nils-herrmann/clean_scirepeval huggingface dataset:
ADVANCES IN IMAGE PROCESSING FOR DETECTION OF PLANT DISEASES

The studies of plant trait/disease refer to the studies of visually observable patterns of a particular plant. Nowadays crops face many traits/diseases. Damage of the insect is one of the major trait/disease. Insecticides are not always proved efficient because insecticides may be toxic to some kind of birds. It also damages natural animal food chains. A common practice for plant scientists is to estimate the damage of plant (leaf, stem) because of disease by an eye on a scale based on percentage of affected area. It results in subjectivity and low throughput. This paper provides a advances in various methods used to study plant diseases/traits using image processing. The methods studied are for increasing throughput & reducing subjectiveness arising from human experts in detecting the plant diseases.
Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling

Semantic role labeling (SRL) is the task of identifying the predicate-argument structure of a sentence. It is typically regarded as an important step in the standard natural language processing pipeline, providing information to downstream tasks such as information extraction and question answering. As the semantic representations are closely related to syntactic ones, we exploit syntactic information in our model. We propose a version of graph convolutional networks (GCNs), a recent class of multilayer neural networks operating on graphs, suited to modeling syntactic dependency graphs. GCNs over syntactic dependency trees are used as sentence encoders, producing latent feature representations of words in a sentence and capturing information relevant to predicting the semantic representations. We observe that GCN layers are complementary to LSTM ones: when we stack both GCN and LSTM layers, we obtain a substantial improvement over an already stateof-the-art LSTM SRL model, resulting in the best reported scores on the standard benchmark (CoNLL-2009) both for Chinese and English.
Classification and Regression Trees

Bellows TS and Fisher TW (eds.) (1999) Handbook of Biological Control: Principles and Applications of Biological Control.  Clausen CP (ed.) (1978) Agricultural Research Service: Handbook No.  Introduced Parasites and Predators of Arthropod Pests and Weeds: A World Review. Washington,  P and Rosen D (1991) Biological Control by Natural Enemies.  (2000) Nontarget Effects of Biological Control. Boston, MA: Kluwer Academic. Jervis M and Kidd N (eds.) (1996) Insect Natural Enemies: Practical Approaches to their Study and Evaluation.  (1998) Biological Control of Weeds, a World Catalogue of Agents and their Target Weeds, 4th edn.  Driesche J and Van Driesche RG (2000)  Driesche RG, Hoddle M, and Center T (2008) Control of Pests and Weeds by Natural Enemies, an Introduction to Biological Control.
BUILDING INFORMATION MODELLING FOR CULTURAL HERITAGE : A REVIEW

We discuss the evolution and state-of-the-art of the use of Building Information Modelling (BIM) in the field of culture heritage documentation. BIM is a hot theme involving different characteristics including principles, technology, even privacy rights for the cultural heritage objects. Modern documentation needs identified the potential of BIM in the recent years. Many architects, archaeologists, conservationists, engineers regard BIM as a disruptive force, changing the way professionals can document and manage a cultural heritage structure. The latest years, there are many developments in the BIM field while the developed technology and methods challenged the cultural heritage community in the documentation framework. In this review article, following a brief historic background for the BIM, we review the recent developments focusing in the cultural heritage documentation perspective.
K-Lines: A theory of Memory

Most theories of memory suggest that when we learn or Jtletriori~e something, some "representation" of that something is cons~ructedt stored and later retrieved. This raises questions like: How is information represented? How is it stored "1 ltow is it retrieved? Then, how is it used? This paper tries to deal with all these at once. When. you gpt an idea and want to "remember" it, you create a "K-line" for it. When later activated, the K-line induces a partial mental state resembling the one that created it. A "partial mental state" is a subset of those mental ilI;!Jnch~s o}Jcrating at one mo#tent. This view leads to many ideas about the development, structure and physiology of Memory, and about how to implement· frame-like representations in a distributed processor.  This report describes research done at. the Artificial Int~nigenc~ Laboratoryof the Massachusetts Institute of Technology, Support for .. the laboratory's artificial intelligence research is provided in part by the . OfficEiofNaval Research under Office of Naval Research contract NOOO14-79C-0260, ~MASSA(,fWSl:ns 1"'5 . I . .'. I~ rm,IE OF. TECmSoWGY ~919 K-LINES: A THEOllY OF MEMORY
"Why Should I Trust Interactive Learners?" Explaining Interactive Queries of Classifiers to Users

