nils-herrmann/clean_scirepeval huggingface dataset:
A Survey on Smartphones Security: Software Vulnerabilities, Malware, and Attacks

Nowadays, the usage of smartphones and their applications have become rapidly popular in people’s daily life. Over the last decade, availability of mobile money services such as mobile-payment systems and app markets have significantly increased due to the different forms of apps and connectivity provided by mobile devices, such as 3G, 4G, GPRS, and Wi-Fi, etc. In the same trend, the number of vulnerabilities targeting these services and communication networks has raised as well. Therefore, smartphones have become ideal target devices for malicious programmers. With increasing the number of vulnerabilities and attacks, there has been a corresponding ascent of the security countermeasures presented by the researchers. Due to these reasons, security of the payment systems is one of the most important issues in mobile payment systems. In this survey, we aim to provide a comprehensive and structured overview of the research on security solutions for smartphone devices. This survey reviews the state of the art on security solutions, threats, and vulnerabilities during the period of 2011-2017, by focusing on software attacks, such those to smartphone applications. We outline some countermeasures aimed at protecting smartphones against these groups of attacks, based on the detection rules, data collections and operating systems, especially focusing on open source applications. With this categorization, we want to provide an easy understanding for users and researchers to improve their knowledge about the security and privacy of smartphones. Mobile security; malware; adware; malicious attacks
Origins of narcissism in children.

Narcissism levels have been increasing among Western youth, and contribute to societal problems such as aggression and violence. The origins of narcissism, however, are not well understood. Here, we report, to our knowledge, the first prospective longitudinal evidence on the origins of narcissism in children. We compared two perspectives: social learning theory (positing that narcissism is cultivated by parental overvaluation) and psychoanalytic theory (positing that narcissism is cultivated by lack of parental warmth). We timed the study in late childhood (ages 7-12), when individual differences in narcissism first emerge. In four 6-mo waves, 565 children and their parents reported child narcissism, child self-esteem, parental overvaluation, and parental warmth. Four-wave cross-lagged panel models were conducted. Results support social learning theory and contradict psychoanalytic theory: Narcissism was predicted by parental overvaluation, not by lack of parental warmth. Thus, children seem to acquire narcissism, in part, by internalizing parents' inflated views of them (e.g., "I am superior to others" and "I am entitled to privileges"). Attesting to the specificity of this finding, self-esteem was predicted by parental warmth, not by parental overvaluation. These findings uncover early socialization experiences that cultivate narcissism, and may inform interventions to curtail narcissistic development at an early age.
TrendFashion - A Framework for the Identification of Fashion Trends

The fashion industry faces different challenges regarding accurate forecasts for future fashion products. The consumer demand is volatile and sales periods of fashion products are short due to production plants in Asia and target markets in Europe. Besides standard statistical approaches based on historical data and advanced methods such as the application of artificial neural networks or fuzzy logic, there are fashion experts, who use different information sources, e.g. fairs, social media, fashion websites, to predict design-trends as well as sales volumes. In this paper we follow this expert-driven approach by collecting data from fashion weblogs, news sites and fashion magazines, in order to identify actual and future designtrends. For this aim, we develop the TrendFashion Tool which collects data from these fashion sources and analyse them. On a higher level, this tool successfully separates fashion related posts from non-fashion related posts. And on a lower level, it identifies fashion related words and weights them according to an index.
Machine learning for attack vector identification in malicious source code

As computers and information technologies become ubiquitous throughout society, the security of our networks and information technologies is a growing concern. As a result, many researchers have become interested in the security domain. Among them, there is growing interest in observing hacker communities for early detection of developing security threats and trends. Research in this area has often reported hackers openly sharing cybercriminal assets and knowledge with one another. In particular, the sharing of raw malware source code files has been documented in past work. Unfortunately, malware code documentation appears often times to be missing, incomplete, or written in a language foreign to researchers. Thus, analysis of such source files embedded within hacker communities has been limited. Here we utilize a subset of popular machine learning methodologies for the automated analysis of malware source code files. Specifically, we explore genetic algorithms to resolve questions related to feature selection within the context of malware analysis. Next, we utilize two common classification algorithms to test selected features for identification of malware attack vectors. Results suggest promising direction in utilizing such techniques to help with the automated analysis of malware source code.
Web graph similarity for anomaly detection

