nils-herrmann/clean_scirepeval huggingface dataset:
Class Imbalance, Redux

Class imbalance (i.e., scenarios in which classes are unequally represented in the training data) occurs in many real-world learning tasks. Yet despite its practical importance, there is no established theory of class imbalance, and existing methods for handling it are therefore not well motivated. In this work, we approach the problem of imbalance from a probabilistic perspective, and from this vantage identify dataset characteristics (such as dimensionality, sparsity, etc.) that exacerbate the problem. Motivated by this theory, we advocate the approach of bagging an ensemble of classifiers induced over balanced bootstrap training samples, arguing that this strategy will often succeed where others fail. Thus in addition to providing a theoretical understanding of class imbalance, corroborated by our experiments on both simulated and real datasets, we provide practical guidance for the data mining practitioner working with imbalanced data.
Digital Cosmology ) and the Cellular Automaton : A Perfect Mathematical Deterministic Structure for Reality – as A Huge Computational Simulation

Logic and Philosophy of Science Research Group, Hokkaido University, Japan Jan 7, 2015  In this paper we provide an analysis and overview of some notable definitions, works and thoughts concerning discrete physics (digital philosophy) that mainly suggest a finite and discrete characteristic for the physical world, as well as, of the cellular automaton, which could serve as the basis of a (or the only) perfect mathematical deterministic model for the physical reality.
Current and future prospects for CRISPR-based tools in bacteria.

CRISPR-Cas systems have rapidly transitioned from intriguing prokaryotic defense systems to powerful and versatile biomolecular tools. This article reviews how these systems have been translated into technologies to manipulate bacterial genetics, physiology, and communities. Recent applications in bacteria have centered on multiplexed genome editing, programmable gene regulation, and sequence-specific antimicrobials, while future applications can build on advances in eukaryotes, the rich natural diversity of CRISPR-Cas systems, and the untapped potential of CRISPR-based DNA acquisition. Overall, these systems have formed the basis of an ever-expanding genetic toolbox and hold tremendous potential for our future understanding and engineering of the bacterial world.
Visual perception and the statistical properties of natural scenes.

The environments in which we live and the tasks we must perform to survive and reproduce have shaped the design of our perceptual systems through evolution and experience. Therefore, direct measurement of the statistical regularities in natural environments (scenes) has great potential value for advancing our understanding of visual perception. This review begins with a general discussion of the natural scene statistics approach, of the different kinds of statistics that can be measured, and of some existing measurement techniques. This is followed by a summary of the natural scene statistics measured over the past 20 years. Finally, there is a summary of the hypotheses, models, and experiments that have emerged from the analysis of natural scene statistics.
Midcheek Lift Using Facial Soft-Tissue Spaces of the Midcheek.

This article describes a preperiosteal midcheek lift technique performed by means of the midcheek soft-tissue spaces by precise release of the retaining ligaments that separate the spaces.



From November of 2009 to June of 2014, 184 patients underwent this procedure. A transcutaneous lower eyelid blepharoplasty incision was used to access the preseptal space. Medially, the orbicularis oculi origins and tear trough ligament are released sharply, connecting the dissection with the premaxillary space. More laterally, the orbicularis retaining ligament is released, connecting the dissection with the prezygomatic space. With this release, the entire midcheek can be effectively lifted. The fat pads were managed by transposition, excision, or with septal resets as indicated. Canthopexy is performed routinely to provide lower eyelid support. Superolateral traction on the orbicularis oculi elevates the entire midcheek, and this was secured to the lateral orbital rim periosteum. In patients with significant volume loss in the midcheek, structural fat grafting is performed.



All patients demonstrated a significant rejuvenation of the midcheek with elimination of the eye bags and elevation of the lid-cheek junction and the cheek prominence and improvement of the nasolabial folds. The majority of the patients (96 percent) were satisfied with the procedure. Complication rates were low. Ectropion occurred in 1 percent of patients, and lower lid retraction occurred in 1 percent of patients.



The midcheek lift by means of the facial soft-tissue spaces is safe, effective, and long lasting. As the dissection is atraumatic, recovery is quick and complications are minimized.
Stylize Aesthetic QR Code

With the continued proliferation of smart mobile devices, Quick Response (QR) code has become one of the most-used types of two-dimensional code in the world. Aiming at beautifying the visual-unpleasant appearance of QR codes, existing works have developed a series of techniques. However, these works still leave much to be desired, such as personalization, artistry, and robustness. To address these issues, in this paper, we propose a novel type of aesthetic QR codes, SEE (Stylize aEsthEtic) QR code, and a three-stage approach to automatically produce such robust style-oriented codes. Specifically, in the first stage, we propose a method to generate an optimized baseline aesthetic QR code, which reduces the visual contrast between the noise-like black/white modules and the blended image. In the second stage, to obtain art style QR code, we tailor an appropriate neural style transformation network to endow the baseline aesthetic QR code with artistic elements. In the third stage, we design an error-correction mechanism by balancing two competing terms, visual quality and readability, to ensure the performance robust. Extensive experiments demonstrate that SEE QR code has high quality in terms of both visual appearance and robustness, and also offers a greater variety of personalized choices to users.
Discrimination-aware Channel Pruning for Deep Neural Networks

