nils-herrmann/clean_scirepeval huggingface dataset:
Bayesian Versus Orthodox Statistics: Which Side Are You On?

Researchers are often confused about what can be inferred from significance tests. One problem occurs when people apply Bayesian intuitions to significance testing-two approaches that must be firmly separated. This article presents some common situations in which the approaches come to different conclusions; you can see where your intuitions initially lie. The situations include multiple testing, deciding when to stop running participants, and when a theory was thought of relative to finding out results. The interpretation of nonsignificant results has also been persistently problematic in a way that Bayesian inference can clarify. The Bayesian and orthodox approaches are placed in the context of different notions of rationality, and I accuse myself and others as having been irrational in the way we have been using statistics on a key notion of rationality. The reader is shown how to apply Bayesian inference in practice, using free online software, to allow more coherent inferences from data.
3D positioning information on augmented identification for indoor localization

Augmented and virtual reality have one big thing in common. They both have the remarkable ability to alter our perception of the world. Virtual reality is able to transpose the user. Augmented reality however, takes our current reality and adds something to it. Hence, the 3D positioning information in Augmented reality system is very important. In our study, the authors prosed the three parts of newly technologies, including the weighted ratio method, the advanced geometric model and the Kalman learning system to improve the accuracy of 3D indoor positioning system, the results show that our proposed method not only can increase the positioning accuracy, but also save the large computation time than the comparing method in Chen's method.
On the Learnability of Fully-Connected Neural Networks

Despite the empirical success of deep neural networks, there is limited theoretical understanding of the learnability of these models with respect to polynomial-time algorithms. In this paper, we characterize the learnability of fullyconnected neural networks via both positive and negative results. We focus on `1-regularized networks, where the `1-norm of the incoming weights of every neuron is assumed to be bounded by a constant B > 0. Our first result shows that such networks are properly learnable in poly(n, d, exp(1/ )) time, where n and d are the sample size and the input dimension, and > 0 is the gap to optimality. The bound is achieved by repeatedly sampling over a low-dimensional manifold so as to ensure approximate optimality, but avoids the exp(d) cost of exhaustively searching over the parameter space. We also establish a hardness result showing that the exponential dependence on 1/ is unavoidable unless RP = NP. Our second result shows that the exponential dependence on 1/ can be avoided by exploiting the underlying structure of the data distribution. In particular, if the positive and negative examples can be separated with margin γ > 0 by an unknown neural network, then the network can be learned in poly(n, d, 1/ ) time. The bound is achieved by an ensemble method which uses the first algorithm as a weak learner. We further show that the separability assumption can be weakened to tolerate noisy labels. Finally, we show that the exponential dependence on 1/γ is unimprovable under a certain cryptographic assumption.  2017, Fort Lauderdale, Florida, USA.  W&CP volume 54.
Fully Convolutional Instance-Aware Semantic Segmentation

We present the first fully convolutional end-to-end solution for instance-aware semantic segmentation task. It inherits all the merits of FCNs for semantic segmentation [29] and instance mask proposal [5]. It performs instance mask prediction and classification jointly. The underlying convolutional representation is fully shared between the two sub-tasks, as well as between all regions of interest. The network architecture is highly integrated and efficient. It achieves state-of-the-art performance in both accuracy and efficiency. It wins the COCO 2016 segmentation competition by a large margin. Code would be released at https://github.com/daijifeng001/TA-FCN.
Realtime library for procedural generation and rendering of terrains

Techniques for procedural generation of the graphics content have seen widespread use in multimedia over the past thirty years. It is still an active area of research with many applications in 3D modeling software, video games, and films. This thesis focuses on algorithmic generation of virtual terrains in real-time and their real-time visualization. We provide an overview of available approaches and present an extendable library for procedural terrain synthesis.
Mining the Relationship between Emoji Usage Patterns and Personality

Emojis have been widely used in textual communications as a new way to convey nonverbal cues. An interesting observation is the various emoji usage patterns among different users. In this paper, we investigate the correlation between user personality traits and their emoji usage patterns, particularly on overall amounts and specific preferences. To achieve this goal, we build a large Twitter dataset which includes 352,245 users and over 1.13 billion tweets associated with calculated personality traits and emoji usage patterns. Our correlation and emoji prediction results provide insights into the power of diverse personalities that lead to varies emoji usage patterns as well as its potential in emoji recommendation
Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification

