nils-herrmann/clean_scirepeval huggingface dataset:
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

We introduce the “exponential linear unit” (ELU) which speeds up learning in deep neural networks and leads to higher classification accuracies. Like rectified linear units (ReLUs), leaky ReLUs (LReLUs) and parametrized ReLUs (PReLUs), ELUs also avoid a vanishing gradient via the identity for positive values. However ELUs have improved learning characteristics compared to the units with other activation functions. In contrast to ReLUs, ELUs have negative values which allows them to push mean unit activations closer to zero. Zero means speed up learning because they bring the gradient closer to the unit natural gradient. We show that the unit natural gradient differs from the normal gradient by a bias shift term, which is proportional to the mean activation of incoming units. Like batch normalization, ELUs push the mean towards zero, but with a significantly smaller computational footprint. While other activation functions like LReLUs and PReLUs also have negative values, they do not ensure a noise-robust deactivation state. ELUs saturate to a negative value with smaller inputs and thereby decrease the propagated variation and information. Therefore ELUs code the degree of presence of particular phenomena in the input, while they do not quantitatively model the degree of their absence. Consequently, dependencies between ELUs are much easier to model and distinct concepts are less likely to interfere. We found that ELUs lead not only to faster learning, but also to better generalization performance once networks have many layers (≥ 5). ELU networks were among top 10 reported CIFAR-10 results and yielded the best published result on CIFAR-100, without resorting to multi-view evaluation or model averaging. On ImageNet, ELU networks considerably speed up learning compared to a ReLU network with the same architecture, obtaining less than 10% classification error for a single crop, single model network.
MUSEEC: A Multilingual Text Summarization Tool

The MUSEEC (MUltilingual SEntence Extraction and Compression) summarization tool implements several extractive summarization techniques – at the level of complete and compressed sentences – that can be applied, with some minor adaptations, to documents in multiple languages. The current version of MUSEEC provides the following summarization methods: (1) MUSE – a supervised summarizer, based on a genetic algorithm (GA), that ranks document sentences and extracts top–ranking sentences into a summary, (2) POLY – an unsupervised summarizer, based on linear programming (LP), that selects the best extract of document sentences, and (3) WECOM – an unsupervised extension of POLY that compiles a document summary from compressed sentences. In this paper, we provide an overview of MUSEEC methods and its architecture in general.
Forecasting Fraudulent Financial Statements using Data Mining

This paper explores the effectiveness of machine learning techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS. To this end, a number of experiments have been conducted using representative learning algorithms, which were trained using a data set of 164 fraud and non-fraud Greek firms in the recent period 2001-2002. The decision of which particular method to choose is a complicated problem. A good alternative to choosing only one method is to create a hybrid forecasting system incorporating a number of possible solution methods as components (an ensemble of classifiers). For this purpose, we have implemented a hybrid decision support system that combines the representative algorithms using a stacking variant methodology and achieves better performance than any examined simple and ensemble method. To sum up, this study indicates that the investigation of financial information can be used in the identification of FFS and underline the importance of financial ratios. Machine learning, stacking, classifier.
Harvesting Runtime Values in Android Applications That Feature Anti-Analysis Techniques

It is generally challenging to tell apart malware from benign applications. To make this decision, human analysts are frequently interested in runtime values: targets of reflective method calls, URLs to which data is sent, target telephone numbers of SMS messages, and many more. However, obfuscation and string encryption, used by malware as well as goodware, often not only render human inspections, but also static analyses ineffective. In addition, malware frequently tricks dynamic analyses by detecting the execution environment emulated by the analysis tool and then refraining from malicious behavior. In this work we therefore present HARVESTER, an approach to fully automatically extract runtime values from Android applications. HARVESTER is designed to extract values even from highly obfuscated state-of-the-art malware samples that obfuscate method calls using reflection, hide sensitive values in native code, load code dynamically and apply anti-analysis techniques. The approach combines program slicing with code generation and dynamic execution. Experiments on 16,799 current malware samples show that HARVESTER fully automatically extracts many sensitive values, with perfect precision. The process usually takes less than three minutes and does not require human interaction. In particular, it goes without simulating UI inputs. Two case studies further show that by integrating the extracted values back into the app, HARVESTER can increase the recall of existing static and dynamic analysis tools such as FlowDroid and TaintDroid.
Unsupervised Discovery of Mid-Level Discriminative Patches

