nils-herrmann/clean_scirepeval huggingface dataset:
Dissecting Android Malware: Characterization and Evolution

The popularity and adoption of smart phones has greatly stimulated the spread of mobile malware, especially on the popular platforms such as Android. In light of their rapid growth, there is a pressing need to develop effective solutions. However, our defense capability is largely constrained by the limited understanding of these emerging mobile malware and the lack of timely access to related samples. In this paper, we focus on the Android platform and aim to systematize or characterize existing Android malware. Particularly, with more than one year effort, we have managed to collect more than 1,200 malware samples that cover the majority of existing Android malware families, ranging from their debut in August 2010 to recent ones in October 2011. In addition, we systematically characterize them from various aspects, including their installation methods, activation mechanisms as well as the nature of carried malicious payloads. The characterization and a subsequent evolution-based study of representative families reveal that they are evolving rapidly to circumvent the detection from existing mobile anti-virus software. Based on the evaluation with four representative mobile security software, our experiments show that the best case detects 79.6% of them while the worst case detects only 20.2% in our dataset. These results clearly call for the need to better develop next-generation anti-mobile-malware solutions.
Causality reasoning about network events for detecting stealthy malware activities

Malicious software activities have become more and more clandestine, making them challenging to detect. Existing security solutions rely heavily on the recognition of known code or behavior signatures, which are incapable of detecting new malware patterns. We propose to discover the triggering relations on network requests and leverage the structural information to identify stealthy malware activities that cannot be attributed to a legitimate cause. The triggering relation is defined as the temporal and causal relationship between two events.We design and compare ruleand learning-based methods to infer the triggering relations on network data.We further introduce a user-intention based security policy for pinpointing stealthy malware activities based on a triggering relation graph. We extensively evaluate our solution on a DARPA dataset and 7 GB real-world network traffic. Results indicate that our dependence analysis successfully detects various malware activities including spyware,data exfiltrating malware, and DNS bots on hosts. With good scalability for large datasets, the learning-based method achieves better classification accuracy than the rule-based one. The significance of our traffic reasoning approach is its ability to detect new and stealthy malware activities.
Comparing Images Using Color Coherence Vectors

Color histograms are used to compare images in many applications. Their advantages are e ciency, and insensitivity to small changes in camera viewpoint. However, color histograms lack spatial information, and this can cause images with very different appearances to have similar histograms. For example, a picture of fall foliage might contain a large number of scattered red pixels; this could have a similar color histogram to a picture of a single large red object. We describe a histogram-based method for comparing images that incorporates spatial information. While a color histogram counts the number of pixels with a given color, a color coherence vector (CCV) measures the spatial coherence of the pixels with a given color. If the red pixels in an image are members of large red regions, this color will have high coherence, while if the red pixels are widely scattered it will have low coherence. CCV's can be computed at over 5 images per second on a standard workstation. A database with 15,000 images can be queried for the images with the most similar CCV's in under 2 seconds. We demonstrate that CCV's can be used to distinguish objects whose color histograms are indistinguishable. We show that CCV's can give superior results to color histograms for image retrieval.
Gated-Attention Readers for Text Comprehension

In this paper we study the problem of answering cloze-style questions over documents. Our model, the Gated-Attention (GA) Reader1, integrates a multi-hop architecture with a novel attention mechanism, which is based on multiplicative interactions between the query embedding and the intermediate states of a recurrent neural network document reader. This enables the reader to build query-specific representations of tokens in the document for accurate answer selection. The GA Reader obtains state-of-the-art results on three benchmarks for this task–the CNN & Daily Mail news stories and the Who Did What dataset. The effectiveness of multiplicative interaction is demonstrated by an ablation study, and by comparing to alternative compositional operators for implementing the gated-attention.
Transcend: Detecting Concept Drift in Malware Classification Models

Building machine learning models of malware behavior is widely accepted as a panacea towards effective malware classification. A crucial requirement for building sustainable learning models, though, is to train on a wide variety of malware samples. Unfortunately, malware evolves rapidly and it thus becomes hard—if not impossible—to generalize learning models to reflect future, previously-unseen behaviors. Consequently, most malware classifiers become unsustainable in the long run, becoming rapidly antiquated as malware continues to evolve. In this work, we propose Transcend, a framework to identify aging classification models in vivo during deployment, much before the machine learning model’s performance starts to degrade. This is a significant departure from conventional approaches that retrain aging models retrospectively when poor performance is observed. Our approach uses a statistical comparison of samples seen during deployment with those used to train the model, thereby building metrics for prediction quality. We show how Transcend can be used to identify concept drift based on two separate case studies on Android and Windows malware, raising a red flag before the model starts making consistently poor decisions due to out-of-date training.
Scheduling for Reduced CPU Energy

