nils-herrmann/clean_scirepeval huggingface dataset:
Beyond Bitcoin - Part I: A critical look at blockchain-based systems

After more than six years from the launch of Bitcoin, it has become evident that the decentralized transaction ledger functionality implemented through the blockchain technology can be used not only for cryptocurrencies, but to register, confirm and transfer any kind of contract and property. In this work we analyze the most relevant functionalities and known issues of this technology, with the intent of pointing out the possible behaviours that are not as efficient as they should when thinking with a broader outlook. Our analysis would be the starting point for the introduction of a new approach to blockchain creation and management, which will be the subject of a forthcoming paper.
Metal-to-Multilayer-Graphene Contact—Part I: Contact Resistance Modeling

Parasitic components are becoming increasingly important with geometric scaling in nanoscale electronic devices and interconnects. The parasitic contact resistance between metal electrodes and multilayer graphene (MLG) is a key factor determining the performance of MLG-based structures in various applications. The available methods for characterizing metal–MLG contact interfaces rely on a model based on the top-contact structure, but it ignores the edge contacts that can greatly reduce the contact resistance. Therefore, in the present work, a rigorous theoretical 1-D model for metal–MLG contact is developed for the first time. The contribution of the major components of resistance—the top and edge contacts (side and end contacts) and the MLG sheet resistivity—to the total resistance of the structure is included in the model. The 1-D model is compared to a 3-D model of the system, and a method for investigation and optimization of the range of validity of the 1-D model is developed. The results of this work provide valuable insight to both the characterization and design of metal–MLG contacts.
Field and Torque Calculation and Transient Analysis in Variable Reluctance Machines

The variable reluctance machine (VRM), also known as the switched reluctance machine, is a class of electrical machines that exhibits many interesting characteristics. However, the satisfactory analytical formulation of its working equations (torque and voltage equations) in a saturated regime is not yet completely available for covering some of its operational aspects, such as the analysis of transient state. This paper provides such equations for the general case in which a VRM operates with a pronounced level of magnetic saturation. These equations together with help of lookup tables allow online access of the instant torque on the shaft as well as the flux linkage of a phase, since the computation times are reduced to a few microseconds, whereas in a numerical approach such as the finite-element method, would take a few minutes of CPU time, which is totally inappropriate for such investigations.
Comparison of angle-only filtering algorithms in 3D using EKF, UKF, PF, PFF, and ensemble KF

In our previous work, we compared the performance of the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF) for the angle-only filtering (AOF) problem in 3D using Cartesian coordinates and modified spherical coordinates (MSC) for the relative state vector. We found that the UKF-MSC and EKF-MSC had the best performance in accuracy, the UKF-MSC being slightly better than the EKF-MSC. The PF didn't perform well compared with the EKF and UKF and had a higher computational cost. In this work, we compare the performance of the particle flow filter (PFF) with the other filters for the AOF problem. In addition, we also analyze the performance of two versions of the ensemble Kalman filter (EnKF) in this comparative study. We present numerical results from Monte Carlo simulations to analyze the state estimation accuracy and computational cost of these filters.
An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning

In recent years, smart home technologies have started to be widely used, bringing a great deal of convenience to people’s daily lives. At the same time, privacy issues have become particularly prominent. Traditional encryption methods can no longer meet the needs of privacy protection in smart home applications, since attacks can be launched even without the need for access to the cipher. Rather, attacks can be successfully realized through analyzing the frequency of radio signals, as well as the timestamp series, so that the daily activities of the residents in the smart home can be learnt. Such types of attacks can achieve a very high success rate, making them a great threat to users’ privacy. In this paper, we propose an adaptive method based on sample data analysis and supervised learning (SDASL), to hide the patterns of daily routines of residents that would adapt to dynamically changing network loads. Compared to some existing solutions, our proposed method exhibits advantages such as low energy consumption, low latency, strong adaptability, and effective privacy protection.
A Job Scheduling Design for Visualization Services Using GPU Clusters

Modern large-scale heterogeneous computers incorporating GPUs offer impressive processing capabilities. It is desirable to fully utilize such systems for serving multiple users concurrently to visualize large data at interactive rates. However, as the disparity between data transfer speed and compute speed continues to increase in heterogeneous systems, data locality becomes crucial for performance. We present a new job scheduling design to support multi-user exploration of large data in a heterogeneous computing environment, achieving near optimal data locality and minimizing I/O overhead. The targeted application is a parallel visualization system which allows multiple users to render large volumetric data sets in both interactive mode and batch mode. We present a cost model to assess the performance of parallel volume rendering and quantify the efficiency of job scheduling. We have tested our job scheduling scheme on two heterogeneous systems with different configurations. The largest test volume data used in our study has over two billion grid points. The timing results demonstrate that our design effectively improves data locality for complex multi-user job scheduling problems, leading to better overall performance of the service.
Design and control of an actuated thumb exoskeleton for hand rehabilitation following stroke

