huygens' metasurfaces

We describe a dielectric Huygens' metasurface, operating in the visible spectrum, that can manipulate the phase of the transmitted light over a full 2π with high transmission efficiency. The metasurface is comprised of dielectric cylindrical nanoparticles embedded in a glass-like medium. The size of the nanoparticles and their separation are much smaller than the wavelength of visible light. From the metasurface, we construct a lens that focuses light to a diffraction limited spot size with high efficiency.
Color-based Segmentation of Point Clouds

Segmentation is one of the most fundamental procedures for the automation of point cloud processing. The methods based on geometrical derivatives such as curvature and normals often lead to over-segmentation and even failure when used to segment point clouds of geometrically-complex architectures. In this paper we present a point cloud segmentation algorithm based on colorimetrical similarity and spatial proximity. The algorithm contains region growing, region merging and refinement processes. The region growing process uses kd-tree to search the k-nearest neighbors of each seed point. The resulting regions are then merged and finally refined on the basis of colorimetrical and spatial relation. In each of the process, we developed different criteria corresponding to the different tasks to carry on the segmentation. The algorithm requires a small number of manually set parameters which are used to keep balance between underand over-segmentation. The experiments of the presented algorithm on a point cloud of Chinese ancient architecture show its effectiveness. The segmented regions can be used to reconstruct 3D models of different parts of the architectures.
NativeGuard: protecting android applications from third-party native libraries

Android applications often include third-party libraries written in native code. However, current native components are not well managed by Android's security architecture. We present NativeGuard, a security framework that isolates native libraries from other components in Android applications. Leveraging the process-based protection in Android, NativeGuard isolates native libraries of an Android application into a second application where unnecessary privileges are eliminated. NativeGuard requires neither modifications to Android nor access to the source code of an application. It addresses multiple technical issues to support various interfaces that Android provides to the native world. Experimental results demonstrate that our framework works well with a set of real-world applications, and incurs only modest overhead on benchmark programs.
Encouraging information security behaviors in organizations: Role of penalties, pressures and perceived effectiveness

a r t i c l e i n f o Keywords: Principal agent theory Information security End-user security behaviors Security policy compliance Secure management of information systems is crucially important in information intensive organizations. Although most organizations have long been using security technologies, it is well known that technology tools alone are not sufficient. Thus, the area of end-user security behaviors in organizations has gained an increased attention. In information security observing end-user security behaviors is challenging. Moreover, recent studies have shown that the end users have divergent security views. The inability to monitor employee IT security behaviors and divergent views regarding security policies, in our view, provide a setting where the principal agent paradigm applies. In this paper, we develop and test a theoretical model of the incentive effects of penalties, pressures and perceived effectiveness of employee actions that enhances our understanding of employee compliance to information security policies. Based on 312 employee responses from 77 organizations, we empirically validate and test the model. Our findings suggest that security behaviors can be influenced by both intrinsic and extrinsic motivators. Pressures exerted by subjective norms and peer behaviors influence employee information security behaviors. Intrinsic motivation of employee perceived effectiveness of their actions was also found to play an important role in security policy compliance intentions. In analyzing the penalties, certainty of detection was found to be significant while surprisingly, severity of punishment was found to have a negative effect on security behavior intentions. We discuss the implications of our findings for theory and practice. In information intensive organizations secured management of information has become an important issue. Organizations have been actively using security technologies. Extant research in information security has been focused on the use technology (e.g., [27,69]). However more recently, practitioners and academics have started to realize that information security cannot be achieved through only technological tools and effective organizational information security depends on all three components, namely: people, processes and technology [34]. However, empirical research on end-user security behaviors and factors influencing them is still in its infancy. With the advances in security technologies, many computing behaviors such as patch management and antivirus updates are now being automated to reduce the task knowledge and time burdens on end users. However, behaviors such as appropriate use of computer and network resources, appropriate password habits etc., that cannot be addressed by security technologies are often dealt through organizational computer …
Enrich machine-to-machine data with semantic web technologies for cross-domain applications

The Internet of Things, more specifically, the Machine-to-Machine (M2M) standard enables machines and devices such as sensors to communicate with each other without human intervention. The M2M devices provide a great deal of M2M data, mainly used for specific M2M applications such as weather forecasting, healthcare or building automation. Existing applications are domain-specific and use their own descriptions of devices and measurements. A major challenge is to combine M2M data provided by these heterogeneous domains and by different projects. It is really a difficult task to understand the meaning of the M2M data to later reason about them. We propose a semantic-based approach to automatically combine, enrich and reason about M2M data to provide promising cross-domain M2M applications. A proof-of-concept to validate our approach is published online (http://sensormeasurement.appspot.com/).
Invar-36 Micro Hemispherical Shell Resonators

