nils-herrmann/clean_scirepeval huggingface dataset:
Learning Rules from System Call Arguments and Sequences for Anomaly 20 Detection

Behavior-based security systems are a new generation of computer security technologies that defend and protect critical IT assets by detecting deviations from a system's normal behavior. Behaviorbased security systems provide the means of detecting attacks from remote sources, and from within, i.e. the insider problem. The Email Mining Toolkit (EMT) is a data mining system that computes behavior profiles or models of user email accounts. These models may be used for a variety of forensic analyses and detection tasks. In this talk we describe the application of these models to detect the early onset of a viral propagation without "contentbased" (or signature-based) analysis in common use in virus scanners. We present several experiments using real email from 15 users with injected simulated viral emails and describe how the combination of different behavior models improves overall detection rates. The performance results vary depending upon parameter settings, approaching 99% true positive (TP) (percentage of viral emails caught) in general cases and with 0.38% false positive (FP) (percentage of emails with attachments that are mislabeled as viral). The principle behind behavior-based security is to model communication flows between systems and users, (possibly including content) using well grounded statistical techniques. The statistics gathered may be used to determine "social clique and communication communities" that typically exchange information, and the frequency of messages and the typical times and days those messages are exchanged. All this information can be used to model accounts, hosts or systems to determine typical behaviors that may be used to detect deviations of interest that may indicate misbehavior or security breaches. We believe EMT thus serves as a model anomaly detection system for any audit stream and detection problem of interest. This work suggests a general framework that is the subject matter of our ongoing work. This framework posits that anomaly detection is best cast as a problem to optimally correlate multiple detectors, where each detector models normal behavior using different features of the audit stream. These detectors generate alerts when there are violations of volume and velocity statistics, anomalous values exhibited in an audit stream, and abnormal or inconsistent formation of vertices when viewing data in the audit stream in graph theoretic formulations. All of these concepts and modeling techniques are embodied in EMT. One Class Support Vector Machines for Detecting Anomalous Windows Registry
Can Personality Be Changed ? The Role of Beliefs in Personality and Change

Using recent research, I argue that beliefs lie at the heart of personality and adaptive functioning and that they give us unique insight into how personality and functioning can be changed. I focus on two classes of beliefs—beliefs about the malleability of self-attributes and expectations of social acceptance versus rejection—and show how modest interventions have brought about important real-world changes. I conclude by suggesting that beliefs are central to the way in which people package their experiences and carry them forward, and that beliefs should play a more central role in the study of personality. personality; personality theories; change; self-beliefs;
An Optimal Algorithm for Finding the Kernel of a Polygon

The kernel K(P) of a simple polygon P wah n verUces is the locus of the points internal to P from which all verUces of P are wslble Equwalently, K(P) is the mtersectmn of appropriate half-planes determined by the polygon's edges Although it is known that to find the intersection of n generic half-planes requires time O(n log n), we show that one can exploit the ordering of the half-planes corresponding to the sequence of the polygon's edges to obtain a kernel finding algorithm which runs m time O(n) and is therefore optimal
ROUTERLESS NETWORKS-ON-CHIP

Traditional bus-based interconnects are simple and easy to implement, but the scalability is greatly limited. While router-based networks-on-chip (NoCs) offer superior scalability, they also incur significant power and area overhead due to complex router structures. In this paper, we explore a new class of on-chip networks, referred to as Routerless NoCs, where routers are completely eliminated. We propose a novel design that utilizes on-chip wiring resources smartly to achieve comparable hop count and scalability as router-based NoCs. Several effective techniques are also proposed that significantly reduce the resource requirement to avoid new network abnormalities in routerless NoC designs. Evaluation results show that, compared with a conventional mesh, the proposed routerless NoC achieves 9.5X reduction in power, 7.2X reduction in area, 2.5X reduction in zero-load packet latency, and 1.7X increase in throughput. Compared with a state-of-the-art low-cost NoC design, the proposed approach achieves 7.7X reduction in power, 3.3X reduction in area, 1.3X reduction in zero-load packet latency, and 1.6X increase
Local optima smoothing for global optimization

It is widely believed that in order to solve large scale global optimization problems an appropriate mixture of local approximation and global exploration is necessary. Local approximation, if first order information on the objective function is available, is efficiently performed by means of local optimization methods. Unfortunately, global exploration, in absence of some kind of global information on the problem, is a “blind” procedure, aimed at placing observations as evenly as possible in the search domain. Often this procedure reduces to uniform random sampling (like in Multistart algorithms, or in techniques based on clustering). In this paper we propose a new framework for global exploration which tries to guide random exploration towards the region of attraction of low-level local optima. The main idea originated by the use of smoothing techniques (based on gaussian convolutions): the possibility of applying a smoothing transformation not to the objective function but to the result of local searches seems to have never been explored yet. Although an exact smoothing of the results of local searches is impossible to implement, in this paper we propose a computational approximation scheme which has proven to be very efficient and (maybe more important) extremely robust in solving large scale global optimization problems with huge numbers of local optima.
Static and Moving Object Detection Using Flux Tensor with Split Gaussian Models

