nils-herrmann/clean_scirepeval huggingface dataset:
Psychological Stress and Welfare in Fish

Galhardo L, Oliveira RF. Psychological Stress and Welfare  The ability to respond to stress is vital to the survival of any living organism, though sustained reactions can become detrimental to the health and welfare of animals. Stress responses of vertebrates are known through several studies in their physiological, behavioural and psychological components, under acute and chronic contexts. In fish, the physiological and behavioural aspects of stress are considerably well known phenomena and show striking similarities to those of other vertebrates. However, the psychological component is not well known. Some authors deny mental experiences to fish on the basis of their lack of neocortex. Nevertheless, recent studies have shown neuroendocrine, cognitive and emotional processes in fish that are not only equivalent to other vertebrates, but also allow inferring some forms of mental representation. The integration of psychological elements in fish stress physiology is insufficiently studied, but, as discussed in this article, there is already indirect evidence to admit that some form of stimuli appraisal can take place in fish. This fact has profound implications on the regulation of the stress response, as well as on fish welfare and its management.
Analytics in the Pursuit of Knowledge: Adapting the Knowledge Pyramid

Advances in storage leading to the Internet of Things (IOT) and Big Data has exponentially increased the Data aspect of the traditional Knowledge Pyramid – Data-Information-Knowledge-Wisdom (DIKW). This paper presents an adaptation of the Knowledge Pyramid as an Analytics Pyramid in which Time is posited to represent Wisdom as the pinnacle achievement when pursuing knowledge. Analogies of the DIKW are presented from the Analytics Pyramid as Description-Aggregation-Modeling-Time. Implementing the premise of the Analytics Pyramid focuses on an interative/repetitive movement of both individuals and organizations through all DescriptionAggregation-Modeling-Time stages in order to build and obtain the Wisdom pursued in the traditional Knowledge Pyramid. This model reinforces organizational learning and the importance of adaptability when pursuing knowledge. In addition, the wisdom gained from analytics is only recognized when monitored business processes are longitudinal in nature. Organizational analytics must rely on the recognition of a changing environment (Time) in order to adapt.
The relation of strength of stimulus to rapidity of habit-formation in the kitten.

In connection with a study of various aspects of the modifiability of behavior in the dancing mouse a need for definite knowledge concerning the relation of strength of stimulus to rate of learning arose. It was for the purpose of obtaining this knowledge that we planned and executed the experiments which are now to be described. Our work was greatly facilitated by the advice and assistance of Doctor E. G. MARTIN, Professor G. W. PIERCE, and Professor A. E. KENNELLY, and we desire to express here both our indebtedness and our thanks for their generous services.
Particle swarm optimization in electromagnetics

The particle swarm optimization (PSO), new to the electromagnetics community, is a robust stochastic evolutionary computation technique based on the movement and intelligence of swarms. This paper introduces a conceptual overview and detailed explanation of the PSO algorithm, as well as how it can be used for electromagnetic optimizations. This paper also presents several results illustrating the swarm behavior in a PSO algorithm developed by the authors at UCLA specifically for engineering optimizations (UCLA-PSO). Also discussed is recent progress in the development of the PSO and the special considerations needed for engineering implementation including suggestions for the selection of parameter values. Additionally, a study of boundary conditions is presented indicating the invisible wall technique outperforms absorbing and reflecting wall techniques. These concepts are then integrated into a representative example of optimization of a profiled corrugated horn antenna.
The role of the gut microbiome in systemic inflammatory disease.

The role of the gut microbiome in models of inflammatory and autoimmune disease is now well characterized. Renewed interest in the human microbiome and its metabolites, as well as notable advances in host mucosal immunology, has opened multiple avenues of research to potentially modulate inflammatory responses. The complexity and interdependence of these diet-microbe-metabolite-host interactions are rapidly being unraveled. Importantly, most of the progress in the field comes from new knowledge about the functional properties of these microorganisms in physiology and their effect in mucosal immunity and distal inflammation. This review summarizes the preclinical and clinical evidence on how dietary, probiotic, prebiotic, and microbiome based therapeutics affect our understanding of wellness and disease, particularly in autoimmunity.
Poisoning Attacks against Support Vector Machines

We investigate a family of poisoning attacks against Support Vector Machines (SVM). Such attacks inject specially crafted training data that increases the SVM’s test error. Central to the motivation for these attacks is the fact that most learning algorithms assume that their training data comes from a natural or well-behaved distribution. However, this assumption does not generally hold in security-sensitive settings. As we demonstrate, an intelligent adversary can, to some extent, predict the change of the SVM’s decision function due to malicious input and use this ability to construct malicious data. The proposed attack uses a gradient ascent strategy in which the gradient is computed based on properties of the SVM’s optimal solution. This method can be kernelized and enables the attack to be constructed in the input space even for non-linear kernels. We experimentally demonstrate that our gradient ascent procedure reliably identifies good local maxima of the non-convex validation error surface, which significantly increases the classifier’s test error.
Security Analysis on Cyber-physical System Using Attack Tree