We report, for the first time, on the successful fabrication and operational characterization of electroplated Invar Micro-Hemispherical Shell Resonators (μHSR). The heat treatment of the samples and its effect on the quality factor (Q) of the resonators is studied. We show that thermal annealing shifts the coefficient of thermal expansion (CTE) of the alloy towards its minimum of ~2ppm/°C, as a result of which the Q of a 29kHz μHSR with diameter of 780 μm increases at least 3 times and reaches 7500 in vacuum.
Ceramic interconnect bridge for heterogeneous integration and multiple chip packaging

In this paper, we describe the architecture and performance of a fine pitch multiple chip heterogeneous integration solution using ceramic interconnect bridge on organic substrate package. We present the increased IO density and improvement of electrical high-speed performance on signal integrity are achievable through this novel integration scheme, where dense copper routings on small ceramic elements are served as interconnect bridges. The cost and signal attenuation of using ceramic bridge is far better than that of silicon bridge or wafer interposer on substrate.
Feature Extraction with Convolutional Neural Networks for Handwritten Word Recognition

In this paper, we show that learning features with convolutional neural networks is better than using hand-crafted features for handwritten word recognition. We consider two kinds of systems: a grapheme based segmentation and a sliding window segmentation. In both cases, the combination of a convolutional neural network with a HMM outperform a state-of-the art HMM system based on explicit feature extraction. The experiments are conducted on the Rimes database. The systems obtained with the two kinds of segmentation are complementary: when they are combined, they outperform the systems in isolation. The system based on grapheme segmentation yields lower recognition rate but is very fast, which is suitable for specific applications such as document classification.
Novel rotor design optimization of Synchronous Reluctance Machine for low torque ripple

Torque ripple can be an important issue in Synchronous Reluctance Machine (SynRM) design, similar to the Induction Machine (IM). A novel, simple and systematic method for ripple reduction in SynRM (that can also be implemented for IPM and PMaSynRM) design is presented in this paper. The decoupling between stator and rotor structure during the torque ripple minimization is another goal. This can be achieved using a general method that minimizes the ripple independent of the stator structure, specially the slots number of the stator, and the number of barriers in the rotor or rotor slots number. Furthermore, it is shown that torque maximization and torque ripple minimization can be achieved independently in the SynRM design using limitedly, FEM simulations.
Algorithms for the Assignment and Transiortation Troblems*

In this paper we presen algorithms for the solution of the general assignment and transportation problems. In Section 1, a statement of the algorithm for the assignment problem appears, along with a proof for the correctness of the algorithm. The remarks which constitute the proof are incorporated parenthetically into the statement of the algorithm. Following this appears a discussion of certain theoretical aspects of the problem. In Section 2, the algorithm is generalized to one for the transportation problem. The algorithm of that section is stated as concisely as possible, with theoretical remarks omitted.
A High-Speed and Ultra Low-Power Subthreshold Signal Level Shifter

In this paper, we present a novel level shifter circuit converting subthreshold signal levels to super-threshold signal levels at high-speed using ultra low-power and a small silicon area, making it well-suited for low-power applications such as wireless sensor networks and implantable medical devices. The proposed circuit introduces a new voltage level shifter topology employing a level-shifting capacitor contributing to increase the range of conversion voltages, while significantly reducing the conversion delay. Such a level-shifting capacitor is quickly charged, whenever the input signal detects a low-to-high transition, in order to boost internal voltage nodes, and quickly reach a high output voltage level. The proposed circuit achieves a shorter propagation delay and a smaller silicon area for a given operating frequency and power consumption compared to other circuit solutions. Measurement results are presented for the proposed circuit fabricated in a 0.18- $\mu \text {m}$  TSMC technology. The proposed circuit can convert a wide range of the input voltages from 330 mV to 1.8 V, and operate over a frequency range of 100 Hz to 100 MHz. It has a propagation delay of 29 ns and a power consumption of 61.5 nW for input signals 0.4 V, at a frequency of 500-kHz, outperforming previous designs.
MedLeaf: Mobile biodiversity informatics tool for mapping and identifying Indonesian medicinal Plants