Automatic sentiment classification has been extensively studied and applied in recent years. However, sentiment is expressed differently in different domains, and annotating corpora for every possible domain of interest is impractical. We investigate domain adaptation for sentiment classifiers, focusing on online reviews for different types of products. First, we extend to sentiment classification the recently-proposed structural correspondence learning (SCL) algorithm, reducing the relative error due to adaptation between domains by an average of 30% over the original SCL algorithm and 46% over a supervised baseline. Second, we identify a measure of domain similarity that correlates well with the potential for adaptation of a classifier from one domain to another. This measure could for instance be used to select a small set of domains to annotate whose trained classifiers would transfer well to many other domains.
Network Neutrality, Broadband Discrimination

Communications regulators over the next decade will spend increasing time on conflicts between the private interests of broadband providers and the public’s interest in a competitive innovation environment centered on the Internet. As the policy questions this conflict raises are basic to communications policy, they are likely to reappear in many different forms. So far, the first major appearance has come in the ‘‘open access’’ (or ‘‘multiple access’’) debate, over the desirability of allowing vertical integration between Internet Service Providers and cable operators. Proponents of open access see it as a structural remedy to guard against an erosion of the ‘‘neutrality’’ of the network as between competing content and applications. Critics, meanwhile, have taken open-access regulation as unnecessary and likely to slow the pace of broadband deployment.
Artificial Intelligence and Natural Language

The goal of the current work is to evaluate semantic feature aggregation techniques in a task of gender classification of public social media texts in Russian. We collect Facebook posts of Russian-speaking users and apply them as a dataset for two topic modelling techniques and a distributional clustering approach. The output of the algorithms is applied as a feature aggregation method in a task of gender classification based on a smaller Facebook sample. The classification performance of the best model is favorably compared against the lemmas baseline and the state-of-the-art results reported for a different genre or language. The resulting successful features are exemplified, and the difference between the three techniques in terms of classification performance and feature contents are discussed, with the best technique clearly outperforming the others.
An Ontology Based Text Analytics on Social Media

The amount of digital information that is created and used is progressively rising along with the growth of sophisticated hardware and software. In addition, real-world data come in a diversity of forms and can be tremendously bulky. This has augmented the need for powerful algorithms that can deduce and dig out appealing facts and useful information from these data. Text Mining (TM), which is a very complex process; has been successfully used for this purpose. Text mining alternately referred to as text data mining, more or less equivalent to text analytics, can be defined as the process of extracting high-quality information from text. Text mining involves the process of structuring the input data, deriving patterns within the structured data and lastly interpretation and revelation of the output. This paper provides outline on text analytics and social media analytics. At the end, this paper presents our proposed work based on ontology framework to cope up with excessive social media textual data.
Joint Matrix-Tensor Factorization for Knowledge Base Inference

While several matrix factorization (MF) and tensor factorization (TF) models have been proposed for knowledge base (KB) inference, they have rarely been compared across various datasets. Is there a single model that performs well across datasets? If not, what characteristics of a dataset determine the performance of MF and TF models? Is there a joint TF+MF model that performs robustly on all datasets? We perform an extensive evaluation to compare popular KB inference models across popular datasets in the literature. In addition to answering the questions above, we remove a limitation in the standard evaluation protocol for MF models, propose an extension to MF models so that they can better handle out-ofvocabulary (OOV) entity pairs, and develop a novel combination of TF and MF models. We also analyze and explain the results based on models and dataset characteristics. Our best model is robust, and obtains strong results across all datasets.
Security Attack on CloudBI: Practical privacy-preserving outsourcing of biometric identification in the cloud

Recently,  presented a security attack on the privacy-preserving outsourcing scheme for biometric identification proposed in . In , the author claims that the scheme CloudBI-II proposed in  can be broken under the collusion case. That is, when the cloud server acts as a user to submit a number of identification requests, CloudBI-II is no longer secure. In this technical report, we will explicitly show that the attack method proposed in  doesn’t work in fact.
The ozone paradox: ozone is a strong oxidant as well as a medical drug.