The energy usage of computer systems is becoming more important, especially for battery operated systems. Displays, disks, and cpus, in that order, use the most energy. Reducing the energy used by displays and disks has been studied elsewhere; this paper considers a new method for reducing the energy used by the cpu. We introduce a new metric for cpu energy performance, millions-of-instructions-per-joule (MIPJ). We examine a class of methods to reduce MIPJ that are characterized by dynamic control of system clock speed by the operating system scheduler. Reducing clock speed alone does not reduce MIPJ, since to do the same work the system must run longer. However, a number of methods are available for reducing energy with reduced clock-speed, such as reducing the voltage [Chandrakasan et al 1992][Horowitz 1993] or using reversible [Younis and Knight 1993] or adiabatic logic [Athas et al 1994].
 What are the right scheduling algorithms for taking advantage of reduced clock-speed, especially in the presence of applications demanding ever more instructions-per-second? We consider several methods for varying the clock speed dynamically under control of the operating system, and examine the performance of these methods against workstation traces. The primary result is that by adjusting the clock speed at a fine grain, substantial CPU energy can be saved with a limited impact on performance.
Essential communication practices for Extreme Programming in a global software development team

We conducted an industrial case study of a distributed team in the USA and the Czech Republic that used Extreme Programming. Our goal was to understand how this globally-distributed team created a successful project in a new problem domain using a methodology that is dependent on informal, face-to-face communication. We collected quantitative and qualitative data and used grounded theory to identify four key factors for communication in globally-distributed XP teams working within a new problem domain. Our study suggests that, if these critical enabling factors are addressed, methodologies dependent on informal communication can be used on global software development projects.
A novel clustering-based image segmentation via density peaks algorithm with mid-level feature

Image segmentation is an important and fundamental task in computer vision. Its performance is mainly influenced by feature representations and segmentation algorithms. In this paper, we propose a novel clustering-based image segmentation approach which can be called ICDP algorithm. It is able to capture the inherent structure of image and detect the nonspherical clusters. Compared to the other segmentation methods based on clustering, there are several advantages as follows: (1) Integral channel features are used to clearly and comprehensively represent the input image by naturally integrating heterogeneous sources of information; (2) cluster number can be determined directly and cluster centers are able to be identified automatically; (3) hierarchical segmentation is easy to be achieved via ICDP algorithm. The PSNR and MSE are applied to quantitatively evaluate the segmentation performance. Experimental results clearly demonstrate the effectiveness of our novel image segmentation algorithm.
Design techniques for high-speed, high-resolution comparators

This paper describes precision techniques for the design of comparators used in high-performance analog-to-digital converters employing parallel conversion stages. Following a review of conventional offset cancellation techniques, circuit designs achieving 12-b resolution in both BiCMOS and CMOS 5-V technologies are presented. The BiCMOS comparator consists of a preamplifier followed by two regenerative stages and achieves an offset of 200 pV at a 1O-MHZ clock rate while dissipating 1.7 mW. In the CMOS comparator offset cancellation is used in both a single-stage preamplifier and a subsequent latch to achieve an offset of less than 300 pV at comparison rates as high as 10 MHz, with a power dissipation of 1.8 m W.
Impairment of social and moral behavior related to early damage in human prefrontal cortex

The long-term consequences of early prefrontal cortex lesions occurring before 16 months were investigated in two adults. As is the case when such damage occurs in adulthood, the two early-onset patients had severely impaired social behavior despite normal basic cognitive abilities, and showed insensitivity to future consequences of decisions, defective autonomic responses to punishment contingencies and failure to respond to behavioral interventions. Unlike adult-onset patients, however, the two patients had defective social and moral reasoning, suggesting that the acquisition of complex social conventions and moral rules had been impaired. Thus early-onset prefrontal damage resulted in a syndrome resembling psychopathy.
Literary reading and eating disorders: survey evidence of therapeutic help and harm

There is growing evidence for the efficacy of self-help bibliotherapy as a treatment for eating disorders, although little understanding of how specific linguistic characteristics may enhance or constrain its effects. Meanwhile, 'creative bibliotherapy' (the therapeutic use of fiction, poetry, or sometimes film, rather than self-help books) is widely practised, but even more poorly understood than the self-help variety: although a range of theoretical models exist, claims of the healing power of literature are far more commonly made than tested.



