nils-herrmann/clean_scirepeval huggingface dataset:
S3Email: A method for securing emails from service providers

We often send our confidential information such as passport, credit card, social security numbers over email without concern about the security of email services. Existing network security mechanisms provide adequate security from external malicious adversaries and eavesdroppers, but they don't guarantee that the email service providers (ESPs) wouldn't or can't access our email data themselves, which in some cases could be highly confidential. One of the ways to protect email data from ESPs is to use Pretty Good Privacy (PGP) that has many limitations including key storage problem and dependability on third party services, making it cumbersome to use in practice. In this paper, we present S3Email method that provides email security against ESPs. The proposed method uses a cryptographic secret sharing technique in a novel way and encrypts the email metadata, body and attachments before the email is sent. In the proposed solution, the email sender and receiver must have at least two email accounts on the existing ESPs, which is not unusual today. Experiments and analysis show that the S3Email method provides information theoretic security with minimal computational overhead.
Exploring steganography: Seeing the unseen

Steganography is the art of hiding information in ways that prevent the detection of hidden messages. It includes a vast array of secret communications methods that conceal the message's very existence. These methods include invisible inks, microdots, character arrangement, digital signatures, covert channels, and spread spectrum communications. Steganography and cryptography are cousins in the spycraft family: cryptography scrambles a message so it cannot be understood while steganography hides the message so it cannot be seen. In this article the authors discuss image files and how to hide information in them, and discuss results obtained from evaluating available steganographic software. They argue that steganography by itself does not ensure secrecy, but neither does simple encryption. If these methods are combined, however, stronger encryption methods result. If an encrypted message is intercepted, the interceptor knows the text is an encrypted message. But with steganography, the interceptor may not know that a hidden message even exists. For a brief look at how steganography evolved, there is included a sidebar titled "Steganography: Some History."
ssEMnet: Serial-section Electron Microscopy Image Registration using a Spatial Transformer Network with Learned Features

The alignment of serial-section electron microscopy (ssEM) images is critical for efforts in neuroscience that seek to reconstruct neuronal circuits. However, each ssEM plane contains densely packed structures that vary from one section to the next, which makes matching features across images a challenge. Advances in deep learning has resulted in unprecedented performance in similar computer vision problems, but to our knowledge, they have not been successfully applied to ssEM image co-registration. In this paper, we introduce a novel deep network model that combines a spatial transformer for image deformation and a convolutional autoencoder for unsupervised feature learning for robust ssEM image alignment. This results in improved accuracy and robustness while requiring substantially less user intervention than conventional methods. We evaluate our method by comparing registration quality across several datasets.
A (sub)graph isomorphism algorithm for matching large graphs

We present an algorithm for graph isomorphism and subgraph isomorphism suited for dealing with large graphs. A first version of the algorithm has been presented in a previous paper, where we examined its performance for the isomorphism of small and medium size graphs. The algorithm is improved here to reduce its spatial complexity and to achieve a better performance on large graphs; its features are analyzed in detail with special reference to time and memory requirements. The results of a testing performed on a publicly available database of synthetically generated graphs and on graphs relative to a real application dealing with technical drawings are presented, confirming the effectiveness of the approach, especially when working with large graphs.
Improved website fingerprinting on Tor

In this paper, we propose new website fingerprinting techniques that achieve a higher classification accuracy on Tor than previous works. We describe our novel methodology for gathering data on Tor; this methodology is essential for accurate classifier comparison and analysis. We offer new ways to interpret the data by using the more fundamental Tor cells as a unit of data rather than TCP/IP packets. We demonstrate an experimental method to remove Tor SENDMEs, which are control cells that provide no useful data, in order to improve accuracy. We also propose a new set of metrics to describe the similarity between two traffic instances; they are derived from observations on how a site is loaded. Using our new metrics we achieve a higher success rate than previous authors. We conduct a thorough analysis and comparison between our new algorithms and the previous best algorithm. To identify the potential power of website fingerprinting on Tor, we perform open-world experiments; we achieve a recall rate over 95% and a false positive rate under 0.2% for several potentially monitored sites, which far exceeds previous reported recall rates. In the closed-world experiments, our accuracy is 91%, as compared to 86-87% from the best previous classifier on the same data.
A RISC-V vector processor with tightly-integrated switched-capacitor DC-DC converters in 28nm FDSOI

This work demonstrates a RISC-V vector microprocessor implemented in 28nm FDSOI with fully-integrated non-interleaved switched-capacitor DCDC (SC-DCDC) converters and adaptive clocking that generates four on-chip voltages between 0.5V and 1V using only 1.0V core and 1.8V IO voltage inputs. The design pushes the capabilities of dynamic voltage scaling by enabling fast transitions (20ns), simple packaging (no off-chip passives), low area overhead (16%), high conversion efficiency (80-86%), and high energy efficiency (26.2 DP GFLOPS/W) for mobile devices.
GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution

