nils-herrmann/clean_scirepeval huggingface dataset:
Trust Among Strangers in Internet Transactions : Empirical Analysis of eBay ’ s Reputation System

Reputations that are transmitted from person to person can deter moral hazard and discourage entry by bad types in markets where players repeat transactions but rarely with the same player. On the Internet, information about past transactions may be both limited and potentially unreliable, but it can be distributed far more systematically than the informal gossip among friends that characterizes conventional marketplaces. One of the earliest and best known Internet reputation systems is run by eBay, which gathers comments from buyers and sellers about each other after each transaction. Examination of a large data set from 1999 reveals several interesting features of this system, which facilitates many millions of sales each month. First, despite incentives to free ride, feedback was provided more than half the time. Second, well beyond reasonable expectation, it was almost always positive. Third, reputation profiles were predictive of future performance. However, the net feedback scores that eBay displays encourages Pollyanna assessments of reputations, and is far from the best predictor available. Fourth, although sellers with better reputations were more likely to sell their items, they enjoyed no boost in price, at least for the two sets of items that we examined. Fifth, there was a high correlation between buyer and seller feedback, suggesting that the players reciprocate and retaliate.
MODELLING FINES IN BLAST FRAGMENTATION AND ITS IMPACT ON CRUSHING AND GRINDING

In the mining industry blasts are usually designed to fracture the in-situ rock mass and prepare it for excavation and subsequent transport. The run of mine (ROM) fragmentation is considered good when it is fine enough and loose enough to ensure efficient digging and loading operations. Mining optimisation strategy is hence usually focussed on minimising total mining costs and maintaining these ROM fragmentation characteristics. Although this approach ensures an efficient mining operation it ignores the potential impact on crushing and grinding. Investigations by several researchers have shown that designing blasts to produce ROM fragmentation to optimise crushing and grinding performance, enhances the overall efficiency and profitability (Eloranta 1995, Kojovic et al., 1995, Bulow et al, 1998, Kanchibotla et al 1998, Scott et al 1998, Simkus and Dance, 1998).
THE NEUROIMAGING OF LOVE AND DESIRE: REVIEW AND FUTURE DIRECTIONS

We review recent neuroimaging research on the experiences of romantic love and sexual desire, focusing specifically on the question of links and distinctions between the brain regions involved in these experiences. We conclude that although love and desire are associated with distinct patterns of brain activation, certain regions (such as the caudate, putamen, insula, and anterior cingulate cortex) have shown activation during both experiences, raising the possibility that certain types of love and desire may be relatively distinct from one another (on an experiential and neural level) whereas others are more interconnected. We outline several promising directions for future research on this possibility, for example testing for differences between the neurobiological bases of different types of sexual desires (i.e., those directed toward strangers versus romantic partners; those which are more “responsive” versus automatic, and those which are more or less dependent on an emotional context). We also discuss future research directions related to the study of female sexual desire and orientation.
FOIL it! Find One mismatch between Image and Language caption

In this paper, we aim to understand whether current language and vision (LaVi) models truly grasp the interaction between the two modalities. To this end, we propose an extension of the MSCOCO dataset, FOIL-COCO, which associates images with both correct and ‘foil’ captions, that is, descriptions of the image that are highly similar to the original ones, but contain one single mistake (‘foil word’). We show that current LaVi models fall into the traps of this data and perform badly on three tasks: a) caption classification (correct vs. foil); b) foil word detection; c) foil word correction. Humans, in contrast, have near-perfect performance on those tasks. We demonstrate that merely utilising language cues is not enough to model FOIL-COCO and that it challenges the state-of-the-art by requiring a fine-grained understanding of the relation between text and image.
Impact of Customer Relationship Management ( CRM ) on Marketing Performance : A Case Study in Mellat Bank of Mazandaran Province

Today, with more competiveness of industries, markets, and working atmosphere in productive and service organizations what is very important for maintaining clients present, for attracting new clients and as a result increasing growth of success in organizations is having a suitable relation with clients. Bank is among organizations which are not an exception. Especially, at the moment according to increasing rate of banks` privatization, it can be argued that significance of attracting clients for banks is more than every time. The article tries to investigate effect of CRM on marketing performance in banking industry. The research method is applied and survey and descriptive. Statistical community of the research is 5 branches from Mellat Banks across Khoramabad Province and their clients. There are 45 personnel in this branch and according to Morgan Table the sample size was 40 people. Clients example was considered according to collected information, one questionnaire was designed for bank organization and another one was prepared for banks` clients in which reliability and validity are approved. The research result indicates that CRM is ineffective on marketing performance.
Shear wave elasticity imaging: a new ultrasonic technology of medical diagnostics.

