nils-herrmann/clean_scirepeval huggingface dataset:
Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare

Machine learning is being used in a wide range of application domains to discover patterns in large datasets. Increasingly, the results of machine learning drive critical decisions in applications related to healthcare and biomedicine. Such health-related applications are often sensitive, and thus, any security breach would be catastrophic. Naturally, the integrity of the results computed by machine learning is of great importance. Recent research has shown that some machine-learning algorithms can be compromised by augmenting their training datasets with malicious data, leading to a new class of attacks called poisoning attacks. Hindrance of a diagnosis may have life-threatening consequences and could cause distrust. On the other hand, not only may a false diagnosis prompt users to distrust the machine-learning algorithm and even abandon the entire system but also such a false positive classification may cause patient distress. In this paper, we present a systematic, algorithm-independent approach for mounting poisoning attacks across a wide range of machine-learning algorithms and healthcare datasets. The proposed attack procedure generates input data, which, when added to the training set, can either cause the results of machine learning to have targeted errors (e.g., increase the likelihood of classification into a specific class), or simply introduce arbitrary errors (incorrect classification). These attacks may be applied to both fixed and evolving datasets. They can be applied even when only statistics of the training dataset are available or, in some cases, even without access to the training dataset, although at a lower efficacy. We establish the effectiveness of the proposed attacks using a suite of six machine-learning algorithms and five healthcare datasets. Finally, we present countermeasures against the proposed generic attacks that are based on tracking and detecting deviations in various accuracy metrics, and benchmark their effectiveness.
Introduction to Response to Intervention : What , why , and how valid is it ?

On December 3, 2004, President Bush signed into law the Individuals with Disabilities Education Improvement Act (IDEA, 2004). The revised law is different from the previous version in at least one important respect. Whereas practitioners were previously encouraged to use IQ–achievement discrepancy to identify children with learning disabilities (LD), they now may use “Response to Intervention,” or RTI, a new, alternative method. It is also a means of providing early intervention to all children at risk for school failure. IDEA 2004 permits districts to use as much as 15% of their special education monies to fund early intervention activities. All this has implications for the number and type of children identified, the kinds of educational services provided, and who delivers them. RTI may be especially important to Reading Research Quarterly readers because roughly 80% of those with an LD label have been described as reading disabled (Lyon, 1995). With RTI, there may be a larger role for reading specialists, which in turn might affect preand inservice professional development activities conducted by universities and school districts. Yet much still needs to be understood to ensure that RTI implementation will promote effective early intervention and represents a valid means of LD identification. In this article, we explain important features of RTI, why it has been promoted as a substitute for IQ–achievement discrepancy, and what remains to be understood before it may be seen as a valid means of LD identification.
Biopesticides: state of the art and future opportunities.

The use of biopesticides and related alternative management products is increasing. New tools, including semiochemicals and plant-incorporated protectants (PIPs), as well as botanical and microbially derived chemicals, are playing an increasing role in pest management, along with plant and animal genetics, biological control, cultural methods, and newer synthetics. The goal of this Perspective is to highlight promising new biopesticide research and development (R&D), based upon recently published work and that presented in the American Chemical Society (ACS) symposium "Biopesticides: State of the Art and Future Opportunities," as well as the authors' own perspectives. Although the focus is on biopesticides, included in this Perspective is progress with products exhibiting similar characteristics, namely those naturally occurring or derived from natural products. These are target specific, of low toxicity to nontarget organisms, reduced in persistence in the environment, and potentially usable in organic agriculture. Progress is being made, illustrated by the number of biopesticides and related products in the registration pipeline, yet major commercial opportunities exist for new bioherbicides and bionematicides, in part occasioned by the emergence of weeds resistant to glyphosate and the phase-out of methyl bromide. The emergence of entrepreneurial start-up companies, the U.S. Environmental Protection Agency (EPA) fast track for biopesticides, and the availability of funding for registration-related R&D for biorational pesticides through the U.S. IR-4 program provide incentives for biopesticide development, but an expanded effort is warranted both in the United States and worldwide to support this relatively nascent industry.
The relation between short-term emotion dynamics and psychological well-being: A meta-analysis.