Generative Adversarial Networks (GAN) have limitations when the goal is to generate sequences of discrete elements. The reason for this is that samples from a distribution on discrete objects such as the multinomial are not differentiable with respect to the distribution parameters. This problem can be avoided by using the Gumbel-softmax distribution, which is a continuous approximation to a multinomial distribution parameterized in terms of the softmax function. In this work, we evaluate the performance of GANs based on recurrent neural networks with Gumbel-softmax output distributions in the task of generating sequences of discrete elements.
Computational Methods in

Statistical authorship attribution has a long history, culminating in the use of modern machine learning classification methods. Nevertheless, most of this work suffers from the limitation of assuming a small closed set of candidate authors and essentially unlimited training text for each. Real-life authorship attribution problems, however, typically fall short of this ideal. Thus, following detailed discussion of previous work, three scenarios are considered here for which solutions to the basic attribution problem are inadequate. In the first variant, the profiling problem, there is no candidate set at all; in this case, the challenge is to provide as much demographic or psychological information as possible about the author. In the second variant, the needle-in-a-haystack problem, there are many thousands of candidates for each of whom we might have a very limited writing sample. In the third variant, the verification problem, there is no closed candidate set but there is one suspect; in this case, the challenge is to determine if the suspect is or is not the author. For each variant, it is shown how machine learning methods can be adapted to handle the special challenges of that variant.
High-Sensitivity Software-Configurable 5.8-GHz Radar Sensor Receiver Chip in 0.13-$\mu$ m CMOS for Noncontact Vital Sign Detection

In this paper, analyses on sensitivity and link budget have been presented to guide the design of high-sensitivity noncontact vital sign detector. Important design issues such as flicker noise, baseband bandwidth, and gain budget have been discussed with practical considerations of analog-to-digital interface and signal processing methods in noncontact vital sign detection. Based on the analyses, a direct-conversion 5.8-GHz radar sensor chip with 1-GHz bandwidth was designed and fabricated. This radar sensor chip is software configurable to set the operation point and detection range for optimal performance. It integrates all the analog functions on-chip so that the output can be directly sampled for digital signal processing. Measurement results show that the fabricated chip has a sensitivity of better than -101 dBm for ideal detection in the absence of random body movement. Experiments have been performed successfully in laboratory environment to detect the vital signs of human subjects.
ON CLASSIFICATION OF BIOLOGICAL DATA USING OUTLIER DETECTION

With the rapid development of information technology, the number of datasets, as well as their complexity and dimension, have been growing dramatically. This dramatic growth of biology data and non-biological commercial databases becomes a challenging issue in data mining. Classification technique is one of the major tools in the captured research area. However, the performance of classification may be degraded when there exists noise in the captured databases. Therefore, outlier detection becomes an urgent need and the issue of how to integrate outlier detection method and classification techniques is an important and challenging issue. In this paper, we proposed a novel and effective approach based on k-means clustering to identify outliers in the databases. In particular, we employed one of famous classification techniques, Support Vector Machine (SVM), owing to its ability to handle highdimensional data set. We also compare the classification results with the multivariate outlier detection method. Numerical results on two different data sets indicate that the classification results after removing the outliers by our proposed method are much better than the multivariate outlier detection method.
Two routes to emotional memory: distinct neural processes for valence and arousal.

Prior investigations have demonstrated that emotional information is often better remembered than neutral information, but they have not directly contrasted effects attributable to valence and those attributable to arousal. By using functional MRI and behavioral studies, we found that distinct cognitive and neural processes contribute to emotional memory enhancement for arousing information versus valenced, nonarousing information. The former depended on an amygdalar-hippocampal network, whereas the latter was supported by a prefrontal cortex-hippocampal network implicated in controlled encoding processes. A behavioral companion study, with a divided-attention paradigm, confirmed that memory enhancement for valenced, nonarousing words relied on controlled encoding processes: concurrent task performance reduced the enhancement effect. Enhancement for arousing words occurred automatically, even when encoding resources were diverted to the secondary task.
Excitation Control Method for a Low Sidelobe SIW Series Slot Array Antenna With 45$^{\circ}$  Linear Polarization

