nils-herrmann/clean_scirepeval huggingface dataset:
Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs

The drastic increase of Android malware has led to a strong interest in developing methods to automate the malware analysis process. Existing automated Android malware detection and classification methods fall into two general categories: 1) signature-based and 2) machine learning-based. Signature-based approaches can be easily evaded by bytecode-level transformation attacks. Prior learning-based works extract features from application syntax, rather than program semantics, and are also subject to evasion. In this paper, we propose a novel semantic-based approach that classifies Android malware via dependency graphs. To battle transformation attacks, we extract a weighted contextual API dependency graph as program semantics to construct feature sets. To fight against malware variants and zero-day malware, we introduce graph similarity metrics to uncover homogeneous application behaviors while tolerating minor implementation differences. We implement a prototype system, DroidSIFT, in 23 thousand lines of Java code. We evaluate our system using 2200 malware samples and 13500 benign samples. Experiments show that our signature detection can correctly label 93\% of malware instances; our anomaly detector is capable of detecting zero-day malware with a low false negative rate (2\%) and an acceptable false positive rate (5.15\%) for a vetting purpose.
Is Bigger Better? Comparing User-Generated Passwords on 3x3 vs. 4x4 Grid Sizes for Android's Pattern Unlock

Android's graphical authentication mechanism requires users to unlock their devices by "drawing" a pattern that connects a sequence of contact points arranged in a 3x3 grid. Prior studies demonstrated that human-generated 3x3 patterns are weak (CCS'13); large portions can be trivially guessed with sufficient training. An obvious solution would be to increase the grid size to increase the complexity of chosen patterns. In this paper we ask the question: Does increasing the grid size increase the security of human-generated patterns? We conducted two large studies to answer this question, and our analysis shows that for both 3x3 and 4x4 patterns, there is a high incidence of repeated patterns and symmetric pairs (patterns that derive from others based on a sequence of flips and rotations), and many 4x4 patterns are expanded versions of 3x3 patterns. Leveraging this information, we developed an advanced guessing algorithm and used it to quantified the strength of the patterns using the partial guessing entropy. We find that guessing the first 20% (G0.2) of patterns for both 3x3 and 4x4 can be done as efficiently as guessing a random 2-digit PIN. While guessing larger portions of 4x4 patterns (G0.5) requires 2-bits more entropy than guessing the same ratio of 3x3 patterns, it remains on the order of cracking random 3-digit PINs. Of the patterns tested, our guessing algorithm successful cracks 15% of 3x3 patterns within 20 guesses (a typical phone lockout) and 19% of 4x4 patterns within 20 guesses; however, after 50,000 guesses, we correctly guess 95.9% of 3x3 patterns but only 66.7% of 4x4 patterns. While there may be some benefit to expanding the grid size to 4x4, we argue the majority of patterns chosen by users will remain trivially guessable and insecure against broad guessing attacks.
Improving Verb Metaphor Detection by Propagating Abstractness to Words, Phrases and Individual Senses

Abstract words refer to things that can not be seen, heard, felt, smelled, or tasted as opposed to concrete words. Among other applications, the degree of abstractness has been shown to be a useful information for metaphor detection. Our contribution to this topic are as follows: i) we compare supervised techniques to learn and extend abstractness ratings for huge vocabularies ii) we learn and investigate norms for multi-word units by propagating abstractness to verb-noun pairs which lead to better metaphor detection, iii) we overcome the limitation of learning a single rating per word and show that multisense abstractness ratings are potentially useful for metaphor detection. Finally, with this paper we publish automatically created abstractness norms for 3 million English words and multi-words as wellwords refer to things that can not be seen, heard, felt, smelled, or tasted as opposed to concrete words. Among other applications, the degree of abstractness has been shown to be a useful information for metaphor detection. Our contribution to this topic are as follows: i) we compare supervised techniques to learn and extend abstractness ratings for huge vocabularies ii) we learn and investigate norms for multi-word units by propagating abstractness to verb-noun pairs which lead to better metaphor detection, iii) we overcome the limitation of learning a single rating per word and show that multisense abstractness ratings are potentially useful for metaphor detection. Finally, with this paper we publish automatically created abstractness norms for 3 million English words and multi-words as well as automatically created sense-specific abstractness ratings.
Big Data Analytics in Mobile Cellular Networks

Mobile cellular networks have become both the generators and carriers of massive data. Big data analytics can improve the performance of mobile cellular networks and maximize the revenue of operators. In this paper, we introduce a unified data model based on the random matrix theory and machine learning. Then, we present an architectural framework for applying the big data analytics in the mobile cellular networks. Moreover, we describe several illustrative examples, including big signaling data, big traffic data, big location data, big radio waveforms data, and big heterogeneous data, in mobile cellular networks. Finally, we discuss a number of open research challenges of the big data analytics in the mobile cellular networks.
ARMlock: Hardware-based Fault Isolation for ARM