After five decades characterized by empiricism and several pitfalls, some of the basic mechanisms of action of ozone in pulmonary toxicology and in medicine have been clarified. The present knowledge allows to understand the prolonged inhalation of ozone can be very deleterious first for the lungs and successively for the whole organism. On the other hand, a small ozone dose well calibrated against the potent antioxidant capacity of blood can trigger several useful biochemical mechanisms and reactivate the antioxidant system. In detail, firstly ex vivo and second during the infusion of ozonated blood into the donor, the ozone therapy approach involves blood cells and the endothelium, which by transferring the ozone messengers to billions of cells will generate a therapeutic effect. Thus, in spite of a common prejudice, single ozone doses can be therapeutically used in selected human diseases without any toxicity or side effects. Moreover, the versatility and amplitude of beneficial effect of ozone applications have become evident in orthopedics, cutaneous, and mucosal infections as well as in dentistry.
Ransomware attacks: detection, prevention and cure

The notion of ransomware has actually been around for quite some time. In 1989, Dr Joseph Popp distributed a trojan called PC Cyborg in which malware would hide all folders and encrypt files on the PC’s C: drive. A script delivered a ransom message demanding that $189 be directed to the PC Cyborg Corporation. The afflicted PC wouldn’t function until the ransom was paid and the malware’s actions were reversed. Since then, numerous enhancements to this type of scheme have been made, especially in the area of stronger file encryption. Now, it’s virtually impossible for victims to decrypt their own files.
Design and analysis of underactuated robotic gripper with adaptive fingers for objects grasping tasks

The ability of the underactuated adaptive gripper is to envelop the objects according to its geometric shapes. It is known that robotic grippers are a nonlinear system, and it is difficult to set up an appropriate mathematical model, so it is hard to execute a precise control. In this paper, an underactuated gripper with two adaptive fingers is presented, and a Copley servo controller (Accelnet Micro Panel, Copley Corp) is applied to the gripper for controlling its input displacement. In order to adaption and simplification of the gripper, an adaptive finger mechanism is proposed in this paper. An underactuated gripper with two adaptive fingers with the underactuated structure is designed. There are three degrees of freedom in each finger with only one motor to drive it. The performance analysis and grasping experiments present that the proposed underactuated gripper has the capacity of enveloping objects with different shapes and sizes, and it can achieve the power grasp and precision grasp like a human hand.
Enhancing Informative Frame Filtering by Water and Bubble Detection in Colonoscopy Videos

Colonoscopy has contributed to a marked decline in the number of colorectal cancer related deaths. However, recent data suggest that there is a significant (4-12%) miss-rate for the detection of even large polyps and cancers. To address this, we have been investigating an ‘automated feedback system’ which informs the endoscopist of possible sub-optimal inspection during colonoscopy. A fundamental step of this system is to distinguish non-informative frames from informative ones. Existing methods for this cannot classify water/ bubble frames as non-informative even though they do not carry any useful visual information of the colon mucosa. In this paper, we propose a novel texture feature based on accumulation of pixel differences, which can detect water and bubble frames with very high accuracy with significantly less processing time. The experimental results show the proposed feature can achieve more than 93% overall accuracy in almost half of the processing time the existing methods take.
Multi-View Canonical Correlation Analysis

Canonical correlation analysis (CCA) is a method for finding linear relations between two multidimensional random variables. This paper presents a generalization of the method to more than two variables. The approach is highly scalable, since it scales linearly with respect to the number of training examples and number of views (standard CCA implementations yield cubic complexity). The method is also extended to handle nonlinear relations via kernel trick (this increases the complexity to quadratic complexity). The scalability is demonstrated on a large scale cross-lingual information retrieval task.
Compassion interventions: The programmes, the evidence, and implications for research and practice.

Over the last 10-15 years, there has been a substantive increase in compassion-based interventions aiming to improve psychological functioning and well-being.



This study provides an overview and synthesis of the currently available compassion-based interventions. What do these programmes looks like, what are their aims, and what is the state of evidence underpinning each of them?