In this paper, we present a moving object detection system named Flux Tensor with Split Gaussian models (FTSG) that exploits the benefits of fusing a motion computation method based on spatio-temporal tensor formulation, a novel foreground and background modeling scheme, and a multi-cue appearance comparison. This hybrid system can handle challenges such as shadows, illumination changes, dynamic background, stopped and removed objects. Extensive testing performed on the CVPR 2014 Change Detection benchmark dataset shows that FTSG outperforms state-of-the-art methods.
Design Approach to a Novel Dual-Mode Wideband Circular Sector Patch Antenna

A design approach to a novel wideband circular sector patch antenna is proposed. Design guidelines are laid down based on an approximate 1.5-wavelength, multimode magnetic dipole, and the cavity model. Then, the flared angle of the circular sector patch and the corresponding usable resonant modes for wideband radiation are determined. It is demonstrated that the resonant TM4/3,1 and the TM8/3,1 modes within a 270° circular sector patch radiator can be simultaneously excited, perturbed, and employed to form a wideband unidirectional radiation characteristic with two resonances. Prototype antennas are designed and fabricated to experimentally validate the dual-resonant wideband property on a single-layered substrate. It is further demonstrated that the antenna designed on a 5-mm-thick air substrate exhibits an available radiation bandwidth (ARB) of 14.5%, while the printed one designed on a 2-mm-thick modified Teflon substrate exhibits an ARB of 6.5%. It is evidently validated that the proposed approach can be employed to effectively enhance the operational bandwidth of microstrip patch antennas without increasing antenna profile, inquiring multiple radiators or employing reactance compensation techniques.
Crowdsourcing of Pollution Data using Smartphones

In this paper we present our research into participatory sensing based solutions for the collection of data on urban pollution and nuisance. In the past 2 years we have been involved in the NoiseTube project which explores a crowdsourcing approach to measuring and mapping urban noise pollution using smartphones. By involving the general public and using off-the-shelf smartphones as noise sensors, we seek to provide a low cost solution for citizens to measure their personal exposure to noise in their everyday environment and participate in the creation of collective noise maps by sharing their geo-localized and annotated measurements with the community. We believe our work represents an interesting example of the novel mobile crowdsourcing applications which are enabled by ubiquitous computing systems. Furthermore we believe the NoiseTube system, and the currently ongoing validation experiments, provide an illustrative context for some of the open challenges faced by creators of ubiquitous crowdsourcing applications and services in general. We will also take the opportunity to present the insights we gained into some of the challenges.
MIMIC-CXR: A large publicly available database of labeled chest radiographs

Chest radiography is an extremely powerful imaging modality, allowing for a detailed inspection of a patient’s thorax, but requiring specialized training for proper interpretation. With the advent of high performance general purpose computer vision algorithms, the accurate automated analysis of chest radiographs is becoming increasingly of interest to researchers. However, a key challenge in the development of these techniques is the lack of sufficient data. Here we describe MIMIC-CXR, a large dataset of 371,920 chest x-rays associated with 227,943 imaging studies sourced from the Beth Israel Deaconess Medical Center between 2011 2016. Each imaging study can pertain to one or more images, but most often are associated with two images: a frontal view and a lateral view. Images are provided with 14 labels derived from a natural language processing tool applied to the corresponding free-text radiology reports. All images have been de-identified to protect patient privacy. The dataset is made freely available to facilitate and encourage a wide range of research in medical computer vision.
Substrate integrated waveguide power divider based on multimode interference imaging

A 1×N (with arbitrary N) power divider based on multimode interference imaging in substrate integrated waveguide (SIW) technology is proposed. This divider is demonstrated by fully-wave analysis and experimental results in the case of a division ratio of 1/6. It is implemented at Ka band with the use of continuous wall SIW geometry. The insertion loss from the input to each output of the divider at 25 GHz is −9 dB. Compared with traditional corporate dividing structures, this divider has a much simpler configuration and also a lower design complexity. This device may find applications in the design of millimeter-wave and quasi-optical spatial power combiners, antenna feeding networks and amplifiers.
Scalable Visualisation of Sentiment and Stance

