nils-herrmann/clean_scirepeval huggingface dataset:
Improving malware detection by applying multi-inducer ensemble

Detection of malicious software (malware) using ma chine learning methods has been explored extensively to enable fas t detection of new released malware. The performance of these classifiers depen ds on the induction algorithms being used. In order to benefit from mul tiple different classifiers, and exploit their strengths we suggest using an ens emble method that will combine the results of the individual classifiers i nto one final result to achieve overall higher detection accuracy. In this paper we evaluate several combining methods using five different base inducers (C4.5 Dec ision Tree, Naïve Bayes, KNN, VFI and OneR) on five malware datasets. The mai n goal is to find the best combining method for the task of detecting mal icious files in terms of accuracy, AUC and execution time.
Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics

We consider the task of estimating, from observed data, a pro babilistic model that is parameterized by a finite number of parameters. In particular, we are consid ering the situation where the model probability density function is unnormalized. That is, the model is only specified up to the partition function. The partition function normalizes a model so that it integrates to one for any choice of the parameters. However, it is often impossible to obtain it closed form. Gibbs distributions, Markov and multi-layer networks are examples of models wher e analytical normalization is often impossible. Maximum likelihood estimation can then not be u s d without resorting to numerical approximations which are often computationally expensive . W propose here a new objective function for the estimation of both normalized and unnormalized models. The basic idea is to perform nonlinear logistic regression to discriminate between the observed data and some artificially generated noise. With this approach, the normalizing partition f u ction can be estimated like any other parameter. We prove that the new estimation method leads to a consistent (convergent) estimator of the parameters. For large noise sample sizes, the new esti mator is furthermore shown to behave like the maximum likelihood estimator. In the estimati on of unnormalized models, there is a trade-off between statistical and computational performa nce. We show that the new method strikes a competitive trade-off in comparison to other estimation m ethods for unnormalized models. As an application to real data, we estimate novel two-layer model s of natural image statistics with spline nonlinearities.
Edit Distance: Sketching, Streaming, and Document Exchange

We show that in the document exchange problem, where Alice holds x ϵ {0, 1}n and Bob holds y ϵ {0, 1}n, Alice can send Bob a message of size O(K(log2 K + log n)) bits such that Bob can recover x using the message and his input y if the edit distance between x and y is no more than K, and output "error" otherwise. Both the encoding and decoding can be done in time Õ(n + poly(K)). This result significantly improves on the previous communication bounds under polynomial encoding/decoding time. We also show that in the referee model, where Alice and Bob hold x and y respectively, they can compute sketches of x and y of sizes poly(K log n) bits (the encoding), and send to the referee, who can then compute the edit distance between x and y together with all the edit operations if the edit distance is no more than K, and output "error" otherwise (the decoding). To the best of our knowledge, this is the first result for sketching edit distance using poly(K log n) bits. Moreover, the encoding phase of our sketching algorithm can be performed by scanning the input string in one pass. Thus our sketching algorithm also implies the first streaming algorithm for computing edit distance and all the edits exactly using poly(K log n) bits of space.
A Novel Method for Comparative Analysis of DNA Sequences by Ramanujan-Fourier Transform

Alignment-free sequence analysis approaches provide important alternatives over multiple sequence alignment (MSA) in biological sequence analysis because alignment-free approaches have low computation complexity and are not dependent on high level of sequence identity. However, most of the existing alignment-free methods do not employ true full information content of sequences and thus can not accurately reveal similarities and differences among DNA sequences. We present a novel alignment-free computational method for sequence analysis based on Ramanujan-Fourier transform (RFT), in which complete information of DNA sequences is retained. We represent DNA sequences as four binary indicator sequences and apply RFT on the indicator sequences to convert them into frequency domain. The Euclidean distance of the complete RFT coefficients of DNA sequences are used as similarity measures. To address the different lengths of RFT coefficients in Euclidean space, we pad zeros to short DNA binary sequences so that the binary sequences equal the longest length in the comparison sequence data. Thus, the DNA sequences are compared in the same dimensional frequency space without information loss. We demonstrate the usefulness of the proposed method by presenting experimental results on hierarchical clustering of genes and genomes. The proposed method opens a new channel to biological sequence analysis, classification, and structural module identification.
Android anti-forensics through a local paradigm

