nils-herrmann/clean_scirepeval huggingface dataset:
Analysis of the Use of XOR as an Obfuscation Technique in a Real Data Corpus

The only digital forensic tools known to provide an automated approach for evaluating XOR obfuscated data are DCCI Carver and DC3 Carver, two general-purpose carving tools developed by the Defense Cyber Crime Center (DC3). In order to determine the use of XOR as an obfuscation technique and the need to adapt additional tools, we analyzed 2,411 drive images from devices acquired from countries around the world. Using a modified version of the open source tool bulk extractor, evidence of XOR obfuscation was found on 698 drive images, with a maximum of 21,031 XOR-obfuscated features on a single drive. XOR usage in the corpus was observed in files with timestamps between the years 1995 and 2009, but the majority of the usage was found in unallocated space. XOR obfuscation was used in the corpus to circumvent malware detection and reverse engineering, to hide information that was apparently being exfiltrated, and by malware detection tools for their quarantine directories and to distribute malware signatures. The results indicate that XOR obfuscation is important to consider when performing malware investigations. However, since the corpus does not contain data sets that are known to have been used by malicious entities, it is difficult to draw conclusions regarding the importance of extracting and examining XOR obfuscated files in criminal, counterintelligence and counterterrorism cases without further research.
Behavioral Assessment of Emotion Discrimination, Emotion Regulation, and Cognitive Control in Childhood, Adolescence, and Adulthood

Emotion discrimination, emotion regulation, and cognitive control are three related, yet separable processes that emerge over the course of development. The current study tested 100 children, adolescents, and adults on an Emotional Go/Nogo task, illustrating the ability of this paradigm to identify the unique developmental patterns for each of these three processes in the context of both positive (happy) and negative emotions (fear, sad, and anger), across three different age groups. Consistent with previous literature, our findings show that emotion discrimination and regulatory abilities (both cognitive control and emotion regulation) improve steadily for each age group, with each age group showing unique patterns of performance. The findings suggest that emotion regulation is constructed from basic cognition control and emotion discrimination skills. The patterns of behavior from the Emotional Go/Nogo task provide normative benchmark data across a wide range of emotions that can be used for future behavioral and neuroimaging studies that examine the developmental construction of emotion regulatory processes.
Government-Driven Participation and Collective Intelligence: A Case of the Government 3.0 Initiative in Korea

The Park Geun-hye Administration of Korea (2013–2017) aims to increase the level of transparency and citizen trust in government through the Government 3.0 initiative. This new initiative for public sector innovation encourages citizen-government collaboration and collective intelligence, thereby improving the quality of policy-making and implementation and solving public problems in a new way. However, the national initiative that identifies collective intelligence and citizen-government collaboration alike fails to understand what the wisdom of crowds genuinely means. Collective intelligence is not a magic bullet to solve public problems, which are called “wicked problems”. Collective deliberation over public issues often brings pain and patience, rather than fun and joy. It is not so easy that the public finds the best solution for soothing public problems through collective deliberation. The Government 3.0 initiative does not pay much attention to difficulties in gathering scattered wisdom, but rather highlights uncertain opportunities created by collective interactions and communications. This study deeply discusses the weaknesses in the logic of, and approach to, collective intelligence underlying the Government 3.0 initiative in Korea and the overall influence of the national initiative on participatory democracy.
Mobileinsight: extracting and analyzing cellular network information on smartphones

We design and implement MobileInsight, a software tool that collects, analyzes and exploits runtime network information from operational cellular networks. MobileInsight runs on commercial off-the-shelf phones without extra hardware or additional support from operators. It exposes protocol messages on both control plane and (below IP) data plane from the 3G/4G chipset. It provides in-device protocol analysis and operation logic inference. It further offers a simple API, through which developers and researchers obtain access to low-level network information for their mobile applications. We have built three showcases to illustrate how MobileInsight is applied to cellular network research.
Low parasite loads accompany the invading population of the bumblebee, Bombus terrestris in Tasmania