Shear wave elasticity imaging (SWEI) is a new approach to imaging and characterizing tissue structures based on the use of shear acoustic waves remotely induced by the radiation force of a focused ultrasonic beam. SWEI provides the physician with a virtual "finger" to probe the elasticity of the internal regions of the body. In SWEI, compared to other approaches in elasticity imaging, the induced strain in the tissue can be highly localized, because the remotely induced shear waves are attenuated fully within a very limited area of tissue in the vicinity of the focal point of a focused ultrasound beam. SWEI may add a new quality to conventional ultrasonic imaging or magnetic resonance imaging. Adding shear elasticity data ("palpation information") by superimposing color-coded elasticity data over ultrasonic or magnetic resonance images may enable better differentiation of tissues and further enhance diagnosis. This article presents a physical and mathematical basis of SWEI with some experimental results of pilot studies proving feasibility of this new ultrasonic technology. A theoretical model of shear oscillations in soft biological tissue remotely induced by the radiation force of focused ultrasound is described. Experimental studies based on optical and magnetic resonance imaging detection of these shear waves are presented. Recorded spatial and temporal profiles of propagating shear waves fully confirm the results of mathematical modeling. Finally, the safety of the SWEI method is discussed, and it is shown that typical ultrasonic exposure of SWEI is significantly below the threshold of damaging effects of focused ultrasound.
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences

This paper is an attempt to bridge the conceptual gaps between researchers working on the two widely used approaches based on positive definite kernels: Bayesian learning or inference using Gaussian processes on the one side, and frequentist kernel methods based on reproducing kernel Hilbert spaces on the other. It is widely known in machine learning that these two formalisms are closely related; for instance, the estimator of kernel ridge regression is identical to the posterior mean of Gaussian process regression. However, they have been studied and developed almost independently by two essentially separate communities, and this makes it difficult to seamlessly transfer results between them. Our aim is to overcome this potential difficulty. To this end, we review several old and new results and concepts from either side, and juxtapose algorithmic quantities from each framework to highlight close similarities. We also provide discussions on subtle philosophical and theoretical differences between the two approaches.
PPSGen : Learning to Generate Presentation Slides for

In this paper, we investigate a very challenging task of automatically generating presentation slides for academic papers. The generated presentation slides can be used as drafts to help the presenters prepare their formal slides in a quicker way. A novel system called PPSGen is proposed to address this task. It first employs regression methods to learn the importance of the sentences in an academic paper, and then exploits the integer linear programming (ILP) method to generate well-structured slides by selecting and aligning key phrases and sentences. Evaluation results on a test set of 200 pairs of papers and slides collected on the web demonstrate that our proposed PPSGen system can generate slides with better quality. A user study is also illustrated to show that PPSGen has a few evident advantages over baseline methods.
Introduction to Analytic Number Theory Math 531 Lecture Notes , Fall 2005

The values of most common arithmetic functions f (n), such as the divisor function d(n) or the Moebius function µ(n), depend heavily on the arithmetic nature of the argument n. As a result, such functions exhibit a seemingly chaotic behavior when plotted or tabulated as functions of n, and it does not make much sense to seek an " asymptotic formula " for f (n). However, it turns out that most natural arithmetic functions are very well behaved on average, in the sense that the arithmetic means M f (x) = (1/x) n≤x f (n), or, equivalently, the " summatory functions " S f (x) = n≤x f (n), behave smoothly as x → ∞ and can often be estimated very accurately. In this chapter we discuss the principal methods to derive such estimates. Aside from the intrinsic interest of studying the behavior of M f (x) or S f (x), these quantities arise naturally in a variety of contexts, and having good estimates available is crucial for a number of applications. Here are some examples, all of which will be discussed in detail later in this chapter. (1) The number of Farey fractions of order Q, i.e., the number of rationals in the interval (0, 1) whose denominator in lowest terms is ≤ Q, is equal to S φ (Q), where S φ (x) = n≤x φ(n) is the summatory function of the Euler phi function. (2) The " probability " that two randomly chosen positive integers are co-prime is equal to the limit lim x→∞ 2S φ (x)/x 2 , which turns to be 6/π 2. (3) The " probability " that a randomly chosen positive integer is squarefree 41 42 CHAPTER 2. ARITHMETIC FUNCTIONS II is equal to the " mean value " of the function µ 2 (n)(= |µ(n)|), i.e., the limit lim x→∞ M µ 2 (x), which turns out to be 6/π 2. (4) More generally, if f A (n) is the characteristic function of a set A ⊂ N, then the mean value lim x→∞ M f A (x) of f A , if it exists, can be interpreted as the " density " of the set A, or the " probability " that a randomly chosen positive integer belongs to A. (5) The Prime Number Theorem is equivalent to the relation lim x→∞ M Λ (x) = 1, which can be interpreted …
Tracking the World State with Recurrent Entity Networks