A sidelobe suppression method for a series slot array antenna which radiates 45° -inclined linear polarization is proposed. Axial displacements are employed to create arbitrary excitation coefficients for individual centered-inclined radiating slots along the center line of a broad wall. To verify the proposed design method, we design two types of center-fed linear slot array antennas with a Dolph-Chebyshev distribution for -20 dB and -26 dB sidelobe levels (SLLs) in the Ka band. Furthermore, a cross-validation process involving an equivalent circuit model analysis and electromagnetic full-wave simulation using CST MWS is utilized. The entire structure of the proposed series slot array antenna is fabricated on printed circuit boards (PCBs), including drilling and chemical etching, to secure advantages of miniaturization and cost reduction. The measured realized gains are 15.17 and 15.95 dBi and SLLs are -18.7 and -22.5 dB respectively for two types of fabricated antennas. It demonstrates the validity of the proposed sidelobe suppression method.
High Reliability and Efficiency Single-Phase Transformerless Inverter for Grid-Connected Photovoltaic Systems

This paper presents a high-reliability single-phase transformerless grid-connected inverter that utilizes superjunction MOSFETs to achieve high efficiency for photovoltaic applications. The proposed converter utilizes two split ac-coupled inductors that operate separately for positive and negative half grid cycles. This eliminates the shoot-through issue that is encountered by traditional voltage source inverters, leading to enhanced system reliability. Dead time is not required at both the high-frequency pulsewidth modulation switching commutation and the grid zero-crossing instants, improving the quality of the output ac-current and increasing the converter efficiency. The split structure of the proposed inverter does not lead itself to the reverse-recovery issues for the main power switches and as such superjunction MOSFETs can be utilized without any reliability or efficiency penalties. Since MOSFETs are utilized in the proposed converter high efficiency can be achieved even at light load operations achieving a high California energy commission (CEC) or European union efficiency of the converter system. It also has the ability to operate at higher switching frequencies while maintaining high efficiency. The higher operating frequencies with high efficiency enables reduced cooling requirements and results in system cost savings by shrinking passive components. With two additional ac-side switches conducting the currents during the freewheeling phases, the photovoltaic array is decoupled from the grid. This reduces the high-frequency common-mode voltage leading to minimized ground loop leakage current. The operation principle, common-mode characteristic and design considerations of the proposed transformerless inverter are illustrated. The total losses of the power semiconductor devices of several existing transformerless inverters which utilize MOSFETs as main switches are evaluated and compared. The experimental results with a 5 kW prototype circuit show 99.0% CEC efficiency and 99.3% peak efficiency with a 20 kHz switching frequency. The high reliability and efficiency of the proposed converter makes it very attractive for single-phase transformerless photovoltaic inverter applications.
News personalization using the CF-IDF semantic recommender

When recommending news items, most of the traditional algorithms are based on TF-IDF, i.e., a term-based weighting method which is mostly used in information retrieval and text mining. However, many new technologies have been made available since the introduction of TF-IDF. This paper proposes a new method for recommending news items based on TF-IDF and a domain ontology. It is demonstrated that adapting TF-IDF with the semantics of a domain ontology, resulting in Concept Frequency - Inverse Document Frequency (CF-IDF), yields better results than using the original TF-IDF method. CF-IDF is built and tested in Athena, a recommender extension to the Hermes news personalization framework. Athena employs a user profile to store concepts or terms found in news items browsed by the user. The framework recommends new articles to the user using a traditional TF-IDF recommender and the CF-IDF recommender. A statistical evaluation of both methods shows that the use of an ontology significantly improves the performance of a traditional recommender.
Architecting the next generation of service-based SCADA/DCS system of systems

SCADA and DCS systems are in the heart of the modern industrial infrastructure. The rapid changes in the networked embedded systems and the way industrial applications are designed and implemented, call for a shift in the architectural paradigm. Next generation SCADA and DCS systems will be able to foster cross-layer collaboration with the shop-floor devices as well as in-network and enterprise applications. Ecosystems driven by (web) service based interactions will enable stronger coupling of real-world and the business side, leading to a new generation of monitoring and control applications and services witnessed as the integration of large-scale systems of systems that are constantly evolving to address new user needs.
yaSpMV: yet another SpMV framework on GPUs