Cyber-Physical System (CPS) is a system of system which integrates physical system with cyber capability in order to improve the physical performance. It is being widely used in areas closely related to national economy and people's livelihood, therefore CPS security problems have drawn a global attention and an appropriate risk assessment for CPS is in urgent need. Existing risk assessment for CPS always focuses on the reliability assessment, using Probability Risk Assessment (PRA). In this way, the assessment of physical part and cyber part is isolated as PRA is difficult to quantify the risks from the cyber world. Methodologies should be developed to assess the both parts as a whole system, considering this integrated system has a high coupling between the physical layer and cyber layer. In this paper, a risk assessment idea for CPS with the use of attack tree is proposed. Firstly, it presents a detailed description about the threat and vulnerability attributes of each leaf in an attack tree and tells how to assign value to its threat and vulnerability vector. Then this paper focuses on calculating the threat and vulnerability vector of an attack path with the use of the leaf vector values. Finally, damage is taken into account and an idea to calculate the risk value of the whole attack path is given.
Loss Aversion in Riskless Choice : A Reference-Dependent Model

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Review of Fish Swimming Modes for Aquatic Locomotion Submitted to the IEEE Journal of Oceanic Engineering December 1998

Several physico-mechanical designs evolved in fish are currently inspiring robotic devices for propulsion and manoeuvring purposes in underwater vehicles. Considering the potential benefits involved, this paper presents an overview of the swimming mechanisms employed by fish. The motivation is to provide a relevant and useful introduction to the existing literature for engineers with an interest in the emerging area of aquatic biomechanisms. The fish swimming types are presented following the well-established classification scheme and nomenclature originally proposed by Breder. Fish swim either by Body and/or Caudal Fin (BCF) movements or using Median and/or Paired Fin (MPF) propulsion. The latter is generally employed at slow speeds, offering greater manoeuvrability and better propulsive efficiency, while BCF movements can achieve greater thrust and accelerations. For both BCF and MPF locomotion specific swimming modes are identified, based on the propulsor and the type of movements (oscillatory or undulatory) employed for thrust generation. Along with general descriptions and kinematic data, the analytical approaches developed to study each swimming mode are also introduced. Particular reference is made to lunate tail propulsion, undulating fins and labriform (oscillatory pectoral fin) swimming mechanisms, identified as having the greatest potential for exploitation in artificial systems. Index Terms marine animals, hydrodynamics, underwater vehicle propulsion, mobile robots, kinematics * Submitted as a regular paper to the IEEE Journal of Oceanic Engineering, March 1998. † Ocean Systems Laboratory, Dept. of Computing & Electrical Engineering, Heriot-Watt University, Edinburgh EH14 4AS, Scotland, U.K. Tel: +(44) (0) 131 4513350.  (0) 131 4513327.  dml@cee.hw.ac.uk ‡ Dept. of Mechanical & Chemical Engineering, Heriot-Watt University, Edinburgh EH14 4AS, Scotland,U.K. Review of Fish Swimming Modes for Aquatic Locomotion -2
Garlic: a review of potential therapeutic effects

Throughout history, many different cultures have recognized the potential use of garlic for prevention and treatment of different diseases. Recent studies support the effects of garlic and its extracts in a wide range of applications. These studies raised the possibility of revival of garlic therapeutic values in different diseases. Different compounds in garlic are thought to reduce the risk for cardiovascular diseases, have anti-tumor and anti-microbial effects, and show benefit on high blood glucose concentration. However, the exact mechanism of all ingredients and their long-term effects are not fully understood. Further studies are needed to elucidate the pathophysiological mechanisms of action of garlic as well as its efficacy and safety in treatment of various diseases.
Review of glucosamine-containing oral joint supplements: are they effective in the horse?

While glucosamine supplementation is very common and a multitude of commercial products are available, there is currently limited information available to assist the equine practitioner in deciding when and how to use these products. Low bioavailability of orally administered glucosamine, poor product quality, low recommended doses, and a lack of scientific evidence showing efficacy of popular oral joint supplements are major concerns. Authors’ addresses: Rolling Thunder Veterinary Services, 225 Roxbury Road, Garden City, NY 11530 (Oke); Ontario Veterinary College, Department of Clinical Studies, University of Guelph, Guelph, Ontario, Canada N1G 2W1 (Weese); e-mail: rollingthunder@optonline.net (Oke).
Multi-scale multi-class conditional generative adversarial network for handwritten character generation