Channel pruning is one of the predominant approaches for deep model compression. Existing pruning methods either train from scratch with sparsity constraints on channels, or minimize the reconstruction error between the pre-trained feature maps and the compressed ones. Both strategies suffer from some limitations: the former kind is computationally expensive and difficult to converge, whilst the latter kind optimizes the reconstruction error but ignores the discriminative power of channels. In this paper, we investigate a simple-yet-effective method called discrimination-aware channel pruning (DCP) to choose those channels that really contribute to discriminative power. To this end, we introduce additional discrimination-aware losses into the network to increase the discriminative power of intermediate layers and then select the most discriminative channels for each layer by considering the additional loss and the reconstruction error. Last, we propose a greedy algorithm to conduct channel selection and parameter optimization in an iterative way. Extensive experiments demonstrate the effectiveness of our method. For example, on ILSVRC-12, our pruned ResNet-50 with 30% reduction of channels outperforms the baseline model by 0.39% in top-1 accuracy.
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework

Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial intelligence that is able to learn and reason in the same way that humans do. We introduce β-VAE, a new state-of-the-art framework for automated discovery of interpretable factorised latent representations from raw image data in a completely unsupervised manner. Our approach is a modification of the variational autoencoder (VAE) framework. We introduce an adjustable hyperparameter β that balances latent channel capacity and independence constraints with reconstruction accuracy. We demonstrate that β-VAE with appropriately tuned β > 1 qualitatively outperforms VAE (β = 1), as well as state of the art unsupervised (InfoGAN) and semi-supervised (DC-IGN) approaches to disentangled factor learning on a variety of datasets (celebA, faces and chairs). Furthermore, we devise a protocol to quantitatively compare the degree of disentanglement learnt by different models, and show that our approach also significantly outperforms all baselines quantitatively. Unlike InfoGAN, β-VAE is stable to train, makes few assumptions about the data and relies on tuning a single hyperparameter β, which can be directly optimised through a hyperparameter search using weakly labelled data or through heuristic visual inspection for purely unsupervised data.
- Based Twitter User Geolocation Prediction

Geographical location is vital to geospatial applications like local search and event detection. In this paper, we investigate and improve on the task of text-based geolocation prediction of Twitter users. Previous studies on this topic have typically assumed that geographical references (e.g., gazetteer terms, dialectal words) in a text are indicative of its author’s location. However, these references are often buried in informal, ungrammatical, and multilingual data, and are therefore non-trivial to identify and exploit. We present an integrated geolocation prediction framework and investigate what factors impact on prediction accuracy. First, we evaluate a range of feature selection methods to obtain “location indicative words”. We then evaluate the impact of nongeotagged tweets, language, and user-declared metadata on geolocation prediction. In addition, we evaluate the impact of temporal variance on model generalisation, and discuss how users differ in terms of their geolocatability. We achieve state-of-the-art results for the text-based Twitter user geolocation task, and also provide the most extensive exploration of the task to date. Our findings provide valuable insights into the design of robust, practical text-based geolocation prediction systems.
Modeling and simulation studies on differential drive assisted steering for EV with four-wheel-independent-drive

A novel power assisted steering system was proposed in this paper, due to the independent driving characteristics of electric vehicle (EV) with four-wheel-independent-drive. The basic theory of differential drive torque assisted steering is discussed at first. After that, vehicle dynamics model with steering system was built in SIMULINK, and the control strategy for minimizing the difference between the reference steering effort and the actual steering effort by controlling the drive torque distribution between the wheels of two sides of the vehicle body was constructed. The simulation results showed that, to four-wheel motored EV, the proposed differential drive torque assisted steering can reduce the steering efforts to a reasonable level obviously, and ensure the most possible road feel feedback to the driver. Moreover, it can improve return-to-center performance of the steering system.
Persuasive Technology and Moral Responsibility Toward an ethical framework for persuasive technologies Paper for Persuasive 06