Our Approach, 0.66 GIST 29.7 Spa>al Pyramid HOG 29.8 Spa>al Pyramid SIFT 34.4 ROI-­‐GIST 26.5 Scene DPM 30.4 MM-­‐Scene 28.0 Object Bank 37.6 Ours 38.1 Ours+GIST 44.0 Ours+SP 46.4 Ours+GIST+SP 47.5 Ours+DPM 42.4 Ours+GIST+DPM 46.9 Ours+SP+DPM 46.4 GIST+SP+DPM 43.1 Ours+GIST+SP+DPM 49.4 Two key requirements • representa,ve: Need to occur frequently enough • discrimina,ve: Need to be different enough from the rest of the “visual world” Goal: a mid-­‐level visual representa>on Experimental Analysis Bonus: works even be`er if weakly supervised!
A Novel Machine Learning Approach to Credit Card Fraud Detection

The use of credit cards is of paramount importance in improving the economic strength of any nation, however, fraudulent activities associated with it is of great concern. When fraud occurs on credit cards, the negative impact is huge as the financial loss experienced cuts across all the parties involved. This paper provides a proactive measure at detecting fraudulent activities regarding the credit card. A novel approach in machine learning known as the cortical learning algorithm was adopted to build the credit card fraud detection model. The algorithm worked on the credit card data obtained from the UCI Repository, it converted the highly populated data to a sparse representation, and then used its learning columns to learn spatial and temporal patterns. The object oriented analysis and design methodology was used in the design of the system which was implemented with JAVA programming language. The simulation was carried out with Matlab. The resulting model performed online learning and recorded higher percentage accuracy of 91% and beyond in detecting fraudulent transactions as compared to the Neural Network model that recorded 89.6%, hence, cases of misclassification was reduced to the barest minimum and efficiency of fraud detection was increased.
Dict2vec : Learning Word Embeddings using Lexical Dictionaries

Learning word embeddings on large unlabeled corpus has been shown to be successful in improving many natural language tasks. The most efficient and popular approaches learn or retrofit such representations using additional external data. Resulting embeddings are generally better than their corpus-only counterparts, although such resources cover a fraction of words in the vocabulary. In this paper, we propose a new approach, Dict2vec, based on one of the largest yet refined datasource for describing words – natural language dictionaries. Dict2vec builds new word pairs from dictionary entries so that semantically-related words are moved closer, and negative sampling filters out pairs whose words are unrelated in dictionaries. We evaluate the word representations obtained using Dict2vec on eleven datasets for the word similarity task and on four datasets for a text classification task.
Detecting Credit Card Fraud by Decision Trees and Support Vector Machines

— With the developments in the Information Technology and improvements in the communication channels, fraud is spreading all over the world, resulting in huge financial losses. Though fraud prevention mechanisms such as CHIP&PIN are developed, these mechanisms do not prevent the most common fraud types such as fraudulent credit card usages over virtual POS terminals or mail orders. As a result, fraud detection is the essential tool and probably the best way to stop such fraud types. In this study, classification models based on decision trees and support vector machines (SVM) are developed and applied on credit card fraud detection problem. This study is one of the firsts to compare the performance of SVM and decision tree methods in credit card fraud detection with a real data set.
DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction

Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods have a strong bias towards lowor high-order interactions, or rely on expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both lowand high-order feature interactions. The proposed framework, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture. Compared to the latest Wide & Deep model from Google, DeepFM has a shared raw feature input to both its “wide” and “deep” components, with no need of feature engineering besides raw features. DeepFM, as a general learning framework, can incorporate various network architectures in its deep component. In this paper, we study two instances of DeepFM where its “deep” component is DNN and PNN respectively, for which we denote as DeepFM-D and DeepFM-P. Comprehensive experiments are conducted to demonstrate the effectiveness of DeepFM-D and DeepFM-P over the existing models for CTR prediction, on both benchmark data and commercial data. We conduct online A/B test in Huawei App Market, which reveals that DeepFM-D leads to more than 10% improvement of click-through rate in the production environment, compared to a well-engineered LR model. We also covered related practice in deploying our framework in Huawei App Market.
THE CAUSES OF CORRUPTION : A CROSS-NATIONAL

Why is corruption—the misuse of public office for private gain— perceived to be more widespread in some countries than others? Different theories associate this with particular historical and cultural traditions, levels of economic development, political institutions, and government policies. This article analyzes several indexes of “perceived corruption” compiled from business risk surveys for the 1980s and 1990s. Six arguments find support. Countries with Protestant traditions, histories of British rule, more developed economies, and (probably) higher imports were less "corrupt". Federal states were more "corrupt". While the current degree of democracy was not significant, long exposure to democracy predicted lower corruption.
ZEUS: Analyzing Safety of Smart Contracts