Web graphs are approximate snapshots of the web, created by search engines. They are essential to monitor the evolution of the web and to compute global properties like PageRank values of web pages. Their continuous monitoring requires a notion of graph similarity to help measure the amount and significance of changes in the evolving web. As a result, these measurements provide means to validate how well search engines acquire content from the web. In this paper, we propose five similarity schemes: three of them we adapted from existing graph similarity measures, and two we adapted from well-known document and vector similarity methods (namely, the shingling method and random projection based method). We empirically evaluate and compare all five schemes using a sequence of web graphs from Yahoo!, and study if the schemes can identify anomalies that may occur due to hardware or other problems.
Parsing to 1-Endpoint-Crossing, Pagenumber-2 Graphs

We study the Maximum Subgraph problem in deep dependency parsing. We consider two restrictions to deep dependency graphs: (a) 1-endpoint-crossing and (b) pagenumber-2. Our main contribution is an exact algorithm that obtains maximum subgraphs satisfying both restrictions simultaneously in time O(n5). Moreover, ignoring one linguistically-rare structure descreases the complexity to O(n4). We also extend our quartic-time algorithm into a practical parser with a discriminative disambiguation model and evaluate its performance on four linguistic data sets used in semantic dependency parsing.
Robot Assistants at Manual Workplaces : Effective Co-operation and Safety Aspects

A safe and flexible co-operation between robot and operator may be a promising way to achieve better productivity at the manual workplace. Thus, robot assistants can be thought to be clever helpers in manufacturing environments for fetch and carry jobs, assembly, handling, machining, measuring etc. Key components and methods supporting these next generation robot systems are currently under intense investigation and it is expected that first scenarios of workers co-operating with manufacturing assistants for selected tasks are within three to five year’s reach from now. In this paper, key issues in the evolution from robots to robot assistants will be addressed and typical scenarios for mid-term realizations will be given. Fundamental requirements regarding intuitive programming lead to advanced control architectures which incorporate multi-modal man-machineinterfaces and automatic task planning.
Simplification of Example Sentences for Learners of Japanese Functional Expressions

Learning functional expressions is one of the difficulties for language learners, since functional expressions tend to have multiple meanings and complicated usages in various situations. In this paper, we report an experiment of simplifying example sentences of Japanese functional expressions especially for Chinese-speaking learners. For this purpose, we developed “Japanese Functional Expressions List” and “Simple Japanese Replacement List”. To evaluate the method, we conduct a small-scale experiment with Chinese-speaking learners on the effectiveness of the simplified example sentences. The experimental results indicate that the simplified sentences are helpful in learning Japanese functional expressions.
FAST CALCULATION OF HARALICK TEXTURE FEATURES

It is our aim in this research to optimize the numerical computation of the Haralick texture features  that consists of two steps. Haralick texture features are used as a primary component to discern between different protein structures in microscopic bio-images. The first of these two parts is the construction of the co-occurrence matrix. Upon completion of this implementation, we will attempt to optimize the code using a novel recursive blocking algorithm. We will also use standard practices of optimizing code such as scalar replacement and unrolling. The second part, feature calculation, will be optimized by putting to use information about the data that we will be working with and eliminating any redundancies in the code. We will also apply the practices of unrolling and scalar replacement. With these optimizations in place, we will show that we reduce the runtime by a20% in the co-occurrence construction phase and by a factor of20 in the feature calculation phase of computation.
A blockchain-based smart grid: towards sustainable local energy markets

The increasing amount of renewable energy sources in the energy system calls for new market approaches to price and distribute the volatile and decentralized generation. Local energy markets, on which consumers and prosumers can trade locally produced renewable generation directly within their community, balance generation and consumption locally in a decentralized approach. We present a comprehensive concept, market design and simulation of a local energy market between 100 residential households. Our approach is based on a distributed information and communication technology, i.e. a private blockchain, which underlines the decentralized nature of local energy markets. Thus, we provide energy prosumers and consumers with a decentralized market platform for trading local energy generation without the need of a central intermediary. Furthermore, we present a preliminary economic evaluation of the market mechanism and a research agenda for the technological evaluation of blockchain technology as the local energy market’s main information and communication technology.
Low-Cost Gas Sensors Produced by the Graphite Line-Patterning Technique Applied to Monitoring Banana Ripeness

