nils-herrmann/clean_scirepeval huggingface dataset:
DDoS Attacks With Randomized Traffic Innovation: Botnet Identification Challenges and Strategies

Distributed Denial-of-Service (DDoS) attacks are usually launched through the botnet, an “army” of compromised nodes hidden in the network. Inferential tools for DDoS mitigation should accordingly enable an early and reliable discrimination of the normal users from the compromised ones. Unfortunately, the recent emergence of attacks performed at the application layer has multiplied the number of possibilities that a botnet can exploit to conceal its malicious activities. New challenges arise, which cannot be addressed by simply borrowing the tools that have been successfully applied so far to earlier DDoS paradigms. In this paper, we offer basically three contributions: 1) we introduce an abstract model for the aforementioned class of attacks, where the botnet emulates normal traffic by continually learning admissible patterns from the environment; 2) we devise an inference algorithm that is shown to provide a consistent (i.e., converging to the true solution as time elapses) estimate of the botnet possibly hidden in the network; and 3) we verify the validity of the proposed inferential strategy on a test-bed environment. Our tests show that, for several scenarios of implementation, the proposed botnet identification algorithm needs an observation time in the order of (or even less than) 1 min to identify correctly almost all bots, without affecting the normal users’ activity.
Improved Semi-supervised Learning with GANs using Manifold Invariances

Semi-supervised learning methods using Generative Adversarial Networks (GANs) have shown promising empirical success recently. Most of these methods use a shared discriminator/classifier which discriminates real examples from fake while also predicting the class label. Motivated by the ability of the GANs generator to capture the data manifold well, we propose to estimate the tangent space to the data manifold using GANs and employ it to inject invariances into the classifier. In the process, we propose enhancements over existing methods for learning the inverse mapping (i.e., the encoder) which greatly improves in terms of semantic similarity of the reconstructed sample with the input sample. We observe considerable empirical gains in semi-supervised learning over baselines, particularly in the cases when the number of labeled examples is low. We also provide insights into how fake examples influence the semi-supervised learning procedure.
Creative accounting : some ethical issues of macro-and micro-manipulation

This paper examines two principal categories of manipulative behaviour. The term ‘macro-manipulation’ is used to describe the lobbying of regulators to persuade them to produce regulation that is more favourable to the interests of preparers. ‘Micromanipulation’ describes the management of accounting figures to produce a biased view at the entity level. Both categories of manipulation can be viewed as attempts at creativity by financial statement preparers. The paper analyses two cases of manipulation which are considered in an ethical context. The paper concludes that the manipulations described in it can be regarded as morally reprehensible. They are not fair to users, they involve an unjust exercise of power, and they tend to weaken the authority of accounting regulators.
Criticality in the brain: A synthesis of neurobiology, models and cognition

Cognitive function requires the coordination of neural activity across many scales, from neurons and circuits to large-scale networks. As such, it is unlikely that an explanatory framework focused upon any single scale will yield a comprehensive theory of brain activity and cognitive function. Modelling and analysis methods for neuroscience should aim to accommodate multiscale phenomena. Emerging research now suggests that multi-scale processes in the brain arise from so-called critical phenomena that occur very broadly in the natural world. Criticality arises in complex systems perched between order and disorder, and is marked by fluctuations that do not have any privileged spatial or temporal scale. We review the core nature of criticality, the evidence supporting its role in neural systems and its explanatory potential in brain health and disease.
The Effect of a Classroom-Based Intensive Robotics and Programming Workshop on Sequencing Ability in Early Childhood

This paper examines the impact of programming robots on sequencing ability during a 1-week intensive robotics workshop at an early childhood STEM magnet school in the Harlem area of New York City. Children participated in computer programming activities using a developmentally appropriate tangible programming language CHERP, specifically designed to program a robot’s behaviors. The study assessed 27 participants’ sequencing skills before and after the programming and robotics curricular intervention using a picture-story sequencing task and compared those skills to a control group. Pre-test and post-test scores were compared using a paired sample t test. The group of children who participated in the 1-week robotics and programming workshop experienced significant increases in post-test compared to pre-test sequencing scores.
AOGNets: Deep AND-OR Grammar Networks for Visual Recognition