An online survey including quantitative (forced-choice) and qualitative (free-response) items was designed and administered in collaboration with the charity Beat to investigate the connections between respondents' reading habits and their mental health, with a focus on eating disorders, and attracted 885 respondents. Responses to two sequences of questions, exploring the differential effects of fiction about eating disorders versus respondents' preferred genre of other fiction on the dimensions of mood, self-esteem, feelings about one's body, and diet and exercise habits, were analysed using a 2 × 2 repeat measures factorial ANOVA design for each of the four dependent variables.



Surprisingly, fiction about eating disorders was perceived by respondents as broadly detrimental to mood, self-esteem, feelings about their bodies, and diet and exercise habits, while respondents' preferred genre of other fiction was experienced as beneficial to mood and broadly neutral on the other three dimensions. The free-response data added detail to these core findings, as well as suggesting numerous other possible effects and mechanisms, drawing attention to the roles of positive and negative feedback structures and of highly selective interpretive filtering, and highlighting the dangers of 'self-triggering': using books to deliberately exacerbate an eating disorder.



The findings directly challenge existing theoretical models of creative-bibliotherapeutic mechanisms, which tend to insist on the importance of a close match between the reader's and the protagonist's situations. They point the way forward for a new programme of clinical research and practice by suggesting other ways to conceive of how embodied cognitive acts of textually cued interpretation may intervene in the psychopathology of an eating disorder - for good and for ill.
A Digitalized Smart Mobile Home Automation and Security System via Bluetooth/Wi-Fi Using Android Platform

Home automation involves introducing a degree of automatic control to certain electrical and electronic systems in a building such as lighting, temperature control etc. This work presents the design, specification, and prototype implementation of a composite home automation system using Bluetooth/Wi-Fi over android platform. The research work provides multiple yet simple design approaches for developing flexible and robust home automation system to cater for the deficiency in overall control of user appliances. It also tackles the problems with complex, multiple, incompatible standards and the resulting expenses in the existing systems. The proposed system features an Arduino Mega board with core AVR microcontroller (ATmega2560) interfaced to the ubiquitous ESP8266 Wi-Fi chip, Infrared transmitter and receiver mechanism, power relay interface as well as user contact LCD and Keypad. With the HTML5 based intuitive mobile and web applications, unlimited flexibility is induced in the system operations and management. Overall, the system extends the capabilities of home automation beyond the basic appliances switching and monitoring by giving broad control over the appliances’ functionalities in addition to switching their power.
The Critical Success Factors for Effective ICT Governance in Malaysian Public Sector : A Delphi Study

The fundamental issues in ICT Governance (ICTG) implementation for Malaysian Public Sector (MPS) is how ICT be applied to support improvements in productivity, management effectiveness and the quality of services offered to its citizens. Our main concern is to develop and adopt a common definition and framework to illustrate how ICTG can be used to better align ICT with government’s operations and strategic focus. In particular, we want to identify and categorize factors that drive a successful ICTG process. This paper presents the results of an exploratory study to identify, validate and refine such Critical Success Factors (CSFs) and confirmed seven CSFs and nineteen sub-factors as influential factors that fit MPS after further validated and refined. The Delphi method applied in validation and refining process before being endorsed as appropriate for MPS. The identified CSFs reflect the focus areas that need to be considered strategically to strengthen ICT Governance implementation and ensure business success. IT Governance, Critical Success Factors.
MULTIMODAL FEEDBACK FROM ROBOTS AND AGENTS IN A STORYTELLING EXPERIMENT

In this project, which lies at the intersection between Human-Robot Interaction (HRI) and Human-Computer Interaction (HCI), we have examined the design of an open-source, real-time software platform for controlling the feedback provided by an AIBO robot and/or by the GRETA Embodied Conversational Agent, when listening to a story told by a human narrator. Based on ground truth data obtained from the recording and annotation of an audio-visual storytelling database, and containing various examples of humanhuman storytelling, we have implemented a proof-of-concept ECA/Robot listening system. As a narrator input, our system uses face and head movement analysis, as well as speech analysis and speech recognition; it then triggers listening behaviors from the listener, using probabilistic rules based on the co-occurrence of the same input and output behaviors in the database. We have finally assessed our system in terms of the homogeneity of the database annotation, as well as regarding the perceived quality of the feedback provided by the ECA/robot.
Collective annotation of Wikipedia entities in web text