How to deal with ethical issues surrounding persuasive technologies? Despite the obvious good intentions behind technologies like the FoodPhone (helping to fight obesity), the HygieneGuard (motivating people to wash their hands after using the toilet), and the EconoMeter (helping people to drive their cars more economically), ethical questions abound. Persuasive technologies can have undesirable consequences, employ problematic methods of persuasion, or persuade users to do things which cannot be morally justified. Moreover, the very practice of technological persuasion can raise moral questions in itself, because it might be seen as a threat to human autonomy, a source of moral laziness, or an anti-democratic force in society which lets designers rather than representatives of the people steer our behavior. This paper will address the ethical aspects of persuasive technologies and their designs in three steps. First, it will localize the most important points of application for moral reflection during the design of persuasive technologies. By integrating the concept of ‘persuasive technology’ with the concept of ‘technological mediation’, as developed in theoretical frameworks from the field of persuasive technology and from the philosophy of technology, it will be possible to identify three such points of application. Second, this paper will deal with the process of moral reflection itself. A method will be elaborated to facilitate moral decision-making during the design of persuasive technologies, using the points of application identified in section 2. Third, the paper will discuss a number of ethical issues which are a bit more external to the practice of designing persuasive technologies, and which mainly concern the moral acceptability of specific persuasive technologies and of the phenomenon of persuasive technology as such.
The Robust Manifold Defense: Adversarial Training using Generative Models

Deep neural networks are demonstrating excellent performance on several classical vision problems. However, these networks are vulnerable to adversarial examples, minutely modified images that induce arbitrary attacker-chosen output from the network. We propose a mechanism to protect against these adversarial inputs based on a generative model of the data. We introduce a pre-processing step that projects on the range of a generative model using gradient descent before feeding an input into a classifier. We show that this step provides the classifier with robustness against first-order, substitute model, and combined adversarial attacks. Using a min-max formulation, we show that there may exist adversarial examples even in the range of the generator, natural-looking images extremely close to the decision boundary for which the classifier has unjustifiedly high confidence. We show that adversarial training on the generative manifold can be used to make a classifier that is robust to these attacks. Finally, we show how our method can be applied even without a pre-trained generative model using a recent method called the deep image prior. We evaluate our method on MNIST, CelebA and Imagenet and show robustness against the current state of the art attacks.
User Acceptance of Mobile Payments : A Theoretical Model for Mobile Payments

Mobile payment refers to the use of mobile devices to conduct payment transactions. Users can use mobile devices for remote and proximity payments; moreover, they can purchase digital contents and physical goods and services. It offers an alternative payment method for consumers. However, there are relative low adoption rates in this payment method. This research aims to identify and explore key factors that affect the decision of whether to use mobile payments. Two well-established theories, the Technology Acceptance Model (TAM) and the Innovation Diffusion Theory (IDT), are applied to investigate user acceptance of mobile payments. Survey data from mobile payments users will be used to test the proposed hypothesis and the model.
Artificial Intelligence to Win the Nobel Prize and Beyond: Creating the Engine for Scientific Discovery

artificial intelligence can deliver? What pushes the human race forward? Our civilization has advanced largely by scientific discoveries and the application of such knowledge. Therefore, I propose the launch of a grand challenge to develop AI systems that can make significant scientific discoveries. As a field with great potential social impacts, and one that suffers particularly from information overflow, along with the limitations of human cognition, I believe that the initial focus of this challenge should be on biomedical sciences, but it can be applied to other areas later. The challenge is “to develop an AI system that can make major scientific discoveries in biomedical sciences and that is worthy of a Nobel Prize and far beyond.” While recent progress in high-throughput “omics” measurement technologies has enabled us to generate vast quantities of data, scientific discoveries themselves still depend heavily upon individual intuition, while researchers are often overwhelmed by the sheer amount of data, as well as by the complexity of the biological phenomena they are seeking to understand. Even now, scientific discovery remains something akin to a cottage industry, but a great transformation seems to have begun. This is an ideal domain and the ideal timing for AI to make a difference. I anticipate that, in the near future, AI systems will make a succession of discoveries that have immediate medical implications, saving millions of lives, and totally changing the fate of the human race.
Effect of core strengthening with pelvic proprioceptive neuromuscular facilitation on trunk, balance, gait, and function in chronic stroke

The purpose of this study was to evaluate the effects of core strengthening combined with pelvic proprioceptive neuromuscular facilitation (PNF) on trunk impairment, balance, gait, and functional ability of chronic stroke patients. Twenty-three participants with chronic stroke were recruited and randomly allocated to one of the two groups: core strengthening combined with pelvic PNF (group 1, n=13), and pelvic PNF with trunk flexibility exercises (group 2, n=10). Intervention was given to both groups for 60 min per session 5 times per week for 4 weeks. Performance of both groups was evaluated on Trunk Impairment Scale, Tinetti Performance Oriented Mobility Assessment (Tinetti-POMA), Balance Evaluation Systems Test (Mini-BESTest), Wisconsin Gait Scale, and Barthel Activities of Daily Living Index prior to and after the completion of the intervention. The comparison between postintervention scores of Tinetti-POMA between group 1 (18.76±1.78) and group 2 (16.8±1.87) and Mini-BESTest group 1 (16.15±1.28) and group 2 (14.7±1.41) showed significant difference (P=0.018). The results indicated that core stabilisation combined with pelvic PNF was more effective for improving trunk impairment, balance and gait of chronic stroke patients.
OCRdroid: A Framework to Digitize Text Using Mobile Phones