A low-cost sensor array system for banana ripeness monitoring is presented. The sensors are constructed by employing a graphite line-patterning technique (LPT) to print interdigitated graphite electrodes on tracing paper and then coating the printed area with a thin film of polyaniline (PANI) by in-situ polymerization as the gas-sensitive layer. The PANI layers were used for the detection of volatile organic compounds (VOCs), including ethylene, emitted during ripening. The influence of the various acid dopants, hydrochloric acid (HCl), methanesulfonic acid (MSA), p-toluenesulfonic acid (TSA) and camphorsulfonic acid (CSA), on the electrical properties of the thin film of PANI adsorbed on the electrodes was also studied. The extent of doping of the films was investigated by UV-Vis absorption spectroscopy and tests showed that the type of dopant plays an important role in the performance of these low-cost sensors. The array of three sensors, without the PANI-HCl sensor, was able to produce a distinct pattern of signals, taken as a signature (fingerprint) that can be used to characterize bananas ripeness.
Autoencoders with Variable Sized Latent Vector for Image Compression

Learning to compress images is an interesting and challenging task. Autoencoders have long been used to compress images into a code of small but fixed size. As different images need different sized code based on their complexity, we propose an autoencoder architecture with a variable sized latent vector. We propose an attention based model which attends over the image and summarizes it into a small code. This summarization is repeated many times depending on the complexity of the image, producing a new code each time to encode new information so as to get a better reconstruction. These small codes then form sub-units of the final code. Our approach is quality progressive and has flexible quality setting which are desirable properties in compression. We show that the proposed model shows better performance compared to JPEG.
Video Frame Interpolation via Adaptive Convolution

Video frame interpolation typically involves two steps: motion estimation and pixel synthesis. Such a two-step approach heavily depends on the quality of motion estimation. This paper presents a robust video frame interpolation method that combines these two steps into a single process. Specifically, our method considers pixel synthesis for the interpolated frame as local convolution over two input frames. The convolution kernel captures both the local motion between the input frames and the coefficients for pixel synthesis. Our method employs a deep fully convolutional neural network to estimate a spatially-adaptive convolution kernel for each pixel. This deep neural network can be directly trained end to end using widely available video data without any difficult-to-obtain ground-truth data like optical flow. Our experiments show that the formulation of video interpolation as a single convolution process allows our method to gracefully handle challenges like occlusion, blur, and abrupt brightness change and enables high-quality video frame interpolation.
“Radio Inside My Head”: A Curious Case of Early Onset “Stuck Song Syndrome” or Obsessive-Compulsive Disorder with Predominant Musical Obsession

Musical obsessions also called "stuck song syndrome" is a topic of limited research. It is among the most poorly understood and rare phenomenology. There are only a few cases reported across the world which describes it. Subjects usually present with complaints of continuous play of a musical tune in the head which the subject is unable to remove. Because of its similarity in presentation with auditory musical hallucination, it presents a diagnostic dilemma to the clinician. Here, we present the case of a 25-year-old man who presented with this particular phenomenon, and the approach we took to manage it.
Proposed embedded security framework for Internet of Things (IoT)

IoT is going to be an established part of life by extending the communication and networking anytime, anywhere. Security requirements for IoT will certainly underline the importance of properly formulated, implemented, and enforced security policies throughout their life-cycle. This paper gives a detailed survey and analysis of embedded security, especially in the area of IoT. Together with the conventional security solutions, the paper highlights the need to provide in-built security in the device itself to provide a flexible infrastructure for dynamic prevention, detection, diagnosis, isolation, and countermeasures against successful breaches. Based on this survey and analysis, the paper defines the security needs taking into account computational time, energy consumption and memory requirements of the devices. Finally, this paper proposes an embedded security framework as a feature of software/hardware co-design methodology.
BeeParking: an ambient display to induce cooperative parking behavior

Interactive ambient systems offer a great potential for attracting user attention, raising awareness and supporting the acquisition of more desirable behaviors in the shared use of limited resources, like physical or digital spaces, energy, water and so on. In this paper we describe the iterative design of BeeParking, an ambient display and automatic notification system aimed to induce more cooperative use of a parking facility within a work environment. We also report main findings from a longitudinal in-situ evaluation showing how the system was adopted and how it affected users' parking behavior over time.
Kijken naar porno: subcorticale hersenactiviteit weerspiegelt negatieve maar niet positieve seksuele associaties