Natural language processing systems have the ability to analyse not only the sentiment of human language, but also the stance of the speaker. Representing this information visually from unevenly distributed and potentially sparse datasets is challenging, in particular when trying to facilitate exploration and knowledge discovery. We present work on a novel visualisation approach for scalable visualisation of sentiment and stance and provide a language resource of e-government public engagement of 9,278 user comments with stance explicitly declared by the author.
Python robotics: an environment for exploring robotics beyond LEGOs

This paper describes Pyro, a robotics programming environment designed to allow inexperienced undergraduates to explore topics in advanced robotics. Pyro, which stands for Python Robotics, runs on a number of advanced robotics platforms. In addition, programs in Pyro can abstract away low-level details such that individual programs can work unchanged across very different robotics hardware. Results of using Pyro in an undergraduate course are discussed.
A Survey for a Virtual Fitting Room by a Mixed Reality Technology*

Mixed Reality (MR) has become popular in recent years, due to the fact that it can be applied to many areas, including new learning media, medical training, and manufacturing. Since MR can simulate 3D objects and environments, bringing the user into a virtual world, it allows more perspectives than are seen by using traditional media. MR can be applied to simulate a real fitting room, where the user can try on a virtual dress without taking off any of their clothes. Additionally, it can estimate the size of the user, so they will know their correct clothing size before buying. This MR technology is a powerful decisionmaking tool for customers in an online clothes shopping environment. By simulating a virtual garment superimposed upon the user body, users can try on virtual clothes as a mirror image, with several angles viewable as they move. This technique can also help customers shop online to select the correct size or type of garment. This should increase confidence when buying garments from websites. Due to advancements in computer technology, a new way to create virtual fitting rooms can be used. In this paper, we have surveyed methodologies and experiments conducted in building virtual fitting rooms. From the survey, it was found that making a good virtual fitting room relies on 2 important factors, i.e., the method of detecting the user’s body size, position, and movement, and the method of displaying the virtual garments superimposed on the user’s body. The first factors are usually solved either by using devices, like Microsoft’s Kinect or the Image Processing technique. The second factor can be accomplished by using augmented reality technology.
Modeling and Verification of a Fault-Tolerant Real-Time Startup Protocol Using Calendar Automata

We discuss the modeling and verification of real-time systems using the SAL model checker. A new modeling framework based on event calendars enables dense timed systems to be described without relying on continuously varying clocks. We present verification techniques that rely on induction and abstraction, and show how these techniques are e ffici ntly supported by the SAL symbolic model-checking tools. The modeling and verification method is applied to the fault-tolerant real-time startup protocol used in the Timed Triggered Architecture.
Analytical and Practical Analysis of Switched-Capacitor DC-DC Converters

Switched-capacitor DC-DC converters are useful alternatives to inductor-based converters in many lowpower and medium-power applications. This work develops a straightforward analysis method to determine a switched-capacitor converter’s output impedance (a measure of performance and power loss). This resistive impedance is a function of frequency and has two asymptotic limits, one corresponding to very high switching frequency where resistive paths dominate the impedance, and one corresponding to very low switching frequency where charge transfers among idealized capacitors dominate the impedance. An optimization method is developed to improve the performance of these converters through component sizing based on practical constraints. Several switched-capacitor converter topologies are compared in the two asymptotic limits. Switched-capacitor converter performance (based on conduction loss) is compared with that of two magnetics-based DC-DC converters. At moderate to high conversion ratios, the switchedcapacitor converter has significantly less conduction loss than an inductor-based buck converter. Some aspects of converter implementation are discussed, including the power loss due to device parasitics and methods for transistor control. Implementation using both integrated and discrete devices is discussed. With the correct analysis methods, switched-capacitor DC-DC converters can provide an attractive alternative to conventional power converters.
Information-Theoretic Measures for Anomaly Detection

Anomaly detection is an essential component of the protection mechanisms against novel attacks. In this paper, we propose to use several information-theoretic measures, namely, entropy, conditional entropy, relative conditional entropy, information gain, and information cost for anomaly detection. These measures can be used to describe the characteristics of an audit data set, suggest the appropriate anomaly detection model(s) to be built, and explain the performance of the model(s). We use case studies on Unix system call data, BSM data, and network tcpdump data to illustrate the utilities of these measures.
A Comparison of Document, Sentence, and Term Event Spaces