Not only how good or bad people feel on average, but also how their feelings fluctuate across time is crucial for psychological health. The last 2 decades have witnessed a surge in research linking various patterns of short-term emotional change to adaptive or maladaptive psychological functioning, often with conflicting results. A meta-analysis was performed to identify consistent relationships between patterns of short-term emotion dynamics-including patterns reflecting emotional variability (measured in terms of within-person standard deviation of emotions across time), emotional instability (measured in terms of the magnitude of consecutive emotional changes), and emotional inertia of emotions over time (measured in terms of autocorrelation)-and relatively stable indicators of psychological well-being or psychopathology. We determined how such relationships are moderated by the type of emotional change, type of psychological well-being or psychopathology involved, valence of the emotion, and methodological factors. A total of 793 effect sizes were identified from 79 articles (N = 11,381) and were subjected to a 3-level meta-analysis. The results confirmed that overall, low psychological well-being co-occurs with more variable (overall ρ̂ = -.178), unstable (overall ρ̂ = -.205), but also more inert (overall ρ̂ = -.151) emotions. These effect sizes were stronger when involving negative compared with positive emotions. Moreover, the results provided evidence for consistency across different types of psychological well-being and psychopathology in their relation with these dynamical patterns, although specificity was also observed. The findings demonstrate that psychological flourishing is characterized by specific patterns of emotional fluctuations across time, and provide insight into what constitutes optimal and suboptimal emotional functioning.
Ontology Learning in the Deep

Recent developments in the area of deep learning have been proved extremely beneficial for several natural language processing tasks, such as sentiment analysis, question answering, and machine translation. In this paper we exploit such advances by tailoring the ontology learning problem as a transductive reasoning task that learns to convert knowledge from natural language to a logic-based specification. More precisely, using a sample of definitory sentences generated starting by a synthetic grammar, we trained Recurrent Neural Network (RNN) based architectures to extract OWL formulae from text. In addition to the low feature engineering costs, our system shows good generalisation capabilities over the lexicon and the syntactic structure. The encouraging results obtained in the paper provide a first evidence of the potential of deep learning techniques towards long term ontology learning challenges such as improving domain independence, reducing engineering costs, and dealing with variable language forms.
Tracking Knowledge Proficiency of Students with Educational Priors

Diagnosing students' knowledge proficiency, i.e., the mastery degrees of a particular knowledge point in exercises, is a crucial issue for numerous educational applications, e.g., targeted knowledge training and exercise recommendation. Educational theories have converged that students learn and forget knowledge from time to time. Thus, it is necessary to track their mastery of knowledge over time. However, traditional methods in this area either ignored the explanatory power of the diagnosis results on knowledge points or relied on a static assumption. To this end, in this paper, we devise an explanatory probabilistic approach to track the knowledge proficiency of students over time by leveraging educational priors. Specifically, we first associate each exercise with a knowledge vector in which each element represents an explicit knowledge point by leveraging educational priors (i.e., Q-matrix ). Correspondingly, each student is represented as a knowledge vector at each time in a same knowledge space. Second, given the student knowledge vector over time, we borrow two classical educational theories (i.e., Learning curve and Forgetting curve ) as priors to capture the change of each student's proficiency over time. After that, we design a probabilistic matrix factorization framework by combining student and exercise priors for tracking student knowledge proficiency. Extensive experiments on three real-world datasets demonstrate both the effectiveness and explanatory power of our proposed model.
Older Adolescents' Motivations for Social Network Site Use: The Influence of Gender, Group Identity, and Collective Self-Esteem

This study assessed motives for social network site (SNS) use, group belonging, collective self-esteem, and gender effects among older adolescents. Communication with peer group members was the most important motivation for SNS use. Participants high in positive collective self-esteem were strongly motivated to communicate with peer group via SNS. Females were more likely to report high positive collective self-esteem, greater overall use, and SNS use to communicate with peers. Females also posted higher means for group-in-self, passing time, and entertainment. Negative collective self-esteem correlated with social compensation, suggesting that those who felt negatively about their social group used SNS as an alternative to communicating with other group members. Males were more likely than females to report negative collective self-esteem and SNS use for social compensation and social identity gratifications.
High levels of psychopathic traits alters moral choice but not moral judgment