Software fault isolation (SFI) is an effective mechanism to confine untrusted modules inside isolated domains to protect their host applications. Since its debut, researchers have proposed different SFI systems for many purposes such as safe execution of untrusted native browser plugins. However, most of these systems focus on the x86 architecture. Inrecent years, ARM has become the dominant architecture for mobile devices and gains in popularity in data centers.Hence there is a compellingneed for an efficient SFI system for the ARM architecture. Unfortunately, existing systems either have prohibitively high performance overhead or place various limitations on the memory layout and instructions of untrusted modules.
 In this paper, we propose ARMlock, a hardware-based fault isolation for ARM. It uniquely leverages the memory domain support in ARM processors to create multiple sandboxes. Memory accesses by the untrusted module (including read, write, and execution) are strictly confined by the hardware,and instructions running inside the sandbox execute at the same speed as those outside it. ARMlock imposes virtually no structural constraints on untrusted modules. For example, they can use self-modifying code, receive exceptions, and make system calls. Moreover, system calls can be interposed by ARMlock to enforce the policies set by the host. We have implemented a prototype of ARMlock for Linux that supports the popular ARMv6 and ARMv7 sub-architecture. Our security assessment and performance measurement show that ARMlock is practical, effective, and efficient.
Transductive Multi-label Zero-shot Learning

Zero-shot learning has received increasing interest as a means to alleviate the often prohibitive expense of annotating training data for large scale recognition problems. These methods have achieved great success via learning intermediate semantic representations in the form of attributes and more recently, semantic word vectors. However, they have thus far been constrained to the single-label case, in contrast to the growing popularity and importance of more realistic multi-label data. In this paper, for the first time, we investigate and formalise a general framework for multi-label zero-shot learning, addressing the unique challenge therein: how to exploit multi-label correlation at test time with no training data for those classes? In particular, we propose (1) a multi-output deep regression model to project an image into a semantic word space, which explicitly exploits the correlations in the intermediate semantic layer of word vectors; (2) a novel zero-shot learning algorithm for multi-label data that exploits the unique compositionality property of semantic word vector representations; and (3) a transductive learning strategy to enable the regression model learned from seen classes to generalise well to unseen classes. Our zero-shot learning experiments on a number of standard multi-label datasets demonstrate that our method outperforms a variety of baselines.
Requirements for a future EWS - Cyber Defence in the internet of the future

The emergence of new technologies and services as well as trillions of devices and petabytes of data to be processed and transferred in the Internet of the Future mean that we have to deal with new threats and vulnerabilities, in addition to handle the remaining old ones. Together with the rise of Cyber Warfare and the resulting impact on the environment means that we have to bring intelligence back to the network. Consequently, effective Cyber Defence will be more and more important. In this paper we will show that the proposed requirements for an Early Warning System are a main part of future Cyber Defence. Special attention is given on the challenges associated to the generation of early warning systems for future attacks on the Internet of the Future. The term Cyber War is used frequently but unfortunately with different intends. Therefore, we start with a definition of the term Cyber War focusing on security aspects related to the Internet of the Future, followed by an exemplification of a Cyber War, of its implications and the challenges associated to it. Then we proceed with an analysis of state of the art recent work that has been proposed on the topic. Additionally the weaknesses of these analyzed systems and approaches are presented. Finally we propose guidelines and requirements for future work which will be needed to implement a next generation early warning system for securing the Internet of the Future.
Creating adaptive affective autonomous NPCs

The paper reports work to create believable autonomous Non Player Characters in Video games in general and educational role play games in particular. It aims to increase their ability to respond appropriately to the player’s actions both cognitively and emotionally by integrating two models: the cognitive appraisal-based FAtiMA architecture, and the drives-based PSI model. We discuss the modelling of adaptive affective autonomous characters based on a biologically-inspired theory of human action regulation taking into account perception, motivation, emotions, memory, learning and planning. These agents populate an educational Role Playing Game, ORIENT (Overcoming Refugee Integration with Empathic Novel Technology) dealing with the cultural-awareness problem for children aged 13–14.
Introduction to automata theory, languages, and computation, 2nd edition