Although interactive learning puts the user into the loop, the learner remains mostly a black box for the user. Understanding the reasons behind queries and predictions is important when assessing how the learner works and, in turn, trust. Consequently, we propose the novel framework of explanatory interactive learning: in each step, the learner explains its interactive query to the user, and she queries of any active classifier for visualizing explanations of the corresponding predictions. We demonstrate that this can boost the predictive and explanatory powers of and the trust into the learned model, using text (e.g. SVMs) and image classification (e.g. neural networks) experiments as well as a user study.
Cognitive and affective development in adolescence

Questions about the nature of normative and atypical development in adolescence have taken on special significance in the last few years, as scientists have begun to recast old portraits of adolescent behavior in the light of new knowledge about brain development. Adolescence is often a period of especially heightened vulnerability as a consequence of potential disjunctions between developing brain, behavioral and cognitive systems that mature along different timetables and under the control of both common and independent biological processes. Taken together, these developments reinforce the emerging understanding of adolescence as a critical or sensitive period for a reorganization of regulatory systems, a reorganization that is fraught with both risks and opportunities.
Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation

Event extraction is of practical utility in natural language processing. In the real world, it is a common phenomenon that multiple events existing in the same sentence, where extracting them are more difficult than extracting a single event. Previous works on modeling the associations between events by sequential modeling methods suffer a lot from the low efficiency in capturing very long-range dependencies. In this paper, we propose a novel Jointly Multiple Events Extraction (JMEE) framework to jointly extract multiple event triggers and arguments by introducing syntactic shortcut arcs to enhance information flow and attention-based graph convolution networks to model graph information. The experiment results demonstrate that our proposed framework achieves competitive results compared with state-of-the-art methods.
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks

We present weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction. By reparameterizing the weights in this way we improve the conditioning of the optimization problem and we speed up convergence of stochastic gradient descent. Our reparameterization is inspired by batch normalization but does not introduce any dependencies between the examples in a minibatch. This means that our method can also be applied successfully to recurrent models such as LSTMs and to noise-sensitive applications such as deep reinforcement learning or generative models, for which batch normalization is less well suited. Although our method is much simpler, it still provides much of the speed-up of full batch normalization. In addition, the computational overhead of our method is lower, permitting more optimization steps to be taken in the same amount of time. We demonstrate the usefulness of our method on applications in supervised image recognition, generative modelling, and deep reinforcement learning.
Cognitive engagement in the problem-based learning classroom

The objective of the present study was to examine to what extent autonomy in problem-based learning (PBL) results in cognitive engagement with the topic at hand. To that end, a short self-report instrument was devised and validated. Moreover, it was examined how cognitive engagement develops as a function of the learning process and the extent to which cognitive engagement determines subsequent levels of cognitive engagement during a one-day PBL event. Data were analyzed by means of confirmatory factor analysis, repeated measures ANOVA, and path analysis. The results showed that the new measure of situational cognitive engagement is valid and reliable. Furthermore, the results revealed that students' cognitive engagement significantly increased as a function of the learning event. Implications of these findings for PBL are discussed.
Sentiment Analysis and Opinion Mining

Sentiment analysis and opinion mining is the field of study that analyzes people’s opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online. KEywORDS sentiment analysis, opinion mining, emotion, affect, evaluation, attitude, mood, social media, natural language progressing, text mining.
The Actor Search Tree Critic (ASTC) for Off-Policy POMDP Learning in Medical Decision Making

Off-policy reinforcement learning enables near-optimal policy from suboptimal experience, thereby provisions opportunity for artificial intelligence applications in healthcare. Previous works have mainly framed patient-clinician interactions as Markov decision processes, while true physiological states are not necessarily fully observable from clinical data. We capture this situation with partially observable Markov decision process, in which an agent optimises its actions in a belief represented as a distribution of patient states inferred from individual history trajectories. A Gaussian mixture model is fitted for the observed data. Moreover, we take into account the fact that nuance in pharmaceutical dosage could presumably result in significantly different effect by modelling a continuous policy through a Gaussian approximator directly in the policy space, i.e. the actor. To address the challenge of infinite number of possible belief states which renders exact value iteration intractable, we evaluate and plan for only every encountered belief, through heuristic search tree by tightly maintaining lower and upper bounds of the true value of belief. We further resort to function approximations to update value bounds estimation, i.e. the critic, so that the tree search can be improved through more compact bounds at the fringe nodes that will be back-propagated to the root. Both actor and critic parameters are learned via gradient-based approaches. Our proposed policy trained from real intensive care unit data is capable of dictating dosing on vasopressors and intravenous fluids for sepsis patients that lead to the best patient outcomes.
Coverage and Rate Analysis for Millimeter-Wave Cellular Networks