SpMV is a key linear algebra algorithm and has been widely used in many important application domains. As a result, numerous attempts have been made to optimize SpMV on GPUs to leverage their massive computational throughput. Although the previous work has shown impressive progress, load imbalance and high memory bandwidth remain the critical performance bottlenecks for SpMV. In this paper, we present our novel solutions to these problems. First, we devise a new SpMV format, called blocked compressed common coordinate (BCCOO), which uses bit flags to store the row indices in a blocked common coordinate (COO) format so as to alleviate the bandwidth problem. We further improve this format by partitioning the matrix into vertical slices to enhance the cache hit rates when accessing the vector to be multiplied. Second, we revisit the segmented scan approach for SpMV to address the load imbalance problem. We propose a highly efficient matrix-based segmented sum/scan for SpMV and further improve it by eliminating global synchronization. Then, we introduce an auto-tuning framework to choose optimization parameters based on the characteristics of input sparse matrices and target hardware platforms. Our experimental results on GTX680 GPUs and GTX480 GPUs show that our proposed framework achieves significant performance improvement over the vendor tuned CUSPARSE V5.0 (up to 229% and 65% on average on GTX680 GPUs, up to 150% and 42% on average on GTX480 GPUs) and some most recently proposed schemes (e.g., up to 195% and 70% on average over clSpMV on GTX680 GPUs, up to 162% and 40% on average over clSpMV on GTX480 GPUs).
Understanding the perpetration of employee computer crime in the organisational context

While hackers and viruses fuel the IS security concerns for organisations, the problems posed by employee computer crime should not be underestimated. Indeed, a growing number of IS security researchers have turned their attention to the 'insider' threat. However, to date, there has been a lack of insight into the relationship between the actual behaviour of offenders during the perpetration of computer crime, and the organisational context in which the behaviour takes place. To address this deficiency, this paper advances two criminological theories, which it is argued can be used to examine the stages an offender must go through in order for a crime to be committed. In addition, this paper illustrates how the two theories, entitled the Rational Choice Perspective and Situational Crime Prevention, can be applied to the IS domain, thereby offering a theoretical basis on which to analyse the offender/context relationship during the perpetration of computer crime. By so doing, practitioners may use these insights to inform and enhance the selection of safeguards in a bid to improve prevention programmes.
Fusion of laserscannner and video based lanemarking detection for robust lateral vehicle control and lane change maneuvers

The knowledge about lanes and the exact position on the road is fundamental for many advanced driver assistance systems. In this paper, a novel iterative histogram based approach with occupancy grids for the detection of multiple lanes is proposed. In highway scenarios, our approach is highly suitable to determine the correct number of all existing lanes on the road. Additionally, the output of the laserscannner based lane detection is fused with a production-available vision based system. It is shown that both sensor systems perfectly complement each other to increase the robustness of a lane tracking system. The achieved accuracy of the fusion system, the laserscannner and video based system is evaluated with a highly accurate DGPS to investigate the performance with respect to lateral vehicle control applications.
Effective Identification of Similar Patients Through Sequential Matching over ICD Code Embedding

Evidence-based medicine often involves the identification of patients with similar conditions, which are often captured in ICD (International Classification of Diseases (World Health Organization 2013)) code sequences. With no satisfying prior solutions for matching ICD-10 code sequences, this paper presents a method which effectively captures the clinical similarity among routine patients who have multiple comorbidities and complex care needs. Our method leverages the recent progress in representation learning of individual ICD-10 codes, and it explicitly uses the sequential order of codes for matching. Empirical evaluation on a state-wide cancer data collection shows that our proposed method achieves significantly higher matching performance compared with state-of-the-art methods ignoring the sequential order. Our method better identifies similar patients in a number of clinical outcomes including readmission and mortality outlook. Although this paper focuses on ICD-10 diagnosis code sequences, our method can be adapted to work with other codified sequence data.
Background Subtraction Algorithms with Post-processing: A Review

Due to its several algorithms with their fast implementations, background subtraction becomes a very important step in many computer vision and video surveillance systems which assume static cameras. Literature counts a large number of robust background subtraction algorithms which try each to outperform the others in a quantitative and qualitative manner. This competition can sometimes confuse the user of this kind of process and make the choice of one of them difficult. To overcome this issue we review, in what follows, the background subtraction process by defining it and exploring most used algorithms of background subtraction. We then expose some post processing techniques used to remove superfluous content derived from background subtraction.
A survey on image-based rendering - representation, sampling and compression

Image-based rendering (IBR) has attracted a lot of research interest recently. In this paper, we survey the various techniques developed for IBR, including representation, sampling and compression. The goal is to provide an overview of research for IBR in a complete and systematic manner. We observe that essentially all the IBR representations are derived from the plenoptic function, which is seven dimensional and difficult to handle. We classify various IBR representations into two categories based on how the plenoptic function is simplified, namely restraining the viewing space and introducing source descriptions. In the former category, we summarize six common assumptions that were often made in various approaches and discuss how the dimension of the plenoptic function can be reduced based on these assumptions. In the latter category, we further categorize the methods based on what kind of source description was introduced, such as scene geometry, texture map or reflection model. Sampling and compression are also discussed respectively for both categories.