G' A. Linz, Peter. An introduction to formal languages and automata / Peter Linz'--3'd cd charrgcs ftrr the second edition wercl t)volutionary rather than rcvolrrtionary and addressed Initially, I felt that giving solutions to exercises was undesirable hecause it lirrritcd the Chapter 1 fntroduction to the Theory of Computation. Issuu solution manual to introduction to languages. Introduction theory computation 2nd edition solution manual sipser. Structural Theory of automata: solution manual of theory of computation. Kellison theory of interest pdf. Transformation, Sylvester's theorem(without proof), Solution of Second Order. Linear Differential Higher Engineering Mathematics by B.S. Grewal, 40th Edition, Khanna. Publication. 2.  Of Automata Theory, Languages and computationHopcroft. Motwani&Ulman UNIX system Utilities manual. 4.
Robust 3D Shape Correspondence in the Spectral Domain

We present an algorithm for finding a meaningful vertex-to-vertex correspondence between two 3D shapes given as triangle meshes. Our algorithm operates on embeddings of the two shapes in the spectral domain so as to normalize them with respect to uniform scaling and rigid-body transformation. Invariance to shape bending is achieved by relying on geodesic point proximities on a mesh to capture its shape. To deal with stretching, we propose to use non-rigid alignment via thin-plate splines in the spectral domain. This is combined with a refinement step based on the geodesic proximities to improve dense correspondence. We show empirically that our algorithm outperforms previous spectral methods, as well as schemes that compute correspondence in the spatial domain via non-rigid iterative closest points or the use of local shape descriptors, e.g., 3D shape context
Accuracy analysis of surface normal reconstruction in stereo vision

Estimating surface normals is an important task in computer vision, e.g. in surface reconstruction, registration and object detection. In stereo vision, the error of depth reconstruction increases quadratically with distance. This makes estimation of surface normals an especially demanding task. In this paper, we analyze how error propagates from noisy disparity data to the orientation of the estimated surface normal. Firstly, we derive a transformation for normals between disparity space and world coordinates. Afterwards, the propagation of disparity noise is analyzed by means of a Monte Carlo method. Normal reconstruction at a pixel position requires to consider a certain neighborhood of the pixel. The extent of this neighborhood affects the reconstruction error. Our method allows to determine the optimal neighborhood size required to achieve a pre specified deviation of the angular reconstruction error, defined by a confidence interval. We show that the reconstruction error only depends on the distance of the surface point to the camera, the pixel distance to the principal point in the image plane and the angle at which the viewing ray intersects the surface.
Quantitative comparisons of the state-of-the-art data center architectures

Data centers are experiencing a remarkable growth in the number of interconnected servers. Being one of the foremost data center design concerns, network infrastructure plays a pivotal role in the initial capital investment and ascertaining the performance parameters for the data center. Legacy data center network (DCN) infrastructure lacks the inherent capability to meet the data centers growth trend and aggregate bandwidth demands. Deployment of even the highest-end enterprise network equipment only delivers around 50% of the aggregate bandwidth at the edge of network. The vital challenges faced by the legacy DCN architecture trigger the need for new DCN architectures, to accommodate the growing demands of the ‘cloud computing’ paradigm. We have implemented and simulated the state of the art DCN models in this paper, namely: (a) legacy DCN architecture, (b) switch-based, and (c) hybrid models, and compared their effectiveness by monitoring the network: (a) throughput and (b) average packet delay. The presented analysis may be perceived as a background benchmarking study for the further research on the simulation and implementation of the DCN-customized topologies and customized addressing protocols in the large-scale data centers. We have performed extensive simulations under various network traffic patterns to ascertain the strengths and inadequacies of the different DCN architectures. Moreover, we provide a firm foundation for further research and enhancement in DCN architectures.
A robust feature extraction algorithm based on class-Modular Image Principal Component Analysis for face verification

Face verification systems reach good performance on ideal environmental conditions. Conversely, they are very sensitive to non-controlled environments. This work proposes the class-Modular Image Principal Component Analysis (cMIMPCA) algorithm for face verification. It extracts local and global information of the user faces aiming to reduce the effects caused by illumination, facial expression and head pose changes. Experimental results performed over three well-known face databases showed that cMIMPCA obtains promising results for the face verification task.
Processing of affective pictures modulates right-hemispheric gamma band EEG activity

The present study was designed to test differential hemispheric activation induced by emotional stimuli in the gamma band range (30-90 Hz). Subjects viewed slides with differing emotional content (from the International Affective Picture System). A significant valence by hemisphere interaction emerged in the gamma band from 30-50 Hz. Other bands, including alpha and beta, did not show such an interaction. Previous hypotheses suggested that the left hemisphere is more involved in positive affective processing as compared to the right hemisphere, while the latter dominates during negative emotions. Contrary to this expectation, the 30-50 Hz band showed relatively more power for negative valence over the left temporal region as compared to the right and a laterality shift towards the right hemisphere for positive valence. In addition, emotional processing enhanced gamma band power at right frontal electrodes regardless of the particular valence as compared to processing neutral pictures. The extended distribution of specific activity in the gamma band may be the signature of cell assemblies with members in limbic, temporal and frontal neocortical structures that differ in spatial distribution depending on the particular type of emotional processing.
A Wireless Sensor Network Solution for Precision Agriculture Based on ZigBee Technology