Handwritten character generation is a popular research topic with various applications. Various methods have been proposed in the literatures which are based on methods such as pattern recognition, machine learning, deep learning or others. However, seldom method could generate realistic and natural handwritten characters with a built-in determination mechanism to enhance the quality of generated image and make the observers unable to tell whether they are written by a person. To address these problems, in this paper, we proposed a novel generative adversarial network, multi-scale multi-class generative adversarial network (MSMC-CGAN). It is a neural network based on conditional generative adversarial network (CGAN), and it is designed for realistic multi-scale character generation. MSMC-CGAN combines the global and partial image information as condition, and the condition can also help us to generate multi-class handwritten characters. Our model is designed with unique neural network structures, image features and training method. To validate the performance of our model, we utilized it in Chinese handwriting generation, and an evaluation method called mean opinion score (MOS) was used. The MOS results show that MSMC-CGAN achieved good performance.
Die Evaluation generischer Einbettung automatisierter Programmbewertung am Beispiel von Moodle und aSQLg

Für die automatisierte Bewertung von Lösungen zu Programmieraufgaben wurde mittlerweile eine Vielzah an Grader-Programmen zu unterschiedlichen Programmiersprachen entwickelt. U m Lernenden wie Lehrenden Zugang zur möglichst vielen Gradern über das gewohn te LMS zu ermöglichen wird das Konzept einer generischen Web-Serviceschni ttstelle (Grappa) vorgestellt, welches im Kontext einer Lehrveranstaltung evaluier t wurde.
Improving Ship Stability in Automated Stowage Planning for Large Containerships

Stowage planning for container vessels concerns the core competence of the shipping lines. As such, automated stowage planning has attracted much research in the past two decades, but with few documented successes. In an ongoing project, we are developing a prototype stowage planning system aiming for large containerships. The system consists of three modules: the stowage plan generator, the stability adjustment module, and the optimization engine. This paper mainly focuses on the stability adjustment module. The objective of the stability adjustment module is to check the global ship stability of the stowage plan produced by the stowage plan generator and resolve the stability issues by applying a heuristic algorithm to search for alternative feasible locations for containers that violate some of the stability criteria. We demonstrate that the procedure proposed is capable of solving the stability problems for a large containership with more than 5000 TEUs.  Automation, Stowage Planning, Local Search, Heuristic algorithm, Stability Optimization
Inverse Fractional Knapsack Problem with Profits and Costs Modification

We address in this paper the problem of modifying both profits and costs of a fractional knapsack problem optimally such that a prespectified solution becomes an optimal solution with prespect to new parameters. This problem is called the inverse fractional knapsack problem. Concerning the l1-norm, we first prove that the problem is NP -hard. The problem can be however solved in quadratic time if we only modify profit parameters. Additionally, we develop a quadratic-time algorithm that solves the inverse fractional knapsack problem under l∞-norm.
Minimum clinically important difference for the COPD Assessment Test: a prospective analysis.

The COPD Assessment Test (CAT) is responsive to change in patients with chronic obstructive pulmonary disease (COPD). However, the minimum clinically important difference (MCID) has not been established. We aimed to identify the MCID for the CAT using anchor-based and distribution-based methods.



We did three studies at two centres in London (UK) between April 1, 2010, and Dec 31, 2012. Study 1 assessed CAT score before and after 8 weeks of outpatient pulmonary rehabilitation in patients with COPD who were able to walk 5 m, and had no contraindication to exercise. Study 2 assessed change in CAT score at discharge and after 3 months in patients admitted to hospital for more than 24 h for acute exacerbation of COPD. Study 3 assessed change in CAT score at baseline and at 12 months in stable outpatients with COPD. We focused on identifying the minimum clinically important improvement in CAT score. The St George's Respiratory Questionnaire (SGRQ) and Chronic Respiratory Questionnaire (CRQ) were measured concurrently as anchors. We used receiver operating characteristic curves, linear regression, and distribution-based methods (half SD, SE of measurement) to estimate the MCID for the CAT; we included only patients with paired CAT scores in the analysis.



In Study 1, 565 of 675 (84%) patients had paired CAT scores. The mean change in CAT score with pulmonary rehabilitation was -2·5 (95% CI -3·0 to -1·9), which correlated significantly with change in SGRQ score (r=0·32; p<0·0001) and CRQ score (r=-0·46; p<0·0001). In Study 2, of 200 patients recruited, 147 (74%) had paired CAT scores. Mean change in CAT score from hospital discharge to 3 months after discharge was -3·0 (95% CI -4·4 to -1·6), which correlated with change in SGRQ score (r=0·47; p<0·0001). In Study 3, of 200 patients recruited, 164 (82%) had paired CAT scores. Although no significant change in CAT score was identified after 12 months (mean 0·6, 95% CI -0·4 to 1·5), change in CAT score correlated significantly with change in SGRQ score (r=0·36; p<0·0001). Linear regression estimated the minimum clinically important improvement for the CAT to range between -1·2 and -2·8 with receiver operating characteristic curves consistently identifying -2 as the MCID. Distribution-based estimates for the MCID ranged from -3·3 to -3·8.