Psychopathy is a personality disorder frequently associated with immoral behaviors. Previous behavioral studies on the influence of psychopathy on moral decision have yielded contradictory results, possibly because they focused either on judgment (abstract evaluation) or on choice of hypothetical action, two processes that may rely on different mechanisms. In this study, we explored the influence of the level of psychopathic traits on judgment and choice of hypothetical action during moral dilemma evaluation. A population of 102 students completed a questionnaire with ten moral dilemmas and nine non-moral dilemmas. The task included questions targeting both judgment ("Is it acceptable to … in order to …?") and choice of hypothetical action ("Would you … in order to …?"). The level of psychopathic traits of each participant was evaluated with the Levenson Self-Report Psychopathy (LSRP) scale. Logistic regression fitted with the generalized estimating equations method analyses were conducted using responses to the judgment and choice tasks as the dependent variables and psychopathy scores as predictor. Results show that a high level of psychopathic traits, and more specifically those related to affective deficit, predicted a greater proportion of utilitarian responses for the choice but not for the judgment question. There was no first-order interaction between the level of psychopathic traits and other potential predictors. The relation between a high level of psychopathic traits and increased utilitarianism in choice of action but not in moral judgment may explain the contradictory results of previous studies where these two processes were not contrasted. It also gives further support to the hypothesis that choice of action endorsement and abstract judgment during moral dilemma evaluation are partially distinct neural and psychological processes. We propose that this distinction should be better taken into account in the evaluation of psychopathic behaviors.
A shortest augmenting path algorithm for dense and sparse linear assignment problems

We develop a shortest augmenting path algorithm for the linear assignment problem. It contains new initialization routines and a special implementation of Dijkstra's shortest path method. For both dense and sparse problems computational experiments show this algorithm to be uniformly faster than the best algorithms from the literature. A Pascal implementation is presented. Wir entwickeln einen Algorithmus mit kürzesten alternierenden Wegen für das lineare Zuordnungsproblem. Er enthält neue Routinen für die Anfangswerte und eine spezielle Implementierung der Kürzesten-Wege-Methode von Dijkstra. Sowohl für dichte als auch für dünne Probleme zeigen Testläufe, daß unser Algorithmus gleichmäßig schneller als die besten Algorithmen aus der Literatur ist. Eine Implementierung in Pascal wird angegeben.
THE SEMANTICS AND PRAGMATICS OF LEXICAL ASPECT FEATURES

This paper analyzes lexical aspect as the pr ivat ive features [+dynamic], [+durative] and [+telic], rather than a s equipollent features. Aspectual interpretation may therefore be described as monotonic composition of verbs with o ther constituents: marked verb features may not be changed, whereas unmarked features may become marked by o ther constituents. This model also predicts the variety of interpretations observed with unmarked features. Six feature combinations are proposed, representing four Vendler classes (states, activities, accomplishments, achievements) and two other attested classes (semelfactives, and stage-level states). The non-occurring combinations a r e excluded by appealing to the inherent temporal s t ructure suggested by the features.
Challenges and Opportunities in Edge Computing

Many cloud-based applications employ a data centers as a central server to process data that is generated by edge devices, such as smartphones, tablets and wearables. This model places ever increasing demands on communication and computational infrastructure with inevitable adverse effect on Quality-of-Service and Experience. The concept of Edge Computing is predicated on moving some of this computational load towards the edge of the network to harness computational capabilities that are currently untapped in edge nodes, such as base stations, routers and switches. This position paper considers the challenges and opportunities that arise out of this new direction in the computing landscape.
Gradient-based learning algorithms for recurrent connectionist networks

Recurrent connectionist networks are important because they can perform temporally extended tasks, giving them considerable power beyond the static mappings performed by the now-familiar multilayer feedforward networks. This ability to perform highly nonlinear dynamic mappings makes these networks particularly interesting to study and potentially quite useful in tasks which have an important temporal component not easily handled through the use of simple tapped delay lines. Some examples are tasks involving recognition or generation of sequential patterns and sensorimotor control. This report examines a number of learning procedures for adjusting the weights in recurrent networks in order to train such networks to produce desired temporal behaviors from input-output stream examples. The procedures are all based on the computation of the gradient of performance error with respect to network weights, and a number of strategies for computing the necessary gradient information are described. Included here are approaches which are familiar and have been rst described elsewhere, along with several novel approaches. One particular purpose of this report is to provide uniform and detailed descriptions and derivations of the various techniques in order to emphasize how they relate to one another. Another important contribution of this report is a detailed analysis of the computational requirements of the various approaches discussed.
Shifts in attention during mental fatigue: Evidence from subjective, behavioral, physiological, and eye-tracking data.