Millimeter wave (mmWave) holds promise as a carrier frequency for fifth generation cellular networks. Because mmWave signals are sensitive to blockage, prior models for cellular networks operated in the ultra high frequency (UHF) band do not apply to analyze mmWave cellular networks directly. Leveraging concepts from stochastic geometry, this paper proposes a general framework to evaluate the coverage and rate performance in mmWave cellular networks. Using a distance-dependent line-of-site (LOS) probability function, the locations of the LOS and non-LOS base stations are modeled as two independent non-homogeneous Poisson point processes, to which different path loss laws are applied. Based on the proposed framework, expressions for the signal-to-noise-and-interference ratio (SINR) and rate coverage probability are derived. The mmWave coverage and rate performance are examined as a function of the antenna geometry and base station density. The case of dense networks is further analyzed by applying a simplified system model, in which the LOS region of a user is approximated as a fixed LOS ball. The results show that dense mmWave networks can achieve comparable coverage and much higher data rates than conventional UHF cellular systems, despite the presence of blockages. The results suggest that the cell size to achieve the optimal SINR scales with the average size of the area that is LOS to a user.
Can a Chatbot Determine My Diet?: Addressing Challenges of Chatbot Application for Meal Recommendation

Poor nutrition can lead to reduced immunity, increased susceptibility to disease, impaired physical and mental development, and reduced productivity. A conversational agent can support people as a virtual coach, however building such systems still have its associated challenges and limitations. This paper describes the background and motivation for chatbot systems in the context of healthy nutrition recommendation. We discuss current challenges associated with chatbot application, we tackled technical, theoretical, behavioural, and social aspects of the challenges. We then propose a pipeline to be used as guidelines by developers to implement theoretically and technically robust chatbot systems. Health, Conversational agent, Recommender systems, HCI, Behaviour Change, Artificial intelligence
Fourth-factor authentication: somebody you know

User authentication in computing systems traditionally depends on three factors: something you have (e.g., a hardware token), something you are (e.g., a fingerprint), and something you know (e.g., a password). In this paper, we explore a fourth factor, the social network of the user, that is, somebody you know.Human authentication through mutual acquaintance is an age-old practice. In the arena of computer security, it plays roles in privilege delegation, peer-level certification, help-desk assistance, and reputation networks. As a direct means of logical authentication, though, the reliance of human being on another has little supporting scientific literature or practice.In this paper, we explore the notion of vouching, that is, peer-level, human-intermediated authentication for access control. We explore its use in emergency authentication, when primary authenticators like passwords or hardware tokens become unavailable. We describe a practical, prototype vouching system based on SecurID, a popular hardware authentication token. We address traditional, cryptographic security requirements, but also consider questions of social engineering and user behavior.
Intelligence-Driven Computer Network Defense Informed by Analysis of Adversary Campaigns and Intrusion Kill Chains

Conventional network defense tools such as intrusion detection systems and anti-virus focus on the vulnerability component of risk, and traditional incident response methodology presupposes a successful intrusion. An evolution in the goals and sophistication of computer network intrusions has rendered these approaches insufficient for certain actors. A new class of threats, appropriately dubbed the “Advanced Persistent Threat” (APT), represents well-resourced and trained adversaries that conduct multi-year intrusion campaigns targeting highly sensitive economic, proprietary, or national security information. These adversaries accomplish their goals using advanced tools and techniques designed to defeat most conventional computer network defense mechanisms. Network defense techniques which leverage knowledge about these adversaries can create an intelligence feedback loop, enabling defenders to establish a state of information superiority which decreases the adversary’s likelihood of success with each subsequent intrusion attempt. Using a kill chain model to describe phases of intrusions, mapping adversary kill chain indicators to defender courses of action, identifying patterns that link individual intrusions into broader campaigns, and understanding the iterative nature of intelligence gathering form the basis of intelligence-driven computer network defense (CND). Institutionalization of this approach reduces the likelihood of adversary success, informs network defense investment and resource prioritization, and yields relevant metrics of performance and effectiveness. The evolution of advanced persistent threats necessitates an intelligence-based model because in this model the defenders mitigate not just vulnerability, but also the threat component of risk.
Network Optimization for High Performance Cloud Computing