Chronic hand impairment is common following stroke. This paper presents an actuated thumb exoskeleton (ATX) to facilitate research in hand rehabilitation therapy. The ATX presented in this work permits independent bi-directional actuation in each of the 5 degrees-of-freedom (DOF) of the thumb using a mechanism that has 5 active DOF and 3 passive DOF. The ATX is able to provide considerable joint torques for the user while still allowing backdrivability through flexible shaft transmission. A prototype has been built and experiments were conducted to evaluate the closed-loop position control. Further improvement and future work are discussed.
Designing gaze gestures for gaming: an investigation of performance

To enable people with motor impairments to use gaze control to play online games and take part in virtual communities, new interaction techniques are needed that overcome the limitations of dwell clicking on icons in the games interface. We have investigated gaze gestures as a means of achieving this. We report the results of an experiment with 24 participants that examined performance differences between different gestures. We were able to predict the effect on performance of the numbers of legs in the gesture and the primary direction of eye movement in a gesture. We also report the outcomes of user trials in which 12 experienced gamers used the gaze gesture interface to play World of Warcraft. All participants were able to move around and engage other characters in fighting episodes successfully. Gestures were good for issuing specific commands such as spell casting, and less good for continuous control of movement compared with other gaze interaction techniques we have developed.
A 7.6 mW, 214-fs RMS jitter 10-GHz phase-locked loop for 40-Gb/s serial link transmitter based on two-stage ring oscillator in 65-nm CMOS

This paper presents a -245.3 dB FoMJ phase-locked loop based on a ring oscillator and a novel analysis on 2-stage ring oscillator. The proposed PLL generates a 4-phase 10-GHz clock for a 40-Gb/s serial link transmitter. The proposed analysis offers a time-domain insight on 2-stage ring oscillator and a precise prediction on oscillator behavior such as an output frequency and whether the 2-stage ring oscillates or not, based on a simple open-loop approach with a single stage buffer. The prototype chip is fabricated in 65-nm CMOS technology, and the PLL occupies only 0.009 mm2 and dissipates 7.6 mW from 1.2-V supply and 9 mW from 1.3-V supply. The measured integrated jitter of the PLL is 214 fs from 1.2-V supply and 182 fs from 1.3-V supply, which corresponds to -244.6 dB and -245.3 dB FoMJ, respectively.
Principal component analysis for speed limit Traffic Sign Recognition

Traffic Sign Recognition has recently become a popular research field. The main applications include Autonomous Driving Systems, Driver Assistance Systems, road sign inventory, to name a few. In this paper, a speed limit traffic sign recognition system is proposed based on Principal Component Analysis (PCA) with preprocessing steps, that help on perspective and extreme luminance variation correction. The classification is performed by a feed-forward neural network, or Multi-Layer Perceptron (MLP). Experimental results present a classification accuracy similar to some state of the art systems, but with a more compact scheme.
Multiple-Loop Design Technique for High-Performance Low-Dropout Regulator

A new multiple-loop design technique for high-performance low-dropout (LDO) regulator designs has been proposed and successfully implemented in many commercial products for portable smart phone and tablet PC applications. The proposed LDO is composed of five loops that allows designers to obtain a good tradeoff between quiescent current and other performances, such as undershoot, overshoot, and so on. A total of one bandgap reference and 38 LDOs (including n-type and p-type LDOs, which will be named NLDO and PLDO, respectively) were integrated in one power-management IC chip for powering an application processor inside mobile devices. The proposed LDO has been fabricated based on 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu {m}$ </tex-math></inline-formula> CMOS process and supplies various current capacities from 50 to 600 mA; 38 LDOs have been designed and supply different output voltage levels from 0.7 to 3.0 V. One of the proposed NLDOs consumes <inline-formula> <tex-math notation="LaTeX">$14~\mu {A}$ </tex-math></inline-formula> of the quiescent current and features under 56/24 mV of undershoot/overshoot at VOUT =1<italic>V</italic> as the load current steps up from 0 to 300 mA with 300 mA/1 <inline-formula> <tex-math notation="LaTeX">$\mu {s}$ </tex-math></inline-formula> on a 1-<inline-formula> <tex-math notation="LaTeX">$\mu {F}$ </tex-math></inline-formula> output capacitor. The measured output load and line regulations are 1.8 and 0.4 mV, respectively. The measured integrated output noise from 10 Hz to 100 kHz at <inline-formula> <tex-math notation="LaTeX">$\text{I}_{\mathrm{ LOAD}}=10\%$ </tex-math></inline-formula> of maximum current shows 80 <inline-formula> <tex-math notation="LaTeX">$\mu {V}$ </tex-math></inline-formula> rms. The package chip size is <inline-formula> <tex-math notation="LaTeX">$6.25 \times 6.25$ </tex-math></inline-formula> mm<sup>2</sup> with 169 balls.
A Probabilistic Approach for Web Service Discovery