A smart contract is hard to patch for bugs once it is deployed, irrespective of the money it holds. A recent bug caused losses worth around $50 million of cryptocurrency. We present ZEUS—a framework to verify the correctness and validate the fairness of smart contracts. We consider correctness as adherence to safe programming practices, while fairness is adherence to agreed upon higher-level business logic. ZEUS leverages both abstract interpretation and symbolic model checking, along with the power of constrained horn clauses to quickly verify contracts for safety. We have built a prototype of ZEUS for Ethereum and Fabric blockchain platforms, and evaluated it with over 22.4K smart contracts. Our evaluation indicates that about 94.6% of contracts (containing cryptocurrency worth more than $0.5 billion) are vulnerable. ZEUS is sound with zero false negatives and has a low false positive rate, with an order of magnitude improvement in analysis time as compared to prior art.
DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding

3D context has been shown to be extremely important for scene understanding, yet very little research has been done on integrating context information with deep neural network architectures. This paper presents an approach to embed 3D context into the topology of a neural network trained to perform holistic scene understanding. Given a depth image depicting a 3D scene, our network aligns the observed scene with a predefined 3D scene template, and then reasons about the existence and location of each object within the scene template. In doing so, our model recognizes multiple objects in a single forward pass of a 3D convolutional neural network, capturing both global scene and local object information simultaneously. To create training data for this 3D network, we generate partially synthetic depth images which are rendered by replacing real objects with a repository of CAD models of the same object category1. Extensive experiments demonstrate the effectiveness of our algorithm compared to the state of the art.
Conceptual Space Markup Language (CSML): Towards the Cognitive Semantic Web

CSML is a semantic markup language created for the publishing and sharing of conceptual spaces, which are geometric structures that represent semantics at the conceptual level. CSML can be used to describe semantics that are not captured well by the ontology languages commonly used in the Semantic Web. Measurement of the semantic similarity of concepts as well as the combination of concepts without shared properties are common human cognitive tasks. However, these operations present sources of difficulty for tools reliant upon set-theoretic and syllogistic reasoning on symbolic ontologies. In contrast, these operations can be modeled naturally using conceptual spaces. This paper describes the design decisions behind CSML, introduces the key component elements of a CSML document, and presents examples of its usage.
Augmented Reality as a New Media Experience

In this paper we discuss our work on applying media theory to the creation of narrative augmented reality (AR) experiences. We summarize the concepts of remediation and media forms as they relate to our work, argue for their importance to the development of a new medium such as AR, and present two example AR experiences we have designed using these conceptual tools. In particular, we focus on leveraging the interaction between the physical and virtual world, remediating existing media (film, stage and interactive CD-ROM), and building on the cultural expectations of our users.
HyperNets and Their Application to Learning Spatial Transformations

In this paper we propose a conceptual framework for higher-order artificial neural networks. The idea of higher-order networks arises naturally when a model is required to learn some group of transformations, every element of which is well-approximated by a traditional feedforward network. Thus the group as a whole can be represented as a hyper network. One of typical examples of such groups is spatial transformations. We show that the proposed framework, which we call HyperNets, is able to deal with at least two basic spatial transformations of images: rotation and affine transformation. We show that HyperNets are able not only to generalize rotation and affine transformation, but also to compensate the rotation of images bringing them into canonical forms.
Fixed dental prostheses with vertical tooth preparations without finish lines: A report of two patients.

Tooth abutments can be prepared to receive fixed dental prostheses with different types of finish lines. The literature reports different complications arising from tooth preparation techniques, including gingival recession. Vertical preparation without a finish line is a technique whereby the abutments are prepared by introducing a diamond rotary instrument into the sulcus to eliminate the cementoenamel junction and to create a new prosthetic cementoenamel junction determined by the prosthetic margin. This article describes 2 patients whose dental abutments were prepared to receive ceramic restorations using vertical preparation without a finish line.
WannaCry, Cybersecurity and Health Information Technology: A Time to Act