As demand grows for mobile phone applications, research in optical character recognition, a technology well developed for scanned documents, is shifting focus to the recognition of text embedded in digital photographs. In this paper, we present OCRdroid, a generic framework for developing OCR-based applications on mobile phones. OCRdroid combines a light-weight image preprocessing suite installed inside the mobile phone and an OCR engine connected to a backend server. We demonstrate the power and functionality of this framework by implementing two applications called PocketPal and PocketReader based on OCRdroid on HTC Android G1 mobile phone. Initial evaluations of these pilot experiments demonstrate the potential of using OCRdroid framework for realworld OCR-based mobile applications.
Adaptive inventory control models for supply chain management

Uncertainties inherent in customer demands make it difficult for supply chains to achieve just-in-time inventory replenishment, resulting in loosing sales opportunities or keeping excessive chain-wide inventories. In this paper, we propose two adaptive inventory-control models for a supply chain consisting of one supplier and multiple retailers. The one is a centralized model and the other is a decentralized model. The objective of the two models is to satisfy a target service level predefined for each retailer. The inventory-control parameters of the supplier and retailers are safety lead time and safety stocks, respectively. Unlike most extant inventory-control approaches, modelling the uncertainty of customer demand as a statistical distribution is not a prerequisite in the two models. Instead, using a reinforcement learning technique called action-value method, the control parameters are designed to adaptively change as customerdemand patterns changes. A simulation-based experiment was C.O. Kim ( ) School of Computer and Industrial Engineering, Yonsei University, Seoul, 120-749, Republic of Korea E-mail: kimco@yonsei.ac.kr Tel: (+822) 2123-2711 Fax: (+822) 364-7807  Research Institute for Information and Communication Technologies, Korea University, Seoul, 136-701, Republic of Korea J.K. Baek Department of Industrial System Management, Seoul College, Seoul, 131-702, Republic of Korea R.L. Smith Department of Industrial and Operations Engineering, University of Michigan,  Kim Department of Industrial Engineering, Korea Advanced Institute of Science and Technology, Daejon, 305-701, Republic of Korea performed to compare the performance of the two inventorycontrol models.
International Call Fraud Detection Systems and Techniques

In recent years, fraud in telecommunication industry becomes one of encumbrance for a telecommunication operator which is growing dramatically. It is befall a serious international problem for GSM and PSTN network service providers. It has undoubtedly become a significant source of revenue losses and bad debts to the telecommunication industry, and with the expected continuing growth in revenue it can be expected that fraud will increase proportionally. It has become a mainreason of revenue losses in the industry of telecommunications. This study focuses on International call fraud detection system and its techniques. It proposes a new technique to detect fraud in international call by classifying the CDRs for roaming subscribers. SIM Boxes (also known as GSM Gateways) causes significant interconnect revenue losses for mobile operators by bypassing official interconnections which makes the operators lose millions of wholesale minutes. This research provides an algorithm to determine suspected fraud number. Even if ordinal CLI is unavailable, the solution can successfully track calls path. The proposed algorithm enables telecommunication operators apply fraud detection solution at minimum cost of operation. There are two main parts of study. The first one is the theory of fraud in telecommunication operators, fraud management system and its techniques. The second one is the implemented solution to detect fraud in international call by utilizing homework data.
Futures and spot prices – an analysis of the Scandinavian electricity market

In this paper we first give a presentation of the history and organisation of the electricity market in Scandinavia, which has been gradually restructured over the last decade. A futures market has been in operation there since September 1995. We analyse the historical prices in the spot and futures markets, using general theory for pricing of commodities futures contracts. We find that the futures prices on average exceeded the actual spot price at delivery. Hence, we conclude that there is a negative risk premium in the electricity futures market. This result contradicts the findings in most other commodities markets, where the risk premium from holding a futures contract tend to be zero or positive. Physical factors like unexpected precipitation can contribute to explain parts of the observations. However, we also identify the difference in flexibility between the supply and demand sides of the electricity market, leaving the demand side with higher incentive to hedge their positions in the futures market, as a possible explanation for the negative risk premium. The limited data available might not be sufficient to draw fully conclusive results. However, the analysis described in the paper can be repeated with higher significance in a few years from now.