Web service discovery is a vital problem in service computing with the increasing number of services. Existing service discovery approaches merely focus on WSDL-based keyword search, semantic matching based on domain knowledge or ontologies, or QoS-based recommendations. The keyword search omits the underlying correlations and semantic knowledge or QoS information is not always available. In this paper, we propose a probabilistic service discovery approach to help web service users to retrieve related services and to improve the search performance. Specifically, we apply a probabilistic model to characterize the latten topics between services and queries, and then propose a matching method based on the topic relevance. Experiments on services from a real service repository confirm the feasibility and efficiency of this proposed method.
Learning patterns of university student retention

Learning predictors for student retention is very difficult. After reviewing the literature, it is evident that there is considerable room for improvement in the current state of the art. As shown in this paper, improvements are possible if we (a) explore a wide range of learning methods; (b) take care when selecting attributes; (c) assess the efficacy of the learned theory not just by its median performance, but also by the variance in that performance; (d) study the delta of student factors between those who stay and those who are retained. Using these techniques, for the goal of predicting if students will remain for the first three years of an undergraduate degree, the following factors were found to be informative: family background and family’s social-economic status, high school GPA
Glaucoma-Deep : Detection of Glaucoma Eye Disease on Retinal Fundus Images using Deep Learning Detection of Glaucoma by Abbas

Detection of glaucoma eye disease is still a challenging task for computer-aided diagnostics (CADx) systems. During eye screening process, the ophthalmologists measures the glaucoma by structure changes in optic disc (OD), loss of nerve fibres (LNF) and atrophy of the peripapillary region (APR). In retinal images, the automated CADx systems are developed to assess this eye disease through segmentation-based hand-crafted features. Therefore in this paper, the convolutional neural network (CNN) unsupervised architecture was used to extract the features through multilayer from raw pixel intensities. Afterwards, the deep-belief network (DBN) model was used to select the most discriminative deep features based on the annotated training dataset. At last, the final decision is performed by softmax linear classifier to differentiate between glaucoma and non-glaucoma retinal fundus image. This proposed system is known as Glaucoma-Deep and tested on 1200 retinal images obtained from publically and privately available datasets. To evaluate the performance of Glaucoma-Deep system, the sensitivity (SE), specificity (SP), accuracy (ACC), and precision (PRC) statistical measures were utilized. On average, the SE of 84.50%, SP of 98.01%, ACC of 99% and PRC of 84% values were achieved. Comparing to state-of-the-art systems, the Nodular-Deep system accomplished significant higher results. Consequently, the Glaucoma-Deep system can easily recognize the glaucoma eye disease to solve the problem of clinical experts during eye-screening process on large-scale environments. Fundus imaging; glaucoma; diabetic retinopathy; deep learning; convolutional neural networks; deep belief network
LogKV: Exploiting Key-Value Stores for Log Processing

Event log processing and analysis play a key role in applications ranging from security management, IT trouble shooting, to user behavior analysis. Recent years have seen a rapid growth in system scales and the corresponding rapid increase in the amount of log event data. At the same time, as logs are found to be a valuable information source, log analysis tasks have become more sophisticated demanding both interactive exploratory query processing and batch computation. Desirable query types include selection with time ranges and value filtering criteria, join within time windows, join between log data and reference tables, and various aggregation types. In such a situation, parallel solutions are necessary, but existing parallel and distributed solutions either support limited query types or perform only batch computations on logs. With a system called LogKV, this paper reports a first study of using Key-Value stores to support log processing and analysis, exploiting the scalability, reliability, and efficiency commonly found in Key-Value store systems. LogKV contains a number of unique techniques that are needed to handle log data in terms of event ingestion, load balancing, storage optimization, and query processing. Preliminary experimental results show that LogKV is a promising solution.
How to Gamify Information Systems - Adapting Gamification to Individual Preferences