Aan de ene kant kunnen mensen visuele seksuele stimulatie (VSS) ervaren als plezierig, maar aan de andere kant kunnen mensen VSS ook ervaren als iets “viezigs” en negatief. Dit onderzoek richt zich op de vraag hoe het brein bij vrouwen reageert op VSS en hoe de hersenactiviteit samenhangt met hun impliciete en expliciete attitudes ten aanzien van VSS. Vrouwen zonder seksuele klachten (N = 20) bekeken een reeks afbeeldingen uit diverse categorieën (seksuele penetratie, walging, neutraal) in een fMRI-scanner. Met behulp van een reactietijd-taak (Implicit Association Test) werd vervolgens de sterkte van hun impliciete seks-vies versus seks-lekker associaties gemeten. Daarnaast beoordeelden ze alle afbeeldingen op een visuele analoge schaal als meting van hun expliciete attitude. De hersenactiviteit in reactie op afbeeldingen van seksuele penetratie vertoonde grote overeenkomst met die op afbeeldingen van walgelijke plaatjes. Specifiek de hersenactiviteit in reactie op de seksuele penetratieafbeeldingen bleek te variëren als functie van de impliciete attitude ten aanzien van de seksuele penetratie-plaatjes. Hoe sterker de impliciete associatie van seks met walging des te sterker de activiteit in de basale voorhersenen (inclusief de nucleus accumbens en de bed nucleus van de stria terminalis), de middenhersenen, en amygdala in reactie op het zien van de seksuele penetratie plaatjes. De betrokken hersengebieden zijn veelal geassocieerd met visuele seksuele verwerkingsprocessen. De huidige resultaten laten zien dat de activatie van deze gebieden niet zondermeer een positieve appreciatie van seksuele stimuli impliceert, maar ook kan duiden op een negatieve of ambivalente houding ten aanzien van seksuele stimuli.
SOCIAL SPACE AND SYMBOLIC POWER *

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OpenHSM: An Open Key Life Cycle Protocol for Public Key Infrastructure's Hardware Security Modules

The private keys used in a PKI are its most important asset. Protect these keys from unauthorised use or disclosure is essential to secure a PKI. Relying parties need assurances that the private key used to sign their certificates is controlled and managed following pre-defined statement policy. Hardware Security Modules (HSM) offer physical and logical protection and should be considered for any PKI deployment. The software that manages keys inside an HSM should control all life cycle of a private key. Normally this kind of equipment implements a embedded key management protocol and this protocols are not available to public scrutiny due to industrial interests. Other important issue is that HSMs are targeted in their development to the Bank industry and not to PKI, making some important PKI issues, like, strict key usage control and a secure auditing trail, play a secondary role. This paper presents an open protocol to securely manage private keys inside HSMs. The protocol is described, analysed and discussed.
PERMIT: Network Slicing for Personalized 5G Mobile Telecommunications

5G mobile systems are expected to meet different strict requirements beyond the traditional operator use cases. Effectively, to accommodate needs of new industry segments such as healthcare and manufacturing, 5G systems need to accommodate elasticity, flexibility, dynamicity, scalability, manageability, agility, and customization along with different levels of service delivery parameters according to the service requirements. This is currently possible only by running the networks on top of the same infrastructure, the technology called network function virtualization, through this sharing of the development and infrastructure costs between the different networks. In this article, we discuss the need for the deep customization of mobile networks at different granularity levels: per network, per application, per group of users, per individual users, and even per data of users. The article also assesses the potential of network slicing to provide the appropriate customization and highlights the technology challenges. Finally, a high-level architectural solution is proposed, addressing a massive multi-slice environment.
Application of Machine Learning To Epileptic Seizure Detection

We present and evaluate a machine learning approach to constructing patient-specific classifiers that detect the onset of an epileptic seizure through analysis of the scalp EEG, a non-invasive measure of the brain’s electrical activity. This problem is challenging because the brain’s electrical activity is composed of numerous classes with overlapping characteristics. The key steps involved in realizing a high performance algorithm included shaping the problem into an appropriate machine learning framework, and identifying the features critical to separating seizure from other types of brain activity. When trained on 2 or more seizures per patient and tested on 916 hours of continuous EEG from 24 patients, our algorithm detected 96% of 173 test seizures with a median detection delay of 3 seconds and a median false detection rate of 2 false detections per 24 hour period. We also provide information about how to download the CHB-MIT database, which contains the data used in this study.
English Filler Gap Constructions

This paper delineates and analyzes the syntactic and semant ic p rameters of variation exhibited by English Filler-Gap constructions. It de monstrates that a detailed, fully explicit account of the observed variation is availab le within a framework embracing the notion “grammatical construction.” This accou nt, which explicates similarities and differences among topicalization, interrog atives, relatives, exclamatives, and comparative correlatives in terms of linguistic types a nd hierarchical constraint inheritance, is articulated in detail within the framework f Sign-Based Construction Grammar, a kind of Head-Driven Phrase Structure Grammar ( HPSG). The results presented here stand as a serious challenge to any analysis inco rporating transformational operations, especially proposals couched within Chomsky’ s “Minimalist Program.”