nils-herrmann/clean_scirepeval huggingface dataset:
A Study of Android Malware Detection Techniques and Machine Learning

Android OS is one of the widely used mobile Operating Systems. The number of malicious applications and adwares are increasing constantly on par with the number of mobile devices. A great number of commercial signature based tools are available on the market which prevent to an extent the penetration and distribution of malicious applications. Numerous researches have been conducted which claims that traditional signature based detection system work well up to certain level and malware authors use numerous techniques to evade these tools. So given this state of affairs, there is an increasing need for an alternative, really tough malware detection system to complement and rectify the signature based system. Recent substantial research focused on machine learning algorithms that analyze features from malicious application and use those features to classify and detect unknown malicious applications. This study summarizes the evolution of malware detection techniques based on machine learning algorithms focused
AI-Assisted Game Debugging with Cicero

We present Cicero, a mixed-initiative application for prototyping two-dimensional sprite-based games across different genres such as shooters, puzzles, and action games. Cicero provides a host of features which can offer assistance in different stages of the game development process. Noteworthy features include AI agents for gameplay simulation, a game mechanics recommender system, a playtrace aggregator, heatmap-based game analysis, a sequential replay mechanism, and a query system that allows searching for particular interaction patterns. In order to evaluate the efficacy and usefulness of the different features of Cicero, we conducted a user study in which we compared how users perform in game debugging tasks with different kinds of assistance.
Design and Fabrication of Integrated Magnetic MEMS Energy Harvester for Low Frequency Applications

An integrated vibration-to-electrical MEMS electromagnetic energy harvester with low resonant frequency is designed, simulated, fabricated and tested. Special structures and magnet arrays of the device are designed and simulated for favorable low frequency performance. Both FEM simulations and system-level simulations are conducted to investigate the induced voltage of the device. Two types of harvester are fabricated with different magnet arrays and the magnetic field of the two harvesters is investigated. With the combination of the microfabricated metal structures and the electroplated CoNiMnP permanent micro magnets, the designed harvesters are of small size (5 mm ×5 mm × 0.53 mm) without manual assembly of conventional bulk magnets. The output power density is 0.03 μW/cm3 with optimized resistance load at 64 Hz. This magnetic harvester is suitable for batch fabrication through MEMS process and can be utilized to drive microelectronic devices, such as micro implantable and portable devices.
Accurate network anomaly classification with generalized entropy metrics

The accurate detection and classification of network anomalies based on traffic feature distributions is still a major challenge. Together with volume metrics, traffic feature distributions are the primary source of information of approaches scalable to high-speed and large scale networks. In previous work, we proposed to use the Tsallis entropy based traffic entropy spectrum (TES) to capture changes in specific activity regions, such as the region of heavy-hitters or rare elements. Our preliminary results suggested that the TES does not only provide more details about an anomaly but might also be better suited for detecting them than traditional approaches based on Shannon entropy. We refine the TES and propose a comprehensive anomaly detection and classification system called the entropy telescope. We analyze the importance of different entropy features and refute findings of previous work reporting a supposedly strong correlation between different feature entropies and provide an extensive evaluation of our entropy telescope. Our evaluation with three different detection methods (Kalman filter, PCA, KLE), one classification method (SVM) and a rich set of anomaly models and real backbone traffic demonstrates the superiority of the refined TES approach over TES and the classical Shannon-only approaches. For instance, we found that when switching from Shannon to the refined TES approach, the PCA method detects small to medium sized anomalies up to 20% more accurately. Classification accuracy is improved by up to 19% when switching from Shannon-only to TES and by another 8% when switching from TES to the refined TES approach. To complement our evaluation, we run the entropy telescope on one month of backbone traffic finding that most prevalent anomalies are different types of scanning (69–84%) and reflector DDoS attacks (15–29%).
Toward intelligent machine-to-machine communications in smart grid

The advanced metering infrastructure of the smart grid presents the biggest growth potential in the machine-to-machine market today. Spurred by recent advances in M2M technologies, SG smart meters are expected not to require human intervention in characterizing power requirements and energy distribution. However, there are many challenges in the design of the SG communications network whereby the electrical appliances and smart meters are able to exchange information pertaining to varying power requirements. Furthermore, different types of M2M gateways are required at different points (e.g., at home, in the building, at the neighborhood, and so forth) of the SG communication network. This article surveys a number of existing communication technologies that can be adopted for M2M communication in SG. Among these, the most reliable technology to facilitate M2M communication in the SG home area network is pointed out, and its shortcoming is also noted. Furthermore, a possible solution to deal with this shortcoming to improve SG communications scalability is also presented.
ANTLRWorks: an ANTLR grammar development environment