In its native Europe, the bumblebee, Bombus terrestris (L.) has co-evolved with a large array of parasites whose numbers are negatively linked to the genetic diversity of the colony. In Tasmania B. terrestris was first detected in 1992 and has since spread over much of the state. In order to understand the bee’s invasive success and as part of a wider study into the genetic diversity of bumblebees across Tasmania, we screened bees for co-invasions of ectoparasitic and endoparasitic mites, nematodes and micro-organisms, and searched their nests for brood parasites. The only bee parasite detected was the relatively benign acarid mite Kuzinia laevis (Dujardin) whose numbers per bee did not vary according to region. Nests supported no brood parasites, but did contain the pollen-feeding life stages of K. laevis. Upon summer-autumn collected drones and queens, mites were present on over 80% of bees, averaged ca. 350–400 per bee and were more abundant on younger bees. Nest searching spring queens had similar mite numbers to those collected in summer-autumn but mite numbers dropped significantly once spring queens began foraging for pollen. The average number of mites per queen bee was over 30 fold greater than that reported in Europe. Mite incidence and mite numbers were significantly lower on worker bees than drones or queens, being present on just 51% of bees and averaging 38 mites per bee. Our reported incidence of worker bee parasitism by this mite is 5–50 times higher than reported in Europe. That only one parasite species co-invaded Tasmania supports the notion that a small number of queens founded the Tasmanian population. However, it is clearly evident that both the bee in the absence of parasites, and the mite have been extraordinarily successful invaders.
Submodular subset selection for large-scale speech training data

We address the problem of subselecting a large set of acoustic data to train automatic speech recognition (ASR) systems. To this end, we apply a novel data selection technique based on constrained submodular function maximization. Though NP-hard, the combinatorial optimization problem can be approximately solved by a simple and scalable greedy algorithm with constant-factor guarantees. We evaluate our approach by subselecting data from 1300 hours of conversational English telephone data to train two types large-vocabulary speech recognizers, one with Gaussian mixture model (GMM) based acoustic models, and another based on deep neural networks (DNNs). We show that training data can be reduced significantly, and that our technique outperforms both random selection and a previously proposed selection method utilizing comparable resources. Notably, using the submodular selection method, the DNN system using only about 5% of the training data is able to achieve performance on par with the GMM system using 100% of the training data - with the baseline subset selection methods, however, the DNN system is unable to accomplish this correspondence.
A stabilized multigrid solver for hyperelastic image registration

Correspondence Lars Ruthotto, Department of Mathematics and Computer Science, Emory University, 400 Dowman Dr, Atlanta, GA 30322, USA.  Image registration is a central problem in a variety of areas involving imaging techniques and is known to be challenging and ill-posed. Regularization functionals based on hyperelasticity provide a powerful mechanism for limiting the ill-posedness. A key feature of hyperelastic image registration approaches is their ability to model large deformations while guaranteeing their invertibility, which is crucial in many applications. To ensure that numerical solutions satisfy this requirement, we discretize the variational problem using piecewise linear finite elements, and then solve the discrete optimization problem using the Gauss–Newton method. In this work, we focus on computational challenges arising in approximately solving the Hessian system. We show that the Hessian is a discretization of a strongly coupled system of partial differential equations whose coefficients can be severely inhomogeneous. Motivated by a local Fourier analysis, we stabilize the system by thresholding the coefficients. We propose a Galerkin-multigrid scheme with a collective pointwise smoother. We demonstrate the accuracy and effectiveness of the proposed scheme, first on a two-dimensional problem of a moderate size and then on a large-scale real-world application with almost 9 million degrees of freedom.
Communication Infrastructure Design in Cyber Physical Systems with Applications in Smart Grids: A Hybrid System Framework

This paper provides a survey on communications in CPS and a tutorial of a unified framework, i.e., hybrid system featured by the co-existence of discrete and continuous system states, for the design of communication infrastructure in cyber physical system (CPS). First, CPS with communication infrastructure is modeled as a hybrid system, in which the discrete system state is the mode of the communication infrastructure while the continuous system state is the state of the physical dynamics. The dynamical system can be considered as being switched among multiple modes corresponding to the discrete system state. Then, the communication infrastructure design is converted to the problems of mode provisioning and scheduling. This framework is applied in the context of smart grid as a special example of CPS and is shown to achieve good performance via numerical simulations.
Performance comparison of vertical axis and horizontal axis wind turbines to get optimum power output

This paper presents the comparison of power captured by vertical and horizontal axis wind turbine (VAWT and HAWT). According to Betz, the limit of maximum coefficient power (CP) is 0.59. In this case CP is important parameter that determines the power extracted by a wind turbine we made. This paper investigates the impact of wind speed variation of wind turbine to extract the power. For VAWT we used H-darrieus type whose swept area is 3.14 m2 and so is HAWT. The wind turbines have 3 blades for each type. The air foil of both wind turbines are NACA 4412. We tested the model of wind turbine with various wind velocity which affects the performance. We have found that CP of HAWT is 0.54 with captured maximum power is 1363.6 Watt while the CP of VAWT is 0.34 with captured maximum power is 505.69 Watt. The power extracted of both wind turbines seems that HAWT power is much better than VAWT power.
DESvisual : A Visualization Tool for the DES Cipher