Wireless Sensor Networks (WSNs) have attracted much attention in recent years. The potential applications of WSNs are immense. They are used for collecting, storing and sharing sensed data. WSNs have been used for various applications including habitat monitoring, agriculture, nuclear reactor control, security and tactical surveillance. The WSN system developed in this paper is for use in precision agriculture applications, where real time data of climatologically and other environmental properties are sensed and control decisions are taken based on it to modify them. The architecture of a WSN system comprises of a set of sensor nodes and a base station that communicate with each other and gather local information to make global decisions about the physical environment. The sensor network is based on the IEEE 802.15.4 standard and two topologies for this application.
- 1-Cooperative Information Systems : A Manifesto

Information systems technology, computer-supported cooperative work practice, and organizational modeling and planning theories have evolved with only accidental contact to each other. Cooperative information systems is a relatively young research area which tries to systematically investigate the synergies between these research fields, driven by the observation that change management is the central issue facing all three areas today and that all three fields have indeed developed rather similar strategies to cope with change. In this paper, we therefore propose a framework which views cooperative information systems as composed from three interrelated facets, viz. the system facet, the group collaboration facet, and the organizational facet. We present an overview of these facets, emphasizing strategies they have developed over the past few years to accommodate change. We also discuss the propagation of change across the facets, and sketch a basic software architecture intended to support the rapid construction and evolution of cooperative information systems on top of existing organizational and technical legacy.
Semantic Role Chunking Combining Complementary Syntactic Views

This paper describes a semantic role labeling system that uses features derived from different syntactic views, and combines them within a phrase-based chunking paradigm. For an input sentence, syntactic constituent structure parses are generated by a Charniak parser and a Collins parser. Semantic role labels are assigned to the constituents of each parse using Support Vector Machine classifiers. The resulting semantic role labels are converted to an IOB representation. These IOB representations are used as additional features, along with flat syntactic chunks, by a chunking SVM classifier that produces the final SRL output. This strategy for combining features from three different syntactic views gives a significant improvement in performance over roles produced by using any one of the syntactic views individually.
Echo-state conditional variational autoencoder for anomaly detection

Anomaly detection involves identifying the events which do not conform to an expected pattern in data. A common approach to anomaly detection is to identify outliers in a latent space learned from data. For instance, PCA has been successfully used for anomaly detection. Variational autoencoder (VAE) is a recently-developed deep generative model which has established itself as a powerful method for learning representation from data in a nonlinear way. However, the VAE does not take the temporal dependence in data into account, so it limits its applicability to time series. In this paper we combine the echo-state network, which is a simple training method for recurrent networks, with the VAE, in order to learn representation from multivariate time series data. We present an echo-state conditional variational autoencoder (ES-CVAE) and demonstrate its useful behavior in the task of anomaly detection in multivariate time series data.
CleanOS: Limiting Mobile Data Exposure with Idle Eviction

Mobile-device theft and loss have reached gigantic proportions. Despite these threats, today’s mobile devices are saturated with sensitive information due to operating systems that never securely erase data and applications that hoard it on the vulnerable device for performance or convenience. This paper presents CleanOS, a new Android-based operating system that manages sensitive data rigorously and maintains a clean environment at all times. To do so, CleanOS leverages a key property of today’s mobile applications – the use of trusted, cloudbased services. Specifically, CleanOS identifies and tracks sensitive data in RAM and on stable storage, encrypts it with a key, and evicts that key to the cloud when the data is not in active use on the device. We call this process idle eviction of sensitive data. To implement CleanOS, we used the TaintDroid mobile taint-tracking system to identify sensitive data locations and instrumented Android’s Dalvik interpreter to securely evict that data after a specified period of non-use. Our experimental results show that CleanOS limits sensitive-data exposure drastically while incurring acceptable overheads on mobile networks.
Spell: Streaming Parsing of System Event Logs

System event logs have been frequently used as a valuable resource in data-driven approaches to enhance system health and stability. A typical procedure in system log analytics is to first parse unstructured logs, and then apply data analysis on the resulting structured data. Previous work on parsing system event logs focused on offline, batch processing of raw log files. But increasingly, applications demand online monitoring and processing. We propose an online streaming method Spell, which utilizes a longest common subsequence based approach, to parse system event logs. We show how to dynamically extract log patterns from incoming logs and how to maintain a set of discovered message types in streaming fashion. Evaluation results on large real system logs demonstrate that even compared with the offline alternatives, Spell shows its superiority in terms of both efficiency and effectiveness.