Programmers tend to avoid using language tools, resorting to ad-hoc methods, because tools can be hard to use, their parsing strategies can be difficult to understand and debug, and their generated parsers can be opaque black-boxes. In particular, there are two very common difficulties encountered by grammar developers: Understanding why a grammar fragment results in a parser nondeterminism and determining why a generated parser incorrectly interprets an input sentence. This paper describes ANTLRWorks, a complete development environment for ANTLR grammars that attempts to resolve these difficulties and, in general, make grammar development more accessible to the average programmer. The main components are a grammar editor with refactoring and navigation features, a grammar interpreter, and a domain-specific grammar debugger. ANTLRWorks’ primary contributions are a parser nondeterminism visualizer based upon syntax diagrams and a time-traveling debugger that pays special attention to parser decision-making by visualizing lookahead usage and speculative parsing during backtracking.
What Did You Mean? - Facing the Challenges of User-generated Software Requirements

Existing approaches towards service composition demand requirements of the customers in terms of service templates, service query profiles, or partial process models. However, addressed non-expert customers may be unable to fill-in the slots of service templates as requested or to describe, for example, preand postconditions, or even have difficulties in formalizing their requirements. Thus, our idea is to provide nonexperts with suggestions how to complete or clarify their requirement descriptions written in natural language. Two main issues have to be tackled: (1) partial or full inability (incapacity) of non-experts to specify their requirements correctly in formal and precise ways, and (2) problems in text analysis due to fuzziness in natural language. We present ideas how to face these challenges by means of requirement disambiguation and completion. Therefore, we conduct ontology-based requirement extraction and similarity retrieval based on requirement descriptions that are gathered from App marketplaces. The innovative aspect of our work is that we support users without expert knowledge in writing their requirements by simultaneously resolving ambiguity, vagueness, and underspecification in natural language.
The semantics of poetry: A distributional reading

Poetry is rarely a focus of linguistic investigation. This i s far from surprising, as poetic language, especially in modern and contemporary l ite ature, seems to defy the general rules of syntax and semantics. This paper as sumes, however, that linguistic theories should ideally be able to account for cr eative uses of language, down to their most difficult incarnations. It proposes that a t he semantic level, what distinguishes poetry from other uses of language may be its ability to trace conceptual patterns which do not belong to everyday discour e but are latent in our shared language structure. Distributional semantics p rovides a theoretical and experimental basis for this exploration. First, the notion of a specific ‘semantics of poetry’ is discussed, with some help from literary criticis m and philosophy. Then, distributionalism is introduced as a theory supporting the notion that the meaning of poetry comes from the meaning of ordinary language. In the second part of the paper, experimental results are provided showing that a) di stributional representations can model the link between ordinary and poetic languag e, b) a distributional model can experimentally distinguish between poetic and ra omised textual output, regardless of the complexity of the poetry involved, c) there is a stable, but not immediately transparent, layer of meaning in poetry, wh ich can be captured distributionally, across different levels of poetic compl exity.
A Framework for Cross-Layer Evaluation of 5G mmWave Cellular Networks in ns-3

The growing demand for ubiquitous mobile data services along with the scarcity of spectrum in the sub-6 GHz bands has given rise to the recent interest in developing wireless systems that can exploit the large amount of spectrum available in the millimeter wave (mmWave) frequency range. Due to its potential for multi-gigabit and ultra-low latency links, mmWave technology is expected to play a central role in 5th Generation (5G) cellular networks. Overcoming the poor radio propagation and sensitivity to blockages at higher frequencies presents major challenges, which is why much of the current research is focused at the physical layer. However, innovations will be required at all layers of the protocol stack to effectively utilize the large air link capacity and provide the end-to-end performance required by future networks. Discrete-event network simulation is a useful tool for researchers and will no doubt be invaluable for evaluating novel 5G protocols and systems from an end-to-end perspective. In this work, we present the first-of-its-kind, opensource framework for modeling mmWave cellular networks in the ns-3 simulator. Channel models are provided along with a configurable physical and MAC-layer implementation, which can be interfaced with the higher-layer protocols and core network model from the ns-3 LTE module for simulating end-to-end connectivity. The framework is demonstrated through several example simulations showing the performance of our custom mmWave stack.
A tomographic formulation of spotlight-mode synthetic aperture radar

Spotlight-mode synthetic aperture radar (spotlight-mode SAR) synthesizes high-resolution terrain maps using data gathered from multiple observation angles. This paper shows that spotlight-mode SAR can be interpreted as a tomographic reeonstrution problem and analyzed using the projection-slice theorem from computer-aided tomograpy (CAT). The signal recorded at each SAR transmission point is modeled as a portion of the Fourier transform of a central projection of the imaged ground area. Reconstruction of a SAR image may then be accomplished using algorithms from CAT. This model permits a simple understanding of SAR imaging, not based on Doppler shifts. Resolution, sampling rates, waveform curvature, the Doppler effect, and other issues are also discussed within the context of this interpretation of SAR.
Adaptation of the Nomophobia Questionnaire (NMP-Q) to Spanish in a sample of adolescents.