Once thought of as a technology restricted primarily to the scientific community, High-performance Computing (HPC) has now been established as an important value creation tool for the enterprises. Predominantly, the enterprise HPC is fueled by the needs for high-performance data analytics (HPDA) and large-scale machine learning – trades instrumental to business growth in today’s competitive markets. Cloud computing, characterized by the paradigm of on-demand network access to computational resources, has great potential of bringing HPC capabilities to a broader audience. Clouds employing traditional lossy network technologies, however, at large, have not proved to be sufficient for HPC applications. Both the traditional HPC workloads and HPDA require high predictability, large bandwidths, and low latencies, features which combined are not readily available using best-effort cloud networks. On the other hand, lossless interconnection networks commonly deployed in HPC systems, lack the flexibility needed for dynamic cloud environments. In this thesis, we identify and address research challenges that hinder the realization of an efficient HPC cloud computing platform, utilizing the InfiniBand interconnect as a demonstration technology. In particular, we address challenges related to efficient routing, load-balancing, low-overhead virtualization, performance isolation, and fast network reconfiguration, all to improve the utilization and flexibility of the underlying interconnect of an HPC cloud. In addition, we provide a framework to realize a self-adaptive network architecture for HPC clouds, offering dynamic and autonomic adaptation of the underlying interconnect according to varying traffic patterns, resource availability, workload distribution, and also in accordance with service provider defined policies. The work presented in this thesis helps bridging the performance gap between the cloud and traditional HPC infrastructures; the thesis provides practical solutions to enable an efficient, flexible, multi-tenant HPC network suitable for high-performance cloud computing.
Best practices for establishing a biobank.

A biobank may be defined as the long-term storage of biological samples for research or clinical purposes. In addition to storage facilities, a biobank may comprise a complete organization with biological samples, data, personnel, policies, and procedures for handling specimens and performing other services, such as the management of the database and the planning of scientific studies. This combination of facilities, policies, and processes may also be called a biological resource center (BRC) . Research using specimens from biobanks is regulated by European Union (EU) recommendations (Recommendations on Research on Human Biological Materials. The draft recommendation on research on human biological materials was approved by CDBI at its plenary meeting on 20 October 2005) and by voluntary best practices from the U.S. National Cancer Institute (NCI) ( http://biospecimens.cancer.gov ) and other organizations. Best practices for the management of research biobanks vary according to the institution and differing international regulations and standards. However, there are many areas of agreement that have resulted in best practices that should be followed in order to establish a biobank for the custodianship of high-quality specimens and data.
Quantifying Phishing Susceptibility for Detection and Behavior Decisions

We use signal detection theory to measure vulnerability to phishing attacks, including variation in performance across task conditions.



Phishing attacks are difficult to prevent with technology alone, as long as technology is operated by people. Those responsible for managing security risks must understand user decision making in order to create and evaluate potential solutions.



Using a scenario-based online task, we performed two experiments comparing performance on two tasks: detection, deciding whether an e-mail is phishing, and behavior, deciding what to do with an e-mail. In Experiment 1, we manipulated the order of the tasks and notification of the phishing base rate. In Experiment 2, we varied which task participants performed.



In both experiments, despite exhibiting cautious behavior, participants' limited detection ability left them vulnerable to phishing attacks. Greater sensitivity was positively correlated with confidence. Greater willingness to treat e-mails as legitimate was negatively correlated with perceived consequences from their actions and positively correlated with confidence. These patterns were robust across experimental conditions.



Phishing-related decisions are sensitive to individuals' detection ability, response bias, confidence, and perception of consequences. Performance differs when people evaluate messages or respond to them but not when their task varies in other ways.



Based on these results, potential interventions include providing users with feedback on their abilities and information about the consequences of phishing, perhaps targeting those with the worst performance. Signal detection methods offer system operators quantitative assessments of the impacts of interventions and their residual vulnerability.