On Friday, May 12, 2017 a large cyber-attack was launched using WannaCry (or WannaCrypt). In a few days, this ransomware virus targeting Microsoft Windows systems infected more than 230,000 computers in 150 countries. Once activated, the virus demanded ransom payments in order to unlock the infected system. The widespread attack affected endless sectors – energy, transportation, shipping, telecommunications, and of course health care. Britain’s National Health Service (NHS) reported that computers, MRI scanners, blood-storage refrigerators and operating room equipment may have all been impacted. Patient care was reportedly hindered and at the height of the attack, NHS was unable to care for non-critical emergencies and resorted to diversion of care from impacted facilities. While daunting to recover from, the entire situation was entirely preventable. A Bcritical^ patch had been released by Microsoft on March 14, 2017. Once applied, this patch removed any vulnerability to the virus. However, hundreds of organizations running thousands of systems had failed to apply the patch in the first 59 days it had been released. This entire situation highlights a critical need to reexamine how we maintain our health information systems. Equally important is a need to rethink how organizations sunset older, unsupported operating systems, to ensure that security risks are minimized. For example, in 2016, the NHS was reported to have thousands of computers still running Windows XP – a version no longer supported or maintained by Microsoft. There is no question that this will happen again. However, health organizations can mitigate future risk by ensuring best security practices are adhered to.
Fuzzy portfolio selection using fuzzy analytic hierarchy process

Financial problems have been the subject of much research. A widely used approach in recent work on these problems is the use of fuzzy set theory, where fuzzy terms are used to model the uncertain environments. The purpose of this work is to combine the fuzzy analytic hierarchy process (AHP) with the portfolio selection problem. More specifically, the decision-making problem is to decide which stocks are to be chosen for investment and in what proportions they will be bought. To do this, we first dealt with two constrained fuzzy AHP methods given by Enea and Piazza [M. Enea, T. Piazza, Project selection by constrained fuzzy AHP, Fuzzy Optimization and Decision Making 3 (2004) 39–62]. We revised the first of these methods, addressing some of its fallacies, and called it revised constrained fuzzy AHP method (RCFAHP). Then we applied these two methods, namely, RCFAHP and the secondmethod of Enea and Piazza (2004), to the problem of choosing stocks on the Istanbul Stock Exchange (ISE). The methodology used for the hierarchy construction is based on the paper of Satty et al. . In this paper, we show that both of the models provide both ranking and weighting information, via fuzzy AHP, to the investors in this financial scenario. Finally, we discuss the relative advantages and disadvantages of these methods in comparison to existing methods in the literature.
Lamellar bone: structure-function relations.

The term "bone" refers to a family of materials that have complex hierarchically organized structures. These structures are primarily adapted to the variety of mechanical functions that bone fulfills. Here we review the structure-mechanical relations of one bone structural type, lamellar bone. This is the most abundant type in many mammals, including humans. A lamellar unit is composed of five sublayers. Each sublayer is an array of aligned mineralized collagen fibrils. The orientations of these arrays differ in each sublayer with respect to both collagen fibril axes and crystal layers, such that a complex rotated plywood-like structure is formed. Specific functions for lamellar bone, as opposed to the other bone types, could not be identified. It is therefore proposed that the lamellar structure is multifunctional-the "concrete" of the bone family of materials. Experimentally measured mechanical properties of lamellar bone demonstrate a clear-cut anisotropy with respect to the axis direction of long bones. A comparison of the elastic and ultimate properties of parallel arrays of lamellar units formed in primary bone with cylindrically shaped osteonal structures in secondary formed bone shows that most of the intrinsic mechanical properties are built into the lamellar structure. The major advantages of osteonal bone are its fracture properties. Mathematical modeling of the elastic properties based on the lamellar structure and using a rule-of-mixtures approach can closely simulate the measured mechanical properties, providing greater insight into the structure-mechanical relations of lamellar bone.
Email Spam Filtering using Supervised Machine Learning Techniques

E-mail spam, known as unsolicited bulk Email (UBE), junk mail, or unsolicited commercial email (UCE), is the practice of sending unwanted e-mail messages, frequently with commercial content, in large quantities to an indiscriminate set of recipients. Spam is prevalent on the Internet because the transaction cost of electronic communications is radically less than any alternate form of communication. There are many spam filters using different approaches to identify the incoming message as spam, ranging from white list / black list, Bayesian analysis, keyword matching, mail header analysis, postage, legislation, and content scanning etc. Even though we are still flooded with spam emails everyday. This is not because the filters are not powerful enough, it is due to the swift adoption of new techniques by the spammers and the inflexibility of spam filters to adapt the changes. In our work, we employed supervised machine learning techniques to filter the email spam messages. Widely used supervised machine learning techniques namely C 4.5 Decision tree classifier, Multilayer Perceptron, Naïve Bayes Classifier are used for learning the features of spam emails and the model is built by training with known spam emails and legitimate emails. The results of the models are discussed.  Spam, Spam filter, Spammer, Mail header, Machine learning, Classifier