The most reliable estimate of the minimum important difference of the CAT is 2 points. This estimate could be useful in the clinical interpretation of CAT data, particularly in response to intervention studies.
Deep Models of Interactions Across Sets

We use deep learning to model interactions across two or more sets of objects, such as user–movie ratings, protein–drug bindings, or ternary useritem-tag interactions. The canonical representation of such interactions is a matrix (or a higherdimensional tensor) with an exchangeability property: the encoding’s meaning is not changed by permuting rows or columns. We argue that models should hence be Permutation Equivariant (PE): constrained to make the same predictions across such permutations. We present a parameter-sharing scheme and prove that it could not be made any more expressive without violating PE. This scheme yields three benefits. First, we demonstrate state-of-the-art performance on multiple matrix completion benchmarks. Second, our models require a number of parameters independent of the numbers of objects, and thus scale well to large datasets. Third, models can be queried about new objects that were not available at training time, but for which interactions have since been observed. In experiments, our models achieved surprisingly good generalization performance on this matrix extrapolation task, both within domains (e.g., new users and new movies drawn from the same distribution used for training) and even across domains (e.g., predicting music ratings after training on movies).
Deep Learning for Textual Entailment Recognition

In this paper we propose a novel two-step procedure to recognize textual entailment. Firstly, we build a joint Restricted Boltzmann Machines (RBM) layer to learn the joint representation of the text-hypothesis pairs. Then the reconstruction error is calculated by comparing the original representation with reconstructed representation derived from the joint layer for each pair to recognize textual entailment. The joint RBM training data is automatically generated from a large news corpus. Experiment results show the contribution of the idea to the performance on textual entailment.
Redactable Blockchain – or – Rewriting History in Bitcoin and Friends

We put forward a new framework that makes it possible to re-write or compress the content of any number of blocks in decentralized services exploiting the blockchain technology. As we argue, there are several reasons to prefer an editable blockchain, spanning from the necessity to remove inappropriate content and the possibility to support applications requiring re-writable storage, to "the right to be forgotten." Our approach generically leverages so-called chameleon hash functions (Krawczyk and Rabin, NDSS '00), which allow determining hash collisions efficiently, given a secret trapdoor information. We detail how to integrate a chameleon hash function in virtually any blockchain-based technology, for both cases where the power of redacting the blockchain content is in the hands of a single trusted entity and where such a capability is distributed among several distrustful parties (as is the case with Bitcoin). We also report on a proof-of-concept implementation of a redactable blockchain, building on top of Nakamoto's Bitcoin core. The prototype only requires minimal changes to the way current client software interprets the information stored in the blockchain and to the current blockchain, block, or transaction structures. Moreover, our experiments show that the overhead imposed by a redactable blockchain is small compared to the case of an immutable one.
edge2vec: Learning Node Representation Using Edge Semantics

Representation learning for networks provides the new way to mine graphs, unfortunately most current researches are limited to homogeneous networks. In reality, most of the graphs we are facing are heterogeneous. Therefore, to be able to represent nodes by considering the semantics of edges and nodes is critical for us to solve real world problems. In this paper, we develop the edge2vec model, which can represent nodes by considering edge semantics. An edge-type transition matrix is initiated from the Expectation-Maximization (EM) framework , and a stochastic gradient descent (SGD) model is leveraged to learn node embedding in a heterogeneous graph incorporating the learned transition matrix afterwards. Edge2vec is verified and evaluated on three medical domain tasks, which are medical entity classification, compound-gene binding prediction, and medical information retrieval. Experimental result shows that by considering edge-types into node embedding learning in heterogeneous graph, edge2vec significantly outperforms the other state-of-art models on all three tasks.
HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment

We introduce HyperLex—a data set and evaluation resource that quantifies the extent of the semantic category membership, that is, type-of relation, also known as hyponymy–hypernymy or lexical entailment (LE) relation between 2,616 concept pairs. Cognitive psychology research has established that typicality and category/class membership are computed in human semantic memory as a gradual rather than binary relation. Nevertheless, most NLP research and existing large-scale inventories of concept category membership (WordNet, DBPedia, etc.) treat category membership and LE as binary. To address this, we asked hundreds of native English speakers to indicate typicality and strength of category membership between a diverse range of concept pairs on a crowdsourcing platform. Our results confirm that category membership and LE are indeed more gradual than binary. We then compare these human judgments with the predictions of automatic systems, which reveals a huge gap between human performance and state-of-the-art LE, distributional and representation learning models, and substantial differences between the models themselves. We discuss a pathway for improving semantic models to overcome this discrepancy, and indicate future application areas for improved graded LE systems.