This overview has found at least eight different compassion-based interventions (e.g., Compassion-Focused Therapy, Mindful Self-Compassion, Cultivating Compassion Training, Cognitively Based Compassion Training), with six having been evaluated in randomized controlled trials, and with a recent meta-analysis finding that compassion-based interventions produce moderate effect sizes for suffering and improved life satisfaction.



Although further research is warranted, the current state of evidence highlights the potential benefits of compassion-based interventions on a range of outcomes that clinicians can use in clinical practice with clients.



There are eight established compassion intervention programmes with six having RCT evidence. The most evaluated intervention to date is compassion-focused therapy. Further RCTs are needed in clinical populations for all compassion interventions. Ten recommendations are provided to improve the evidence-base of compassion interventions.
Causal Effects in Mediation Modeling : An Introduction With Applications to Latent Variables

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Management of hepatocellular carcinoma: An update

The recommendations provided in this document provide a data-supported approach to the diagnosis, staging and treatment of patients diagnosed with hepatocellular carcinoma (HCC). They are based on the following: (a) formal review and analysis of the recently-published world literature on the topic (Medline search through early 2010); (b) American College of Physicians Manual for Assessing Health Practices and Designing Practice Guidelines; (c) guideline policies, including the AASLD Policy on the Development and Use of Practice Guidelines and the American Gastroenterology Association Policy Statement on Guidelines; (d) the experience of the authors. These recommendations suggest preferred approaches to the diagnostic, therapeutic, and preventive aspects of care. In an attempt to characterize the quality of evidence supporting recommendations, the Practice Guidelines Committee of the American Association for Study of Liver Disease (AASLD) requires a category to be assigned and reported with each recommendation (Table 1). These recommendations are fully endorsed by the American Association for the Study of Liver Diseases.
UX Design Innovation: Challenges for Working with Machine Learning as a Design Material

Machine learning (ML) is now a fairly established technology, and user experience (UX) designers appear regularly to integrate ML services in new apps, devices, and systems. Interestingly, this technology has not experienced a wealth of design innovation that other technologies have, and this might be because it is a new and difficult design material. To better understand why we have witnessed little design innovation, we conducted a survey of current UX practitioners with regards to how new ML services are envisioned and developed in UX practice. Our survey probed on how ML may or may not have been a part of their UX design education, on how they work to create new things with developers, and on the challenges they have faced working with this material. We use the findings from this survey and our review of related literature to present a series of challenges for UX and interaction design research and education. Finally, we discuss areas where new research and new curriculum might help our community unlock the power of design thinking to re-imagine what ML might be and might do.
Information security culture: A Behaviour Compliance Conceptual Framework

Understanding the complex dynamic and uncertain characteristics of organisational employees who perform authorised or unauthorised information security activities is deemed to be a very important and challenging task. This paper presents a conceptual framework for classifying and organising the characteristics of organisational subjects involved in these information security practices. Our framework expands the traditional Human Behaviour and the Social Environment perspectives used in social work by identifying how knowledge, skills and individual preferences work to influence individual and group practices with respect to information security management. The classification of concepts and characteristics in the framework arises from a review of recent literature and is underpinned by theoretical models that explain these concepts and characteristics. Further, based upon an exploratory study of three case organisations in Saudi Arabia involving extensive interviews with senior managers, department managers, IT managers, information security officers, and IT staff; this article describes observed information security practices and identifies several factors which appear to be particularly important in influencing information security behaviour. These factors include values associated with national and organisational culture and how they manifest in practice, and activities related to information security management.
Graph Partition Neural Networks for Semi-Supervised Classification

We present graph partition neural networks (GPNN), an extension of graph neural networks (GNNs) able to handle extremely large graphs. GPNNs alternate between locally propagating information between nodes in small subgraphs and globally propagating information between the subgraphs. To efficiently partition graphs, we experiment with spectral partitioning and also propose a modified multi-seed flood fill for fast processing of large scale graphs. We extensively test our model on a variety of semi-supervised node classification tasks. Experimental results indicate that GPNNs are either superior or comparable to state-of-the-art methods on a wide variety of datasets for graph-based semi-supervised classification. We also show that GPNNs can achieve similar performance as standard GNNs with fewer propagation steps.