This paper presents a method of learning deep AND-OR Grammar (AOG) networks for visual recognition, which we term AOGNets. An AOGNet consists of a number of stages each of which is composed of a number of AOG building blocks. An AOG building block is designed based on a principled AND-OR grammar and represented by a hierarchical and compositional AND-OR graph [33, 46]. Each node applies some basic operation (e.g., Conv-BatchNormReLU) to its input. There are three types of nodes: an AND-node explores composition, whose input is computed by concatenating features of its child nodes; an OR-node represents alternative ways of composition in the spirit of exploitation, whose input is the element-wise sum of features of its child nodes; and a Terminal-node takes as input a channel-wise slice of the input feature map of the AOG building block. AOGNets aim to harness the best of two worlds (grammar models and deep neural networks) in representation learning with end-to-end training. In experiments, AOGNets are tested on three highly competitive image classification benchmarks: CIFAR-10, CIFAR-100 and ImageNet-1K. AOGNets obtain better performance than the widely used Residual Net  and most of its variants, and are comparable to the Dense Net . AOGNets are also tested in object detection on the PASCAL VOC 2007 and 2012  using the vanilla Faster RCNN [30] system and obtain better performance than the Residual Net.
Lux0R: Detection of Malicious PDF-embedded JavaScript code through Discriminant Analysis of API References

JavaScript is a dynamic programming language adopted in a variety of applications, including web pages, PDF Readers, widget engines, network platforms, office suites. Given its widespread presence throughout different software platforms, JavaScript is a primary tool for the development of novel -rapidly evolving- malicious exploits. If the classical signature- and heuristic-based detection approaches are clearly inadequate to cope with this kind of threat, machine learning solutions proposed so far suffer from high false-alarm rates or require special instrumentation that make them not suitable for protecting end-user systems.
 In this paper we present Lux0R "Lux 0n discriminant References", a novel, lightweight approach to the detection of malicious JavaScript code. Our method is based on the characterization of JavaScript code through its API references, i.e., functions, constants, objects, methods, keywords as well as attributes natively recognized by a JavaScript Application Programming Interface (API). We exploit machine learning techniques to select a subset of API references that characterize malicious code, and then use them to detect JavaScript malware. The selection algorithm has been thought to be "secure by design" against evasion by mimicry attacks. In this investigation, we focus on a relevant application domain, i.e., the detection of malicious JavaScript code within PDF documents. We show that our technique is able to achieve excellent malware detection accuracy, even on samples exploiting never-before-seen vulnerabilities, i.e., for which there are no examples in training data. Finally, we experimentally assess the robustness of Lux0R against mimicry attacks based on feature addition.
Exploring Principles-of-Art Features For Image Emotion Recognition

Emotions can be evoked in humans by images. Most previous works on image emotion analysis mainly used the elements-of-art-based low-level visual features. However, these features are vulnerable and not invariant to the different arrangements of elements. In this paper, we investigate the concept of principles-of-art and its influence on image emotions. Principles-of-art-based emotion features (PAEF) are extracted to classify and score image emotions for understanding the relationship between artistic principles and emotions. PAEF are the unified combination of representation features derived from different principles, including balance, emphasis, harmony, variety, gradation, and movement. Experiments on the International Affective Picture System (IAPS), a set of artistic photography and a set of peer rated abstract paintings, demonstrate the superiority of PAEF for affective image classification and regression (with about 5% improvement on classification accuracy and 0.2 decrease in mean squared error), as compared to the state-of-the-art approaches. We then utilize PAEF to analyze the emotions of master paintings, with promising results.
Noise Performance of a Regulated Cascode Transimpedance Amplifier for Radiation Detectors

A low noise transimpedance amplifier (TIA) is used in radiation detectors to transform the current pulse produced by a photo-sensitive device into an output voltage pulse with a specified amplitude and shape. We consider here the specifications of a PET (positron emission tomography) system. We review the traditional approach, feedback TIA, using an operational amplifier with feedback, and we investigate two alternative circuits: the common-gate TIA, and the regulated cascode TIA. We derive the transimpedance function (the poles of which determine the pulse shaping); we identify the transistor in each circuit that has the dominant noise source, and we obtain closed-form equations for the rms output noise voltage. We find that the common-gate TIA has high noise, but the regulated cascode TIA has the same dominant noise contribution as the feedback TIA, if the same maximum transconductance value is considered. A circuit prototype of a regulated cascode TIA is designed in a 0.35 μm CMOS technology, to validate the theoretical results by simulation and by measurement.
Machine Learning and Cloud Computing: Survey of Distributed and SaaS Solutions