This paper describes a visualization tool DESvisual that helps students understand and instructors teach the building blocks of symmetric encryption. In particular, the tool depicts the primitive operations required to perform the initial permutation and one Feistel round of DES using either an eight or 16 bit input. A student can trace through an encryption performed by the tool, or can be guided through an encryption or decryption, computing the output of each operation herself. This helps students to understand the primitive operations, how these operations are composed into the DES algorithm, and how functions and their composition are depicted and documented. Furthermore, the opportunity for self-study provides an instructor greater flexibility in selecting a lecture pace over this detail-filled material. Security concerns impact an ever increasing number of the applications that computer scientists design and develop. Many security problems are solved through use of cryptography. Unfortunately, computer science students commonly have difficulty with the sophisticated mathematics used in cryptographic algorithms. This problem is exacerbated by the fact ∗Communicating Author
A study on virtual machine deployment for application outsourcing in mobile cloud computing

In mobile cloud computing, application offloading is implemented as a software level solution for augmenting computing potentials of smart mobile devices. VM is one of the prominent approaches for offloading computational load to cloud server nodes. A challenging aspect of such frameworks is the additional computing resources utilization in the deployment and management of VM on Smartphone. The deployment of Virtual Machine (VM) requires computing resources for VM creation and configuration. The management of VM includes computing resources utilization in the monitoring of VM in entire lifecycle and physical resources management for VM on Smartphone. The objective of this work is to ensure that VM deployment and management requires additional computing resources on mobile device for application offloading. This paper analyzes the impact of VM deployment and management on the execution time of application in different experiments. We investigate VM deployment and management for application processing in simulation environment by using CloudSim, which is a simulation toolkit that provides an extensible simulation framework to model the simulation of VM deployment and management for application processing in cloud-computing infrastructure. VM deployment and management in application processing is evaluated by analyzing VM deployment, the execution time of applications and total execution time of the simulation. The analysis concludes that VM deployment and management require additional resources on the computing host. Therefore, VM deployment is a heavyweight approach for process offloading on smart mobile devices.
Local collaborative ranking

Personalized recommendation systems are used in a wide variety of applications such as electronic commerce, social networks, web search, and more. Collaborative filtering approaches to recommendation systems typically assume that the rating matrix (e.g., movie ratings by viewers) is low-rank. In this paper, we examine an alternative approach in which the rating matrix is locally low-rank. Concretely, we assume that the rating matrix is low-rank within certain neighborhoods of the metric space defined by (user, item) pairs. We combine a recent approach for local low-rank approximation based on the Frobenius norm with a general empirical risk minimization for ranking losses. Our experiments indicate that the combination of a mixture of local low-rank matrices each of which was trained to minimize a ranking loss outperforms many of the currently used state-of-the-art recommendation systems. Moreover, our method is easy to parallelize, making it a viable approach for large scale real-world rank-based recommendation systems.
Large vocabulary continuous speech recognition with context-dependent DBN-HMMS

The context-independent deep belief network (DBN) hidden Markov model (HMM) hybrid architecture has recently achieved promising results for phone recognition. In this work, we propose a context-dependent DBN-HMM system that dramatically outperforms strong Gaussian mixture model (GMM)-HMM baselines on a challenging, large vocabulary, spontaneous speech recognition dataset from the Bing mobile voice search task. Our system achieves absolute sentence accuracy improvements of 5.8% and 9.2% over GMM-HMMs trained using the minimum phone error rate (MPE) and maximum likelihood (ML) criteria, respectively, which translate to relative error reductions of 16.0% and 23.2%.
A simple amplitude detector-based demodulator for resolver converters

This paper presents a simple method based on sinusoidal-amplitude detector for realizing the resolver-signal demodulator. The proposed demodulator consists of two full-wave rectifiers, two ±unity-gain amplifiers, and two sinusoidal-amplitude detectors with control switches. Two output voltages are proportional to sine and cosine envelopes of resolver-shaft angle without low-pass filter. Experimental results demonstrating characteristic of the proposed circuit are included.
Is parenting style a predictor of suicide attempts in a representative sample of adolescents?

Suicidal ideation and suicide attempts are serious but not rare conditions in adolescents. However, there are several research and practical suicide-prevention initiatives that discuss the possibility of preventing serious self-harm. Profound knowledge about risk and protective factors is therefore necessary. The aim of this study is a) to clarify the role of parenting behavior and parenting styles in adolescents' suicide attempts and b) to identify other statistically significant and clinically relevant risk and protective factors for suicide attempts in a representative sample of German adolescents.