There is an increasing amount of evidence that during mental fatigue, shifts in motivation drive performance rather than reductions in finite mental energy. So far, studies that investigated such an approach have mainly focused on cognitive indicators of task engagement that were measured during controlled tasks, offering limited to no alternative stimuli. Therefore it remained unclear whether during fatigue, attention is diverted to stimuli that are unrelated to the task, or whether fatigued individuals still focused on the task but were unable to use their cognitive resources efficiently. With a combination of subjective, EEG, pupil, eye-tracking, and performance measures the present study investigated the influence of mental fatigue on a cognitive task which also contained alternative task-unrelated stimuli. With increasing time-on-task, task engagement and performance decreased, but there was no significant decrease in gaze toward the task-related stimuli. After increasing the task rewards, irrelevant rewarding stimuli where largely ignored, and task engagement and performance were restored, even though participants still reported to be highly fatigued. Overall, these findings support an explanation of less efficient processing of the task that is influenced by motivational cost/reward tradeoffs, rather than a depletion of a finite mental energy resource.
Unsupervised Domain Adaptation for Semantic Segmentation with GANs

Visual Domain Adaptation is a problem of immense importance in computer vision. Previous approaches showcase the inability of even deep neural networks to learn informative representations across domain shift. This problem is more severe for tasks where acquiring hand labeled data is extremely hard and tedious. In this work, we focus on adapting the representations learned by segmentation networks across synthetic and real domains. Contrary to previous approaches that use a simple adversarial objective or superpixel information to aid the process, we propose an approach based on Generative Adversarial Networks (GANs) that brings the embeddings closer in the learned feature space. To showcase the generality and scalability of our approach, we show that we can achieve state of the art results on two challenging scenarios of synthetic to real domain adaptation. Additional exploratory experiments show that our approach: (1) generalizes to unseen domains and (2) results in improved alignment of source and target dis-
Design Guidelines for Mobile Augmented Reality: User Experience

Mobile augmented reality (MAR) enabled devices have the capability to present a large amount of information in real time, based on sensors that determine proximity, visual reference, maps, and detailed information on the environment. Location and proximity technologies combined with detailed mapping allow effective navigation. Visual analysis software and growing image databases enable object recognition. Advanced graphics capabilities bring sophisticated presentation of the user interface. These capabilities together allow for real-time melding of the physical and the virtual worlds and can be used for information overlay of the user’s environment for various purposes such as entertainment, tourist assistance, navigation assistance, and education  . In designing for MAR applications it is very important to understand the context in which the information has to be presented. Past research on information presentation on small form factor computing has highlighted the importance of presenting the right information in the right way to effectively engage the user [ 2– 4 ] . The screen space that is available on a small form factor is limited, and having augmented information presented as an overlay poses very interesting challenges. MAR usages involve devices that are able to perceive the context of the user based on the location and other sensor based information. In their paper on “ContextAware Pervasive Systems: Architectures for a New Breed of Applications”, Loke  ,
Do we think about time in terms of space ?

The human capacity for abstract thought poses an unsolved problem for the neural and cognitive sciences. How are people able to think about things that they can never see or touch, like ideas, mathematics, or time? A potential solution has emerged independently from such diverse fields as evolutionary biology and cognitive linguistics: the mind recruits old structures for new uses. It has been proposed, for example, that sensory and motor processes underlying spatial cognition are co-opted to support our thinking about phenomena such as mathematics and time. Empirical support for this proposal has been elusive. Until recently, arguments have rested largely on patterns observed in human language. Here we present a series of psychophysical experiments that investigate mental representations in the abstract domain of time and in the perceptually richer domain of space. Results show that people rely on spatial information to estimate time, but not the other way around. These studies provide some of the first entirely nonlinguistic evidence that spatial representations subserve temporal representations, and suggest a new way to explore the perceptual foundations of abstract thought.
Phishing Websites Detection based on Phishing Characteristics in the Webpage Source Code

World Wide Web Consortium (W3C) is the international standards organization for the World Wide Web (www). It develops standards, specifications and recommendations to enhance the interoperability and maximize consensus about the content of the web and define major parts of what makes the World Wide Web work. Phishing is a type of Internet scams that seeks to get a user‟s credentials by fraud websites, such as passwords, credit card numbers, bank account details and other sensitive information. There are some characteristics in webpage source code that distinguish phishing websites from legitimate websites and violate the w3c standards, so we can detect the phishing attacks by check the webpage and search for these characteristics in the source code file if it exists or not. In this paper, we propose a phishing detection approach based on checking the webpage source code, we extract some phishing characteristics out of the W3C standards to evaluate the security of the websites, and check each character in the webpage source code, if we find a phishing character, we will decrease from the initial secure weight. Finally we calculate the security percentage based on the final weight, the high percentage indicates secure website and others indicates the website is most likely to be a phishing website. We check two webpage source codes for legitimate and phishing websites and compare the security percentages between them, we find the phishing website is less security percentage than the legitimate website; our approach can detect the phishing website based on checking phishing characteristics in the webpage source code.