We presents a mobile biodiversity informatics tools for identifying and mapping Indonesian medicinal plants. The system - called MedLeaf - has been developed as a prototype data resource for documenting, integrating, disseminating, and identifying of Indonesian medicinal plants. Identification of medicinal plant is done automatically based on digital image processing. Fuzzy Local Binary Pattern (LBP) and geometrical features are used to extract leaves features. Probabilistic Neural Network is used as classifier for discrimination. Data set consist of 85 species of Indonesian medicinal plants with 3,502 leaves digital images. Our results indicate that combination of leaves features outperform than using single features with accuracy 88.5%. The distribution of medicinal plants can be shown on mobile phone using GIS application. The application is essential to help people identify the medicinal plants and disseminate information of medicinal plants distribution in Indonesia.
A Capacitor-DAC-Based Technique For Pre-Emphasis-Enabled Multilevel Transmitters

This brief presents a capacitor digital-to-analog converter (DAC) based technique that is suitable for pre-emphasis-enabled multilevel wireline transmitter design in voltage mode. Detailed comparisons between the proposed technique and conventional direct-coupling-based as well as resistor-DAC-based multilevel transmitter design techniques are given, revealing potential benefits in terms of speed, linearity, implementation complexity, and also power consumption. A PAM-4 transmitter with 2-Tap feed-forward equalization adopting the proposed technique is implemented in 65-nm CMOS technology. It achieves a 25-Gb/s data rate and energy efficiency of 2 mW/Gb/s.
Active-clamp snubbers for isolated half-bridge DC-DC converters

In conventional isolated half-bridge dc-dc converters, the leakage-inductance-related losses degrade converter efficiency and limit the ability to increase the converters' switching frequencies. In this paper, a novel active-clamp snubber circuit for half-bridge dc-dc converters is proposed to recycle the energy stored in the leakage inductance by transferring this energy to a capacitor with zero-voltage zero-current-switching switched auxiliary switches, such that body-diode conduction of primary-side main switches are prevented and primary side ringing are attenuated resulting in improved converter efficiency. Principles of operation and simulation analysis are presented and supported by experimental results that show significant improvement in efficiency.
Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks

Parallelization framework has become a necessity to speed up the training of deep neural networks (DNN) recently. Such framework typically employs the Model Average approach, denoted as MA-DNN, in which parallel workers conduct respective training based on their own local data while the parameters of local models are periodically communicated and averaged to obtain a global model which serves as the new start of local models. However, since DNN is a highly non-convex model, averaging parameters cannot ensure that such global model can perform better than those local models. To tackle this problem, we introduce a new parallel training framework called Ensemble-Compression, denoted as EC-DNN. In this framework, we propose to aggregate the local models by ensemble, i.e., averaging the outputs of local models instead of the parameters. As most of prevalent loss functions are convex to the output of DNN, the performance of ensemble-based global model is guaranteed to be at least as good as the average performance of local models. However, a big challenge lies in the explosion of model size since each round of ensemble can give rise to multiple times size increment. Thus, we carry out model compression after each ensemble, specialized by a distillation based method in this paper, to reduce the size of the global model to be the same as the local ones. Our experimental results demonstrate the prominent advantage of EC-DNN over MA-DNN in terms of both accuracy and speedup.
TrustOTP: Transforming Smartphones into Secure One-Time Password Tokens

Two-factor authentication has been widely used due to the vulnerabilities associated with traditional text-based password. One-time password (OTP) plays an indispensable role on authenticating mobile users to critical web services that demand a high level of security. As the smartphones are increasingly gaining popularity nowadays, software-based OTP generators have been developed and installed into smartphones as software apps, which bring great convenience to the users without introducing extra burden. However, software-based OTP solutions cannot guarantee the confidentiality of the generated passwords or even the seeds when the mobile OS is compromised. Moreover, they also suffer from denial-of-service attacks when the mobile OS crashes. Hardware-based OTP tokens can solve these security problems in the software-based OTP solutions; however, it is inconvenient for the users to carry physical tokens with them, particularly, when there are more than one token to be carried. In this paper, we present TrustOTP, a secure one-time password solution that can achieve both the flexibility of software tokens and the security of hardware tokens by using ARM TrustZone technology. TrustOTP can not only protect the confidentiality of the OTPs against a malicious mobile OS, but also guarantee reliable OTP generation and trusted OTP display when the mobile OS is compromised or even crashes. It is flexible to integrate multiple OTP algorithms and instances for different application scenarios on the same smartphone platform without modifying the mobile OS. We develop a prototype of TrustOTP on Freescale i.MX53 QSB. The experimental results show that TrustOTP has small impacts on the mobile OS and its power consumption is low.