Nomophobia is the fear of being out of mobile phone contact. People suffering from this anxiety disorder have feelings of stress and nervousness when access to their mobiles or computers is not possible. This work is an adaptation and validation study of the Spanish version of the Nomophobia Questionnaire (NMP-Q).



The study included 306 students (46.1% males and 53.9% females) with ages ranging 13 to 19 years (Md=15.41±1.22).



Exploratory factor analysis revealed four dimensions that accounted for 64.4% of total variance. The ordinal α-value was 0.95, ranging from 0.75 to 0.92 across factors. Measure of stability was calculated by the testretest method (r=0.823). Indicators of convergence with the Spanish versions of the “Mobile Phone Problem Use Scale” (r=0.654) and the “Generalized Problematic Internet Use Scale” (r=0.531) were identified. Problematic mobile phone use patterns were examined taking the 15P, 80P and 95P percentiles as cut-off points. Scores of 39, 87 and 116 on NMP-Q corresponded to occasional, at-risk and problematic users, respectively.



Psychometric analysis shows that the Spanish version of the NMP-Q is a valid and reliable tool for the study of nomophobia.
Bigrams of Syntactic Labels for Authorship Discrimination of Short Texts

We present a method for authorship discrimination that is based on the frequency of bigrams of syntactic labels that arise from partial parsing of the text. We show that this method, alone or combined with other classification features, achieves a high accuracy on discrimination of the work of Anne and Charlotte Brontë, which is very difficult to do by traditional methods. Moreover, high accuracies are achieved even on fragments of text little more than 200 words long.
The Use Of Edmodo In Creating An Online Learning Community Of Practice For Learning To Teach Science

This study aimed to create an online community of practice by creating a virtual classroom in the Edmodo application and ascertain the opinions of pre-service primary teachers about the effects of Edmodo on their learning to teach science and availability of Edmodo. The research used a case study, which is one method of descriptive research. During the implementation process, pre-service primary teachers used Edmodo to share activities they had designed that centred on the scientific concepts taught in primary science education programmes. They also shared their diary entries that outlined their experiences and views after they had practised their activities in a real classroom. 58 pre-service primary teachers participated in the study. The author developed a questionnaire and it included one closed-ended and six open-ended questions; the questionnaire was used as the data collection tool. The pre-service primary teachers used Edmodo for 12 weeks. Descriptive and content analysis methods were used to analyse the data obtained from the study. The results obtained from the data analysis showed that pre-service primary teachers generally had positive views about the use of Edmodo in teacher education programmes. Most pre-service primary teachers stated that Edmodo provides the possibility of sharing knowledge, experiences and views. However, some pre-service teachers stated that Edmodo has some limitations; for example, the fact that it requires the user to have internet access. As a result, it can be said that Edmodo can be used to create an online community of practice in teacher education programmes.
An Adaption of BIOASQ Question Answering dataset for Machine Reading systems by Manual Annotations of Answer Spans

BIOASQ Task B Phase B challenge focuses on extracting answers from snippets for a given question. The dataset provided by the organizers contains answers, but not all their variants. Henceforth a manual annotation was performed to extract all forms of correct answers. This article shows the impact of using all occurrences of correct answers for training on the evaluation scores which are improved significantly.
Sequential Query Expansion using Concept Graph

Manually and automatically constructed concept graphs (or semantic networks), in which the nodes correspond to words or phrases and the typed edges designate semantic relationships between words and phrases, have been previously shown to be rich sources of effective latent concepts for query expansion. However, finding good expansion concepts for a given query in large and dense concept graphs is a challenging problem, since the number of candidate concepts that are related to query terms and phrases and need to be examined increases exponentially with the distance from the original query concepts. In this paper, we propose a two-stage feature-based method for sequential selection of the most effective concepts for query expansion from a concept graph. In the first stage, the proposed method weighs the concepts according to different types of computationally inexpensive features, including collection and concept graph statistics. In the second stage, a sequential concept selection algorithm utilizing more expensive features is applied to find the most effective expansion concepts at different distances from the original query concepts. Experiments on TREC datasets of different type indicate that the proposed method achieves significant improvement in retrieval accuracy over state-of-the-art methods for query expansion using concept graphs.
Evaluation in Design-Oriented Research