Gamification refers to the use of game elements in a non-entertainment-based context, and is a wellknown approach to motivate users of information systems. Despite the positive effects of games in daily life, more gamification projects fail. A possible explanation for this observation is that game elements are often designed without considering the needs of different groups of users. Consequently, we aim to develop a gamification approach that allows us to adapt the gamification elements to the individual motivation structures of information system users. To achieve our goal, we rely on selfdetermination theory to design different specifications of game elements. We exemplarily developed four different specifications for each of the nine gamification elements we found in the literature. We are currently in the process of conducting a discrete choice experiment allowing us to match the motivation structures of system users and their preferences regarding the specifications of game elements. Based on our results, we expect to better understand how gamification can motivate users to use systems more regularly. In a subsequent step, we plan to rely on our results to design different gamification configurations for an information system, allowing us to adapt the gamification elements to individual preferences of the users.
A SURVEY : MACHINE LEARNING USING HETEROGENEOUS INFORMATION GRAPH

Most of the information available in the real world are of different type or heterogeneous, when we connect such a kind of information it will form the information network. The extraction of useful knowledge from this information network becomes ubiquitous so, this can be effectively handled by using clustering and ranking approach. When we use clustering and ranking methods for extracting useful knowledge with semantic structure we can get relative information and also it may lead to better understanding of hidden knowledge of the network, as well as particular role of every objects within the cluster. By using the ranking method we can clustered the dataset effectively based on the ranking value of the dataset so ranking methods serves as a good measure for clustering the heterogeneous information. The concept of heterogeneous information graphs has attracted in the field of social-networks, social media and machine learning systems. This paper review some of the methods which is used for the processing of heterogeneous information networks for different types of data sets and structure of heterogeneous information networks for extracting semantic information.
Memory systems do not divide on consciousness: Reinterpreting memory in terms of activation and binding.

There is a popular hypothesis that performance on implicit and explicit memory tasks reflects 2 distinct memory systems. Explicit memory is said to store those experiences that can be consciously recollected, and implicit memory is said to store experiences and affect subsequent behavior but to be unavailable to conscious awareness. Although this division based on awareness is a useful taxonomy for memory tasks, the authors review the evidence that the unconscious character of implicit memory does not necessitate that it be treated as a separate system of human memory. They also argue that some implicit and explicit memory tasks share the same memory representations and that the important distinction is whether the task (implicit or explicit) requires the formation of a new association. The authors review and critique dissociations from the behavioral, amnesia, and neuroimaging literatures that have been advanced in support of separate explicit and implicit memory systems by highlighting contradictory evidence and by illustrating how the data can be accounted for using a simple computational memory model that assumes the same memory representation for those disparate tasks.
Automated detection of vulnerabilities in privileged programs by execution monitoring

We present a method for detecting exploitations of vulnerabilities in privileged programs by monitoring their execution using audit trials, where the monitoring is with respect to specifications of the security-relevant behavior of the programs. Our work is motivated by the intrusion detection paradigm, but is a n attempt to avoid a d hoc approaches to codifying misuse behavior. Our approach as based on the observation that although privileged programs can be exploited (due to errors) to cause security compromise in systems because of the privileges accorded to them, the intended behavior of privileged programs is, of course, limited a n d benign. The key, then i s to specify the intended behavior (Le., the program policy) and to detect any action by privileged program that is outside the intended behavior a n d that imperils security. We describe a program policy specification language, which is based on simple predicate logic and regular expressions. I n addition, we present specifications of privileged programs in Unix, and Q prototype execution monitor for analyzing audit trails with respect to these specifications. The program policies are surprisingly concise and clear, a n d an addition, capable of detecting exploitations of known vulnerabilities in these programs. Although our work has been motivated by the known vulnerabilities in Unix, we believe that by tightly restricting the behavior of all privileged programs, exploitations of unknown vulnerabilities can be detected. A s Q check on the specifications, work is in progress on verifying them with respect to a n abstract security policy. This work is funded in part by the National Security Agency University Research Program under Contract No. DOD-MDA904-93-C4083 and by ARPA under Contract No. USNN00014-94-1-0065.
Unsupervised Anomaly Detection in Network Intrusion Detection Using Clusters

Most current network intrusion detection systems employ signature-based methods or data mining-based methods which rely on labelled training data. This training data is typically expensive to produce. Moreover, these methods have difficulty in detecting new types of attack. Using unsupervised anomaly detection techniques, however, the system can be trained with unlabelled data and is capable of detecting previously “unseen” attacks. In this paper, we present a new density-based and grid-based clustering algorithm that is suitable for unsupervised anomaly detection. We evaluated our methods using the 1999 KDD Cup data set. Our evaluation shows that the accuracy of our approach is close to that of existing techniques reported in the literature, and has several advantages in terms of computational complexity.