To take the first step beyond keyword-based search toward entity-based search, suitable token spans ("spots") on documents must be identified as references to real-world entities from an entity catalog. Several systems have been proposed to link spots on Web pages to entities in Wikipedia. They are largely based on local compatibility between the text around the spot and textual metadata associated with the entity. Two recent systems exploit inter-label dependencies, but in limited ways. We propose a general collective disambiguation approach. Our premise is that coherent documents refer to entities from one or a few related topics or domains. We give formulations for the trade-off between local spot-to-entity compatibility and measures of global coherence between entities. Optimizing the overall entity assignment is NP-hard. We investigate practical solutions based on local hill-climbing, rounding integer linear programs, and pre-clustering entities followed by local optimization within clusters. In experiments involving over a hundred manually-annotated Web pages and tens of thousands of spots, our approaches significantly outperform recently-proposed algorithms.
Music emotion classification: a fuzzy approach

Due to the subjective nature of human perception, classification of the emotion of music is a challenging problem. Simply assigning an emotion class to a song segment in a deterministic way does not work well because not all people share the same feeling for a song. In this paper, we consider a different approach to music emotion classification. For each music segment, the approach determines how likely the song segment belongs to an emotion class. Two fuzzy classifiers are adopted to provide the measurement of the emotion strength. The measurement is also found useful for tracking the variation of music emotions in a song. Results are shown to illustrate the effectiveness of the approach.
The architecture of the Festival speech synthesis system

We describe a new formalism for storing linguistic data in a text to speech system. Linguistic entities such as words and phones are stored as feature structures in a general object called an linguistic item. Items are configurable at run time and via the feature structure can contain arbitrary information. Linguistic relations are used to store the relationship between items of the same linguistic type. Relations can take any graph structure but are commonly trees or lists. Utterance structures contain all the items and relations contained in a single utterance. We first describe the design goals when building a synthesis architecture, and then describe some problems with previous architectures. We then discuss our new formalism in general along with the implementation details and consequences of our approach.
PV panel model based on datasheet values

This work presents the construction of a model for a PV panel using the single-diode five-parameters model, based exclusively on data-sheet parameters. The model takes into account the series and parallel (shunt) resistance of the panel. The equivalent circuit and the basic equations of the PV cell/panel in Standard Test Conditions (STC)1 are shown, as well as the parameters extraction from the data-sheet values. The temperature dependence of the cell dark saturation current is expressed with an alternative formula, which gives better correlation with the datasheet values of the power temperature dependence. Based on these equations, a PV panel model, which is able to predict the panel behavior in different temperature and irradiance conditions, is built and tested.
Control of systems integrating logic, dynamics, and constraints

This paper proposes a framework for modeling and controlling systems described by interdependent physical laws, logic rules, and operating constraints, denoted as Mixed Logical Dynamical (MLD) systems. These are described by linear dynamic equations subject to linear inequalities involving real and integer variables. MLD systems include constrained linear systems, finite state machines, some classes of discrete event systems, and nonlinear systems which can be approximated by piecewise linear functions. A predictive control scheme is proposed which is able to stabilize MLD systems on desired reference trajectories while fulfilling operating constraints, and possibly take into account previous qualitative knowledge in the form of heuristic rules. Due to the presence of integer variables, the resulting on-line optimization procedures are solved through Mixed Integer Quadratic Programming (MIQP), for which efficient solvers have been recently developed. Some examples and a simulation case study on a complex gas supply system are reported.
An effective hybrid transactional memory system with strong isolation guarantees

We propose signature-accelerated transactional memory (SigTM), ahybrid TM system that reduces the overhead of software transactions. SigTM uses hardware signatures to track the read-set and write-set forpending transactions and perform conflict detection between concurrent threads. All other transactional functionality, including dataversioning, is implemented in software. Unlike previously proposed hybrid TM systems, SigTM requires no modifications to the hardware caches, which reduces hardware cost and simplifies support for nested transactions and multithreaded processor cores. SigTM is also the first hybrid TM system to provide strong isolation guarantees between transactional blocks and non-transactional accesses without additional read and write barriers in non-transactional code.
 Using a set of parallel programs that make frequent use of coarse-grain transactions, we show that SigTM accelerates software transactions by 30% to 280%. For certain workloads, SigTM can match the performance of a full-featured hardware TM system, while for workloads with large read-sets it can be up to two times slower. Overall, we show that SigTM combines the performance characteristics and strong isolation guarantees of hardware TM implementations with the low cost and flexibility of software TM systems.