We introduce a new model, the Recurrent Entity Network (EntNet). It is equipped with a dynamic long-term memory which allows it to maintain and update a representation of the state of the world as it receives new data. For language understanding tasks, it can reason on-the-fly as it reads text, not just when it is required to answer a question or respond as is the case for a Memory Network (Sukhbaatar et al., 2015). Like a Neural Turing Machine or Differentiable Neural Computer (Graves et al., 2014; 2016) it maintains a fixed size memory and can learn to perform location and content-based read and write operations. However, unlike those models it has a simple parallel architecture in which several memory locations can be updated simultaneously. The EntNet sets a new state-of-the-art on the bAbI tasks, and is the first method to solve all the tasks in the 10k training examples setting. We also demonstrate that it can solve a reasoning task which requires a large number of supporting facts, which other methods are not able to solve, and can generalize past its training horizon. It can also be practically used on large scale datasets such as Children’s Book Test, where it obtains competitive performance, reading the story in a single pass.
DNA Steganalysis Using Deep Recurrent Neural Networks

Recent advances in next-generation sequencing technologies have facilitated the use of deoxyribonucleic acid (DNA) as a novel covert channels in steganography. There are various methods that exist in other domains to detect hidden messages in conventional covert channels. However, they have not been applied to DNA steganography. The current most common detection approaches, namely frequency analysis-based methods, often overlook important signals when directly applied to DNA steganography because those methods depend on the distribution of the number of sequence characters. To address this limitation, we propose a general sequence learning-based DNA steganalysis framework. The proposed approach learns the intrinsic distribution of coding and non-coding sequences and detects hidden messages by exploiting distribution variations after hiding these messages. Using deep recurrent neural networks (RNNs), our framework identifies the distribution variations by using the classification score to predict whether a sequence is to be a coding or non-coding sequence. We compare our proposed method to various existing methods and biological sequence analysis methods implemented on top of our framework. According to our experimental results, our approach delivers a robust detection performance compared to other tools.
Topic-bridged PLSA for cross-domain text classification

In many Web applications, such as blog classification and new-sgroup classification, labeled data are in short supply. It often happens that obtaining labeled data in a new domain is expensive and time consuming, while there may be plenty of labeled data in a related but different domain. Traditional text classification ap-proaches are not able to cope well with learning across different domains. In this paper, we propose a novel cross-domain text classification algorithm which extends the traditional probabilistic latent semantic analysis (PLSA) algorithm to integrate labeled and unlabeled data, which come from different but related domains, into a unified probabilistic model. We call this new model Topic-bridged PLSA, or TPLSA. By exploiting the common topics between two domains, we transfer knowledge across different domains through a topic-bridge to help the text classification in the target domain. A unique advantage of our method is its ability to maximally mine knowledge that can be transferred between domains, resulting in superior performance when compared to other state-of-the-art text classification approaches. Experimental eval-uation on different kinds of datasets shows that our proposed algorithm can improve the performance of cross-domain text classification significantly.
Multimodal Topic Labelling

Topics generated by topic models are typically presented as a list of topic terms. Automatic topic labelling is the task of generating a succinct label that summarises the theme or subject of a topic, with the intention of reducing the cognitive load of end-users when interpreting these topics. Traditionally, topic label systems focus on a single label modality, e.g. textual labels. In this work we propose a multimodal approach to topic labelling using a simple feedforward neural network. Given a topic and a candidate image or textual label, our method automatically generates a rating for the label, relative to the topic. Experiments show that this multimodal approach outperforms single-modality topic labelling systems.
What Computers Should Know, Shouldn't Know, and Shouldn't Believe

Automatically constructed knowledge bases (KB’s) are a powerful asset for search, analytics, recommendations and data integration, with intensive use at big industrial stakeholders. Examples are the knowledge graphs for search engines (e.g., Google, Bing, Baidu) and social networks (e.g., Facebook), as well as domain-specific KB’s (e.g., Bloomberg, Walmart). These achievements are rooted in academic research and community projects. The largest general-purpose KB’s with publicly accessible contents are BabelNet, DBpedia, Wikidata, and Yago. They contain millions of entities, organized in hundreds to hundred thousands of semantic classes, and billions of relational facts on entities. These and other knowledge and data resources are interlinked at the entity level, forming the Web of Linked Open Data.
Topological Spatial Verification for Instance Search