Mobile devices are among the most disruptive technologies of the last years, gaining even more diffusion and success in the daily life of a wide range of people categories. Unfortunately, while the number of mobile devices implicated in crime activities is relevant and growing, the capability to perform the forensic analysis of such devices is limited both by technological and methodological problems. In this paper, we focus on Anti-Forensic techniques applied to mobile devices, presenting some fully automated instances of such techniques to Android devices. Furthermore, we tested the effectiveness of such techniques versus both the cursory examination of the device and some acquisition tools. a 2010 Digital Forensic Research Workshop.
Compact Single- and Dual-Band Filtering Patch Antenna Arrays Using Novel Feeding Scheme

This paper presents two compact patch antenna arrays using a novel feeding scheme to integrate bandpass responses. The two arrays have no filtering circuits but reveal high filtering performance. For each array, only a microstrip feed line is coupled to radiating patches and specific coupling regions are employed to obtain the desired current distribution on the radiating patches. In the operating band, the induced currents on the patches are in phase, resulting in high boresight gain. At the out-of-band frequencies, the patches are not excited or the induced currents on them are 180° out of phase, resulting in radiation suppression. Using this method, out-of-band radiation can be suppressed without degrading the in-band performance. For demonstration, a four-element planar patch array is first implemented. Single-band bandpass response is observed in the experiment. Measured in-band gains are 12.6 dBi, whereas out-of-band suppression is more than 20 dB. Then the feeding scheme is employed to design two three-element subarrays to form a planar dual-band patch array. Also, dual-band bandpass responses are obtained with high frequency selectivity.
Don't Trust the Cloud, Verify: Integrity and Consistency for Cloud Object Stores

Cloud services have turned remote computation into a commodity and enable convenient online collaboration. However, they require that clients fully trust the service provider in terms of confidentiality, integrity, and availability. Toward reducing this dependency, this article introduces VICOS, a protocol for verification of integrity and consistency for cloud object storage that enables a group of mutually trusting clients to detect data integrity and consistency violations for a cloud object storage service. It aims at services where multiple clients cooperate on data stored remotely on a potentially misbehaving service. VICOS enforces the consistency notion of fork-linearizability, supports wait-free client semantics for most operations, and reduces the computation and communication overhead compared to previous protocols. VICOS is based on a generic authenticated data structure. Moreover, its operations cover the hierarchical name space of a cloud object store, supporting a real-world interface and not only a simplistic abstraction. A prototype of VICOS that works with the key-value store interface of commodity cloud storage services has been implemented, and an evaluation demonstrates its advantage compared to existing systems.
A Digital Signature Scheme Secure Against Adaptive Chosen-Message Attacks

Shafi Goldwasser∗∗ Silvio Micali∗∗ Ronald L. Rivest ∗∗  We present a digital signature scheme based on the computational difficulty of integer factorization. The scheme possesses the novel property of being robust against an adaptive chosen-message attack: an adversary who receives signatures for messages of his choice (where each message may be chosen in a way that depends on the signatures of previously chosen messages) can not later forge the signature of even a single additional message. This may be somewhat surprising, since the properties of having forgery being equivalent to factoring and being invulnerable to an adaptive chosen-message attack were considered in the folklore to be contradictory. More generally, we show how to construct a signature scheme with such properties based on the existence of a “claw-free” pair of permutations – a potentially weaker assumption than the intractibility of integer factorization. The new scheme is potentially practical: signing and verifying signatures are reasonably fast, and signatures are compact.
EMC and switching loss improvement for fast switching power stages by di/dt, dv/dt optimization with 10ns variable current source gate driver

There is a growing need for motor drives with improved EMC in various automotive and industrial applications. An often referenced approach to reduce EME is to change the shape of the switching signal to reduce the EMI caused by the voltage and current transitions. This requires very precise gate control of the power MOSFET to achieve better switching behaviour and lower EME without a major increase in switching losses. In order to find an optimal trade-off, this work utilizes a monolithic current mode gate driver with a variable output current that can be changed within 10ns. With this driver, measurements with different gate current profiles were taken. The di/dt transition was confirmed to be as important as the dv/dt transition in the power MOSFET. As a result of the improved switching behavior the emissions were reduced by up to 20dB between 7MHz and 60MHz with a switching loss that is 52% lower than with a constantly low gate current.
RELATIONSHIPS BETWEEN HOTEL ROOM PRICING , OCCUPANCY , AND GUEST SATISFACTION : A LONGITUDINAL CASE OF A MIDSCALE HOTEL IN THE UNITED STATES