The trend in information retrieval systems is from document to sub-document retrieval, such as sentences in a summarization system and words or phrases in question-answering system. Despite this trend, systems continue to model language at a document level using the inverse document frequency (IDF). In this paper, we compare and contrast IDF with inverse sentence frequency (ISF) and inverse term frequency (ITF). A direct comparison reveals that all language models are highly correlated; however, the average ISF and ITF values are 5.5 and 10.4 higher than IDF. All language models appeared to follow a power law distribution with a slope coefficient of 1.6 for documents and 1.7 for sentences and terms. We conclude with an analysis of IDF stability with respect to random, journal, and section partitions of the 100,830 full-text scientific articles in our experimental corpus.
eCryptfs: An Enterprise-class Encrypted Filesystem for Linux

eCryptfs is a cryptographic filesystem for Linux that stacks on top of existing filesystems. It provides functionality similar to that of GnuPG, except the process of encrypting and decrypting the data is done transparently from the perspective of the application. eCryptfs leverages the recently introduced Linux kernel keyring service, the kernel cryptographic API, the Linux Pluggable Authentication Modules (PAM) framework, OpenSSL/GPGME, the Trusted Platform Module (TPM), and the GnuPG keyring in order to make the process of key and authentication token management seamless to the end user. 1 Enterprise Requirements Any cryptographic application is hard to implement correctly and hard to effectively deploy. When key management and interaction with the cryptographic processes are cumbersome and unwieldy, people will tend to ignore, disable, or circumvent the security measures. They will select insecure passphrases, mishandle their secret keys, or fail to encrypt their sensitive data altogether. This places the confidentiality and the integrity of the data in jeopardy of compromise in the event of unauthorized access to the media on which the data is stored. While users and administrators take great pains to configure access control mechanisms, including measures such as user account and privilege separation, Mandatory Access Control[13], and biometric identification, they often fail to fully consider the circumstances where none of these technologies can have any effect – for example, when the media itself is separated from the control of its host environment. In these cases, access control must be enforced via cryptography. When a business process incorporates a cryptographic solution, it must take several issues into account. How will this affect incremental backups? What sort of mitigation is in place to address key loss? What sort of education is required on the part of the employees? What should the policies be? Who should decide them, and how are they expressed? How disruptive or costly will this technology be? What class of cryptography is appropriate, given the risks? Just what are the risks, anyway? Whenever sensitive data is involved, it is incumbent upon those responsible for the information to reflect on these sorts of questions and to take action accordingly. We see today that far too many businesses
Novel Multilevel Inverter Carrier-Based PWM Method

The advent of the transformerless multilevel inverter topology has brought forth various pulsewidth modulation (PWM) schemes as a means to control the switching of the active devices in each of the multiple voltage levels in the inverter. An analysis of how existing multilevel carrier-based PWM affects switch utilization for the different levels of a diode-clamped inverter is conducted. Two novel carrier-based multilevel PWM schemes are presented which help to optimize or balance the switch utilization in multilevel inverters. A 10-kW prototype sixlevel diode-clamped inverter has been built and controlled with the novel PWM strategies proposed in this paper to act as a voltage-source inverter for a motor drive.
Gated Hierarchical Attention for Image Captioning

Attention modules connecting encoder and decoders have been widely applied in the field of object recognition, image captioning, visual question answering and neural machine translation, and significantly improves the performance. In this paper, we propose a bottomup gated hierarchical attention (GHA) mechanism for image captioning. Our proposed model employs a CNN as the decoder which is able to learn different concepts at different layers, and apparently, different concepts correspond to different areas of an image. Therefore, we develop the GHA in which low-level concepts are merged into high-level concepts and simultaneously low-level attended features pass to the top to make predictions. Our GHA significantly improves the performance of the model that only applies one level attention, e.g., the CIDEr score increases from 0.923 to 0.999, which is comparable to the state-of-theart models that employ attributes boosting and reinforcement learning (RL). We also conduct extensive experiments to analyze the CNN decoder and our proposed GHA, and we find that deeper decoders cannot obtain better performance, and when the convolutional decoder becomes deeper the model is likely to collapse during training.
Efficient and Autonomous Energy Management Techniques for the Future Smart Homes

Smart grid with latest technologies provides solid foundation for the implementation of energy management systems at home premises. This paper proposes an autonomous energy management-based cost reduction solution for peak load times using a home energy management system (HEMS). Within a home environment, both the real time and the schedulable appliances are connected with smart meter through HEMS. We formulate an optimization problem under various practical constraints, which is shown to be a mixed integer programming problem that can be solved through a step-wise approach. A novel scheme based on Dijkstra algorithm is proposed, which results in the similar performance to that of the proposed optimal scheme while exhibiting much lower complexity. To further save the computational efforts, a low complexity scheme is also proposed, which produces considerably better results than the non-optimized scheme with the same complexity yet. Simulation results are presented at show the performance and complexity comparison of different proposed solutions and the existing methods.