Design has been recognized for a long time both as art and as science. In the sixties of the previous century design-oriented research began to draw the attention of scientific researchers and methodologists, not only in technical engineering but also in the social sciences. However, a rather limited methodology for design-oriented research has been developed, especially as to the social sciences. In this article we introduce evaluation methodology and research methodology as a systematic input in the process of designing. A designing cycle is formulated with six stages, and for each of these stages operations, guidelines and criteria for evaluation are defined. All this may be used for a considerable improvement of the process and product of designing.
Inpainting of Continuous Frames of Old Movies Based on Deep Neural Network

With the changes of eras, traditional movies may suffer continuous frame damage after digits due to improper preservation. In order to solve this problem, we proposed a new inpainting technique for continuous video sequences based on deep neural networks. We introduced the latest image restoration techniques for inpainting key frames of new scenes. Then we use the deep neural network interpolation algorithm to interpolate the intermediate frames, so that the video can achieve a coherent effect in time. In order to preserve the original information as much as possible, we only replace the damaged areas and preserve most of undamaged areas, and finally perform image blending. We test different types of videos and compare them with other methods. Our method has higher quality video inpainting than existing methods.
Development and initial validation of a short three-dimensional inventory of character strengths

Character strength is described as a positive and organized pattern of emotions, thoughts, and behaviors. It serves as a schema that organizes categories of information toward the self, others, and the world, and provides the self-aware knowledge that facilitates the pursuit of goals, values, and ethical principles. Recent research has suggested that three reliable factors emerge from the measures of character strengths: caring, inquisitiveness, and self-control. The goal of this paper is to develop a psychometrically sound short measure of character strength. The questions were addressed in two studies using two independent samples: a cross-cultural (i.e., 518 Asians and 556 Westerners) sample, and a cross-population (i.e., 175 community participants and 171 inpatients) sample in China. Findings from the exploratory and confirmatory factor analysis suggested a cross-cultural three-factor model of character strength that could be measured by the Three-dimensional Inventory of Character Strengths (TICS). A multigroup confirmatory factor analysis further indicated that the number of factors and factor loadings was invariant in the medical and community samples. This result indicated that the brief inventory could be applied to a medical context. Internal reliability, content validity, and predictive validity were good, although the predictive validity of the three character strengths for psychological symptoms in the medical sample was more modest than that in the community sample. TICS is expected to be used for screening populations at risk, and a tool to aid mental health professionals in group-based treatment/intervention planning. It also should be noted that this short inventory should be used with caution for individual decision making.
An RNN-based Music Language Model for Improving Automatic Music Transcription

In this paper, we investigate the use of Music Language Models (MLMs) for improving AutomaticMusic Transcription performance. The MLMs are trained on sequences of symbolic polyphonic music from the Nottingham dataset. We train Recurrent Neural Network (RNN)-based models, as they are capable of capturing complex temporal structure present in symbolic music data. Similar to the function of language models in automatic speech recognition, we use the MLMs to generate a prior probability for the occurrence of a sequence. The acoustic AMT model is based on probabilistic latent component analysis, and prior information from the MLM is incorporated into the transcription framework using Dirichlet priors. We test our hybrid models on a dataset of multiple-instrument polyphonic music and report a significant 3% improvement in terms of Fmeasure, when compared to using an acoustic-only model.
Ontologies in E-Learning: Review of the Literature

We have witnessed a great interest in ontologies as an emerging technology within the Semantic Web. Its foundation that was laid in the last decade paved the way for the development of ontologies and systems that use ontologies in various domains, including the E-Learning domain. In this article, we survey key contributions related to the development and use of ontologies in the domain of E-Learning systems. We provide a framework for classification of the literature that is useful for the community of practice in categorizing work in this field and for determining possible research lines. We also discuss future trends in this area.
Occurrence of partenites in mollusks and the influence that metacercaria of Apophallus muehlingi (Jagerskiold, 1898) and Posthodiplostomum cuticola (Nordmann, 1832) has on some biochemical parameters in fish

A heavy parasitic load from trematode partenites of Apophallus muehlingi (Jagerskiold, 1898) was observed in the mollusk population of Lithoglyphus naticoides (Pfeiffer) after its natural invasion into the Rybinsk Reservoir. An elevated concentration of glycogen was detected in the muscles of infected fish, both in the natural populations and under experimental conditions. Interspecific differences were found for the protein concentrations when elevated concentrations were observed in the muscles of the infected roach, and no differences were discovered for the goldfish (both infected and parasite-free). The ties between the biochemical parameters and motor activity are discussed for the infected fish.