This paper proposes an elastic spatial verification method for Instance Search, particularly for dealing with non-planar and non-rigid queries exhibiting complex spatial transformations. Different from existing models that map keypoints between images based on a linear transformation (e.g., affine, homography), our model exploits the topological arrangement of keypoints to address the non-linear spatial transformations that are extremely common in real life situations. In particular, we propose a novel technique to elastically verify the topological spatial consistency with the triangulated graph through a “sketch-and-match” scheme. The spatial topology configuration, emphasizing relative positioning rather than absolute coordinates, is first sketched by a triangulated graph, whose edges essentially capture the topological layout of the corresponding keypoints. Next, the spatial consistency is efficiently estimated as the number of common edges between the triangulated graphs. Compared to the existing methods, our technique is much more effective in modeling the complex spatial transformations of non-planar and non-rigid instances, while being compatible to instances with simple linear transformations. Moreover, our method is by nature more robust in spatial verification by considering the locations, rather than the local geometry of keypoints, which are sensitive to motions and viewpoint changes. We evaluate our method extensively on three years of TRECVID datasets, as well as our own dataset MQA, showing large improvement over other methods for the task of Instance Search.
The clinical assessment, treatment, and prevention of lyme disease, human granulocytic anaplasmosis, and babesiosis: clinical practice guidelines by the Infectious Diseases Society of America.

Evidence-based guidelines for the management of patients with Lyme disease, human granulocytic anaplasmosis (formerly known as human granulocytic ehrlichiosis), and babesiosis were prepared by an expert panel of the Infectious Diseases Society of America. These updated guidelines replace the previous treatment guidelines published in 2000 (. The guidelines are intended for use by health care providers who care for patients who either have these infections or may be at risk for them. For each of these Ixodes tickborne infections, information is provided about prevention, epidemiology, clinical manifestations, diagnosis, and treatment. Tables list the doses and durations of antimicrobial therapy recommended for treatment and prevention of Lyme disease and provide a partial list of therapies to be avoided. A definition of post-Lyme disease syndrome is proposed.
Horizontal Monotonicity of the Modulus of the Riemann Zeta Function and Related Functions

As usual let s = σ+ it. For any fixed value t = t0 with |t0| ≥ 8, and for σ ≤ 0, we show that |ζ(s)| is strictly monotone decreasing in σ, with the same result also holding for the related functions ξ of Riemann and η of Euler. The following inequality relating the monotonicity of all three functions is proved:
Enhancement of Cloud Computing Security with Secure Data Storage using AES

The evolution of Cloud computing makes the major changes in computing world as with the assistance of basic cloud computing service models like SaaS, PaaS, and IaaS an organization achieves their business goal with minimum effort as compared to traditional computing environment. On the other hand security of the data in the cloud database server is the key area of concern in the acceptance of cloud. It requires a very high degree of privacy and authentication. To protect the data in cloud database server cryptography is one of the important methods. Cryptography provides various symmetric and asymmetric algorithms to secure the data. This paper presents the symmetric cryptographic algorithm named as AES (Advanced Encryption Standard). It is based on several substitutions, permutation and transformation.
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark

The deep learning community has proposed optimizations spanning hardware, software, and learning theory to improve the computational performance of deep learning workloads. While some of these optimizations perform the same operations faster (e.g., switching from a NVIDIA K80 to P100), many modify the semantics of the training procedure (e.g., large minibatch training, reduced precision), which can impact a model’s generalization ability. Due to a lack of standard evaluation criteria that considers these trade-offs, it has become increasingly difficult to compare these different advances. To address this shortcoming, DAWNBENCH and the upcoming MLPERF benchmarks use time-to-accuracy as the primary metric for evaluation, with the accuracy threshold set close to state-of-the-art and measured on a held-out dataset not used in training; the goal is to train to this accuracy threshold as fast as possible. In DAWNBENCH, the winning entries improved time-to-accuracy on ImageNet by two orders of magnitude over the seed entries. Despite this progress, it is unclear how sensitive time-to-accuracy is to the chosen threshold as well as the variance between independent training runs, and how well models optimized for time-to-accuracy generalize. In this paper, we provide evidence to suggest that time-to-accuracy has a low coefficient of variance and that the models tuned for it generalize nearly as well as pre-trained models. We additionally analyze the winning entries to understand the source of these speedups, and give recommendations for future benchmarking efforts.