In the years 2007/2008, a representative written survey of N = 44,610 students in the 9th grade of different school types in Germany was conducted. In this survey, the lifetime prevalence of suicide attempts was investigated as well as potential predictors including parenting behavior. A three-step statistical analysis was carried out: I) As basic model, the association between parenting and suicide attempts was explored via binary logistic regression controlled for age and sex. II) The predictive values of 13 additional potential risk/protective factors were analyzed with single binary logistic regression analyses for each predictor alone. Non-significant predictors were excluded in Step III. III) In a multivariate binary logistic regression analysis, all significant predictor variables from Step II and the parenting styles were included after testing for multicollinearity.



Three parental variables showed a relevant association with suicide attempts in adolescents - (all protective): mother's warmth and father's warmth in childhood and mother's control in adolescence (Step I). In the full model (Step III), Authoritative parenting (protective: OR: .79) and Rejecting-Neglecting parenting (risk: OR: 1.63) were identified as significant predictors (p < .001) for suicidal attempts. Seven further variables were interpreted to be statistically significant and clinically relevant: ADHD, female sex, smoking, Binge Drinking, absenteeism/truancy, migration background, and parental separation events.



Parenting style does matter. While children of Authoritative parents profit, children of Rejecting-Neglecting parents are put at risk - as we were able to show for suicide attempts in adolescence. Some of the identified risk factors contribute new knowledge and potential areas of intervention for special groups such as migrants or children diagnosed with ADHD.
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A role for introspection in AI research

The main thesis is that Introspection is recommended for the development of anthropic AI. Human-like AI, distinct from rational AI, would suit robots for care for the elderly and for other tasks that require interaction with naïve humans. “Anthropic AI” is a sub-type of human-like AI, aiming for the pre-cultured, universal intelligence that is available to healthy humans regardless of time and civilisation. This is contrasted with western, modern, well-trained and adult intelligence that is often the focus of AI. Anthropic AI would pick up local cultures and habits, ignoring optimality. Introspection is recommended for the AI developer, as a source of ideas for designing an artificial mind, in the context of technology rather than science. Existing notions of introspection are analysed, and the aspiration for “clean” or “good” introspection is exposed as a mirage. Nonetheless, introspection is shown to be a legitimate source of ideas for AI using considerations of the contexts of discovery vs. justification. Moreover, introspection is shown to be a positively plausible basis for ideas for AI since if a teacher uses introspection to extract mental skills from themselves to transmit them to a student, an AI developer can also use introspection to uncover the human skills that they want to transfer to a computer. Methods and pitfalls of this approach are detailed, including the common error of polluting one's introspection with highly-educated notions such as mathematical methods. Examples are coded and run, showing promising learning behaviour. This is interpreted as a compromise between Classic AI and Dreyfus's tradition. So far AI practitioners have largely ignored the subjective, while the Phenomenologists have not written code – this thesis bridges that gap. One of the examples is shown to have Gadamerian characteristics, as recommended by (Winograd & Flores, 1986). This serves also as a response to Dreyfus's more recent publications critiquing AI (Dreyfus, 2007, 2012).
Proteins and Carbohydrates from Red Seaweeds: Evidence for Beneficial Effects on Gut Function and Microbiota

Based on their composition, marine algae, and namely red seaweeds, are good potential functional foods. Intestinal mucosal barrier function refers to the capacity of the intestine to provide adequate containment of luminal microorganisms and molecules. Here, we will first outline the component of seaweeds and will summarize the effects of these on the regulation of mucosal barrier function. Special attention will be paid to unique components of red seaweeds: proteins and derived peptides (e.g., phycobiliproteins, glycoproteins that contain "cellulose binding domains", phycolectins and the related mycosporine-like amino acids) together with polysaccharides (e.g., floridean starch and sulfated galactans, such as carrageenans, agarans and "dl-hybrid") and minerals. These compounds have been shown to exert prebiotic effects, to regulate intestinal epithelial cell, macrophage and lymphocyte proliferation and differentiation and to modulate the immune response. Molecular mechanisms of action of peptides and polysaccharides are starting to be elucidated, and evidence indicating the involvement of epidermal growth factor receptor (EGFR), insulin-like growth factor receptor (IGFR), Toll-like receptors (TLR) and signal transduction pathways mediated by protein kinase B (PKB or AKT), nuclear factor-κB (NF-κB) and mitogen activated protein kinases (MAPK) will also be summarized. The need for further research is clear, but in vivo experiments point to an overall antiinflammatory effect of these algae, indicating that they can reinforce membrane barrier function.