Applying popular machine learning algorithms to large amounts of data raised new challenges for the ML practitioners. Traditional ML libraries does not support well processing of huge datasets, so that new approaches were needed. Parallelization using modern parallel computing frameworks, such as MapReduce, CUDA, or Dryad gained in popularity and acceptance, resulting in new ML libraries developed on top of these frameworks. We will briefly introduce the most prominent industrial and academic outcomes, such as Apache Mahout, GraphLab or Jubatus. We will investigate how cloud computing paradigm impacted the field of ML. First direction is of popular statistics tools and libraries (R system, Python) deployed in the cloud. A second line of products is augmenting existing tools with plugins that allow users to create a Hadoop cluster in the cloud and run jobs on it. Next on the list are libraries of distributed implementations for ML algorithms, and on-premise deployments of complex systems for data analytics and data mining. Last approach on the radar of this survey is ML as Software-as-a-Service, several BigData start-ups (and large companies as well) already opening their solutions to the market.
Descriptive and prescriptive data cleaning

Data cleaning techniques usually rely on some quality rules to identify violating tuples, and then fix these violations using some repair algorithms. Oftentimes, the rules, which are related to the business logic, can only be defined on some target report generated by transformations over multiple data sources. This creates a situation where the violations detected in the report are decoupled in space and time from the actual source of errors. In addition, applying the repair on the report would need to be repeated whenever the data sources change. Finally, even if repairing the report is possible and affordable, this would be of little help towards identifying and analyzing the actual sources of errors for future prevention of violations at the target. In this paper, we propose a system to address this decoupling. The system takes quality rules defined over the output of a transformation and computes explanations of the errors seen on the output. This is performed both at the target level to describe these errors and at the source level to prescribe actions to solve them. We present scalable techniques to detect, propagate, and explain errors. We also study the effectiveness and efficiency of our techniques using the TPC-H Benchmark for different scenarios and classes of quality rules.
Youth Unemployment in Korea : From a German and Transitional Labour Market Point of View

Youth Unemployment in Korea: From a German and Transitional Labour Market Point of View By conventional statistics, youth unemployment seems to be quite moderate in Korea: ‘only’ 9.6 percent of the ‘active’ youth labour force was unemployed compared to 21.4 percent in EU-27 in 2011. Germany, with a youth unemployment rate of 8.5 percent, is one of the very few European countries outperforming Korea. But the Korean case is in one respect unusual. From the perspective of intergenerational risk sharing Korea’s youth unemployment rate is 4.6 times higher than the unemployment rate of adults aged 45 to 54; in Germany, this figure is only 1.7. Further peculiarities come up if unemployment is measured by the number of youth not in employment, education or training (NEET) in percent of the total youth population. Korea’s NEET figures are at the top in OECD countries, especially for youth with tertiary education. This paper throws some light to explain this conundrum: It sketches, first, the main causes of youth unemployment and the general policy interventions; because a large part of the problem is structural, possible immediate measures to avoid long-term scar effects for the unemployed youth are briefly reviewed; differences between Europe and the United States show in particular the importance of automatic stabilizers like unemployment insurance in order to reduce the pressure on unfavourable risk sharing for youth in times of recession. The main part is devoted to possible lessons for Korea from Europe, in particular from Germany. Dual education and vocational training systems that emphasise middle level and market oriented skills are identified as institutional device both for fairer intergenerational risk sharing as well as for a smoother transition from school to work. In its outlook, the paper comes back to the puzzle of highly and academically inflated youth unemployment by referring to a possible hidden cause in Korea: A strong insurance motive might explain the overall striving for an academic degree inducing not only wasteful congestion at labour market entries but also unfair job allocation through credentialism.
The semantics/pragmatics distinction: a view from relevance theory *

The assumption that sentence types encode proposition types was shaken by Donnellan’s observation that a sentence with a definite description subject could express either a general or a singular propos ition. In other words, a single sentence type could have different truth conditions on different occasions of use. Relevance Theory holds a strong version of this “semantic underdeterminacy” thesis, according to which natural language sentences standardly fall far short of encoding propositions or proposition types. The relevance-driven pragmatic inferential mechanism is part of our “theory of mind” capacity and functions independently of any code; it follows that linguistically encoded utterance meaning need be
Sleep Quality and Academic Performance in University Students : A Wake-Up Call for College Psychologists