This case study examined the relationship among hotel room prices, occupancy percentage, and guest satisfaction using 3 years of data from 3,875 actual guest satisfaction surveys at an upper midscale hotel. The study concludes that price was a significant predictor of overall guest satisfaction and three key guest-satisfaction components: guest room cleanliness, maintenance, and attentiveness of staff (with negative, curvilinear relationships in all cases). Conversely, occupancy percentage failed to be a significant predictor of guest satisfaction.
A common reference frame for movement plans in the posterior parietal cortex

Orchestrating a movement towards a sensory target requires many computational processes, including a transformation between reference frames. This transformation is important because the reference frames in which sensory stimuli are encoded often differ from those of motor effectors. The posterior parietal cortex has an important role in these transformations. Recent work indicates that a significant proportion of parietal neurons in two cortical areas transforms the sensory signals that are used to guide movements into a common reference frame. This common reference frame is an eye-centred representation that is modulated by eye-, head-, body- or limb-position signals. A common reference frame might facilitate communication between different areas that are involved in coordinating the movements of different effectors. It might also be an efficient way to represent the locations of different sensory targets in the world.
SUPPLE: automatically generating user interfaces

In order to give people ubiquitous access to software applications, device controllers, and Internet services, it will be necessary to automatically adapt user interfaces to the computational devices at hand (eg, cell phones, PDAs, touch panels, etc.). While previous researchers have proposed solutions to this problem, each has limitations. This paper proposes a novel solution based on treating interface adaptation as an optimization problem. When asked to render an interface on a specific device, our supple system searches for the rendition that meets the device's constraints and minimizes the estimated effort for the user's expected interface actions. We make several contributions: 1) precisely defining the interface rendition problem, 2) demonstrating how user traces can be used to customize interface rendering to particular user's usage pattern, 3) presenting an efficient interface rendering algorithm, 4) performing experiments that demonstrate the utility of our approach.
A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study

The main real-coded genetic algorithm (RCGA) research effort has been spent on developing efficient crossover operators. This study presents a taxonomy for this operator that groups its instances in different categories according to the way they generate the genes of the offspring from the genes of the parents. The empirical study of representative crossovers of all the categories reveals concrete features that allow the crossover operator to have a positive influence on RCGA performance. They may be useful to design more effective crossover models.
Co-Evolution in the Successful Learning of Backgammon Strategy

Following Tesauro's work on TD-Gammon, we used a 4,000 parameter feedforward neural network to develop a competitive backgammon evaluation function. Play proceeds by a roll of the dice, application of the network to all legal moves, and selection of the position with the highest evaluation. However, no backpropagation, reinforcement or temporal difference learning methods were employed. Instead we apply simple hillclimbing in a relative fitness environment. We start with an initial champion of all zero weights and proceed simply by playing the current champion network against a slightly mutated challenger and changing weights if the challenger wins. Surprisingly, this worked rather well. We investigate how the peculiar dynamics of this domain enabled a previously discarded weak method to succeed, by preventing suboptimal equilibria in a “meta-game” of self-learning.
A Critical Survey and a Research Model

Most existing models or theories in IS focus on the cognitive and behavioral aspects of human decision-making processes and on individual reactions to using technologies in organizations and other contexts. The influence of affect or emotion is traditionally neglected. The affective aspect, however, is considered crucial, and has gained attention in psychology, marketing, organizational behavior, and other fields. Recently, affect and related concepts have attracted attention from researchers in information systems (IS) and human-computer interaction (HCI). Yet, studies of affect have been scattered and less systematic. This paper first examines the theoretical advancement of affect studies in several referencing disciplines to IS: psychology, organizational psychology, and marketing and consumer behavior. An abstract model of the individual interacting with an object (IIO) is developed to represent the important contributors to behavior intention and behavior of people interacting with objects. Then the chapter continues with a comprehensive survey of existing studies on affect in the IS discipline to demonstrate the current status of the research stream, some conceptual discrepancies and limitations, and some potential areas for future research. An ITspecific model of IIO, a model of individual interaction with IT (IIIT), is constructed as both a framework and a theoretical model to interpret and predict individual IT user behavior. This study is an attempt to highlight and systematically analyze the influence of affect in IS and therefore has great implications for both researchers and practitioners.
Visual saliency based on multiscale deep features

Visual saliency is a fundamental problem in both cognitive and computational sciences, including computer vision. In this paper, we discover that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks. For learning such saliency models, we introduce a neural network architecture, which has fully connected layers on top of CNNs responsible for feature extraction at three different scales. We then propose a refinement method to enhance the spatial coherence of our saliency results. Finally, aggregating multiple saliency maps computed for different levels of image segmentation can further boost the performance, yielding saliency maps better than those generated from a single segmentation. To promote further research and evaluation of visual saliency models, we also construct a new large database of 4447 challenging images and their pixelwise saliency annotations. Experimental results demonstrate that our proposed method is capable of achieving state-of-the-art performance on all public benchmarks, improving the F-Measure by 5.0% and 13.2% respectively on the MSRA-B dataset and our new dataset (HKU-IS), and lowering the mean absolute error by 5.7% and 35.1% respectively on these two datasets.
Toward an affect-sensitive multimodal human-computer interaction

The ability to recognize affective states of a person we are communicating with is the core of emotional intelligence. Emotional intelligenceisafacetofhumanintelligencethathasbeenarguedtobe indispensable and perhaps the most important for successful interpersonal social interaction. This paper argues that next-generation human–computer interaction (HCI) designs need to include the essence of emotional intelligence—the ability to recognize a user’s affective states—in order to become more human-like, more effective, and more efficient. Affective arousal modulates all nonverbal communicative cues (facial expressions, body movements, and vocal and physiological reactions). In a face-to-face interaction, humans detect and interpret those interactive signals of their communicator with little or no effort. Yet design and development of an automated system that accomplishes these tasks is rather difficult. This paper surveys the past work in solving these problems by a computer and provides a set of recommendations for developing the first part of an intelligent multimodal HCI—an automatic personalized analyzer of a user’s nonverbal affective feedback.
A Platform for Robotics Research Based on the Remote-Brained Robot Approach

Robots of the future will act in the real world. To realize robots with the common sense to do this will, it is generally believed, require massively parallel processing. This is a problem for those of us who want to do experiments with robots in the real world today—it is hard to build active, limber, situated robots when they have to carry along heavy brains. Our answer is “remote-brained robots.” A remotebrained robot is designed to have the brain and body separate, both conceptually and physically. It allows us to tie artificial intelligence (AI) with massive parallelism directly to the real world, enabling the verification of high-level AI techniques, which could previously only be used in simulation. Once the brain is placed remotely from the body, it encourages us to investigate many important research topics of embodied agents working in the real world. This paper describes such platform for future robotics and principal experiments done on
Automatic Ontology Matching via Upper Ontologies: A Systematic Evaluation

Ontology matching¿ is the process of finding correspondences between entities belonging to different ontologies. This paper describes a set of algorithms that exploit upper ontologies as semantic bridges in the ontology matching process and presents a systematic analysis of the relationships among features of matched ontologies (number of simple and composite concepts, stems, concepts at the top level, common English suffixes and prefixes, and ontology depth), matching algorithms, used upper ontologies, and experiment results. This analysis allowed us to state under which circumstances the exploitation of upper ontologies gives significant advantages with respect to traditional approaches that do no use them. We run experiments with SUMO-OWL (a restricted version of SUMO), OpenCyc, and DOLCE. The experiments demonstrate that when our ¿structural matching method via upper ontology¿ uses an upper ontology large enough (OpenCyc, SUMO-OWL), the recall is significantly improved while preserving the precision obtained without upper ontologies. Instead, our ¿nonstructural matching method¿ via OpenCyc and SUMO-OWL improves the precision and maintains the recall. The ¿mixed method¿ that combines the results of structural alignment without using upper ontologies and structural alignment via upper ontologies improves the recall and maintains the F-measure independently of the used upper ontology.
Secu Wear: An Open Source, Multi-component Hardware/Software Platform for Exploring Wearable Security

Wearables are the next big development in the mobile internet of things. Operating in a body area network around a smartphone user they serve a variety of commercial, medical, and personal uses. Whether used for fitness tracking, mobile health monitoring, or as remote controllers, wearable devices can include sensors that collect a variety of data and actuators that provide hap tic feedback and unique user interfaces for controlling software and hardware. Wearables are typically wireless and use Bluetooth LE (low energy) to transmit data to a waiting smartphone app. Frequently, apps forward this data onward to online web servers for tracking. Security and privacy concerns abound when wearables capture sensitive data or provide critical functionality. This paper develops a platform, called SecuWear, for conducting wearable security research, collecting data, and identifying vulnerabilities in hardware and software. SecuWear combines open source technologies to enable researchers to rapidly prototype security vulnerability test cases, evaluate them on actual hardware, and analyze the results to understand how best to mitigate problems. The paper includes two types of evaluation in the form of a comparative analysis and empirical study. The results reveal how several passive observation attacks present themselves in wearable applications and how the SecuWear platform can capture the necessary information needed to identify and combat such attacks.