Both sleep deprivation and poor sleep quality are prominent in American society, especially in college student populations. Sleep problems are often a primary disorder rather than secondary to depression. The purpose of the present study was to determine if sleep deprivation and/or poor sleep quality in a sample of nondepressed university students was associated with lower academic performance. A significant negative correlation between Global Sleep Quality score (GSQ) on the Pittsburgh Sleep Quality Index and grade point average supports the hypothesis that poor sleep quality is associated with lower academic performance for nondepressed students. Implications for both the remedial (assessment and treatment) and preventive (outreach) work of college and university counseling centers is discussed.
Cultural stereotypes as gatekeepers: increasing girls’ interest in computer science and engineering by diversifying stereotypes

Despite having made significant inroads into many traditionally male-dominated fields (e.g., biology, chemistry), women continue to be underrepresented in computer science and engineering. We propose that students' stereotypes about the culture of these fields-including the kind of people, the work involved, and the values of the field-steer girls away from choosing to enter them. Computer science and engineering are stereotyped in modern American culture as male-oriented fields that involve social isolation, an intense focus on machinery, and inborn brilliance. These stereotypes are compatible with qualities that are typically more valued in men than women in American culture. As a result, when computer science and engineering stereotypes are salient, girls report less interest in these fields than their male peers. However, altering these stereotypes-by broadening the representation of the people who do this work, the work itself, and the environments in which it occurs-significantly increases girls' sense of belonging and interest in the field. Academic stereotypes thus serve as gatekeepers, driving girls away from certain fields and constraining their learning opportunities and career aspirations.
A Taxi Gap Prediction Method via Double Ensemble Gradient Boosting Decision Tree

Predicting the gap between taxi demand and supply in taxi booking apps is completely new and important but challenging. However, manually mining gap rule for different conditions may become impractical because of massive and sparse taxi data. Existing works unilaterally consider demand or supply, used only few simple features and verified by little data, but not predict the gap value. Meanwhile, none of them dealing with missing values. In this paper, we introduce a Double Ensemble Gradient Boosting Decision Tree Model(DEGBDT) to predict taxi gap. (1) Our approach specifically considers demand and supply to predict the gap between them. (2) Also, our method provides a greedy feature ranking and selecting method to exploit most reliable feature. (3) To deal with missing value, our model takes the lead in proposing a double ensemble method, which secondarily integrates different Gradient Boosting Decision Tree(GBDT) model at the different data sparse situation. Experiments on real large-scale dataset demonstrate that our approach can effectively predict the taxi gap than state-of-the-art methods, and shows that double ensemble method is efficacious for sparse data.
Assessing Creativity

In exploring the question of whether a computer program is behaving creatively, it is important to be explicit, and if possible formal, about the criteria that are being applied in making judgements of creativity. We propose a formal (and rather simplified) outline of the relevant attributes of a potentially creative program. Based on this, we posit a number of formal criteria that could be applied to rate the extent to which the program has behaved creatively. A guiding principle is that the question of what computational mechanisms might lead to creative behaviour is open and empirical, and hence we should clearly distinguish between judgements about creative achievements and theoretical proposals about potentially creative mechanisms. The intention is to focus, clarify and make more concrete the debate about creative
Hierarchical Attention Flow for Multiple-Choice Reading Comprehension

In this paper, we focus on multiple-choice reading comprehension which aims to answer a question given a passage and multiple candidate options. We present the hierarchical attention flow to adequately leverage candidate options to model the interactions among passages, questions and candidate options. We observe that leveraging candidate options to boost evidence gathering from the passages play a vital role in this task, which is ignored in previous works. In addition, we explicitly model the option correlations with attention mechanism to obtain better option representations, which are further fed into a bilinear layer to obtain the ranking score for each option. On a large-scale multiple-choice reading comprehension dataset (i.e. the RACE dataset), the proposed model outperforms two previous neural network baselines on both RACE-M and RACE-H subsets and yields the state-of-the-art overall results.
Learning Discriminative Aggregation Network for Video-Based Face Recognition

In this paper, we propose a discriminative aggregation network (DAN) method for video face recognition, which aims to integrate information from video frames effectively and efficiently. Unlike existing aggregation methods, our method aggregates raw video frames directly instead of the features obtained by complex processing. By combining the idea of metric learning and adversarial learning, we learn an aggregation network that produces more discriminative synthesized images compared to raw input frames. Our framework reduces the number of frames to be processed and significantly speed up the recognition procedure. Furthermore, low-quality frames containing misleading information are filtered and denoised during the aggregation process, which makes our system more robust and discriminative. Experimental results show that our method can generate discriminative images from video clips and improve the overall recognition performance in both the speed and accuracy on three widely used datasets.