nils-herrmann/clean_scirepeval huggingface dataset:
Reweighted Wake-Sleep

Training deep directed graphical models with many hidden variables and performing inference remains a major challenge. Helmholtz machines and deep belief networks are such models, and the wake-sleep algorithm has been proposed to train them. The wake-sleep algorithm relies on training not just the directed generative model but also a conditional generative model (the inference network) that runs backward from visible to latent, estimating the posterior distribution of latent given visible. We propose a novel interpretation of the wake-sleep algorithm which suggests that better estimators of the gradient can be obtained by sampling latent variables multiple times from the inference network. This view is based on importance sampling as an estimator of the likelihood, with the approximate inference network as a proposal distribution. This interpretation is confirmed experimentally, showing that better likelihood can be achieved with this reweighted wake-sleep procedure. Based on this interpretation, we propose that a sigmoid belief network is not sufficiently powerful for the layers of the inference network, in order to recover a good estimator of the posterior distribution of latent variables. Our experiments show that using a more powerful layer model, such as NADE, yields substantially better generative models.
A Formal Framework to Characterize Interpretability of Procedures

We provide a novel notion of what it means to be interpretable, looking past the usual association with human understanding. Our key insight is that interpretability is not an absolute concept and so we define it relative to a target model, which may or may not be a human. We define a framework that allows for comparing interpretable procedures by linking it to important practical aspects such as accuracy and robustness. We characterize many of the current state-of-the-art interpretable methods in our framework portraying its general applicability.
Defenses To Protect Against SQL Injection Attacks

Web applications are steadily increasing in our daily routines activities and continue to integrate them. Online Banking, On-line reservations, on-line shopping expect these web applications to be secure and reliable; the terror of SQL– Injection Attacks has become increasingly frequent and serious. SQL Injection Attacks are one of the topmost threats for web application security. Using SQL Injection attackers can leak confidential information: such as credit card numbers, ATM pins, User credentials from web applications and even corrupt the database. This paper presents a new technique to protect Web applications against SQL injection Attacks. SQL Injection Attacks are a class of attacks that many of these systems are highly vulnerable to, and there is no known foolproof defense against such attacks. In this paper, some predefined methods are discussed and integrated approach of encryption method with secure hashing is applied in the database to avoid attack on login phase. This combined method is applied to a system where user’s information is kept and the designing of this system are done by using PHP and MYSQL.  Database security, SQL injection attacks, Hashing, Encryption technique, Preventions.
From Depth Data to Head Pose Estimation: a Siamese approach

The correct estimation of the head pose is a problem of the great importance for many applications. For instance, it is an enabling technology in automotive for driver attention monitoring. In this paper, we tackle the pose estimation problem through a deep learning network working in regression manner. Traditional methods usually rely on visual facial features, such as facial landmarks or nose tip position. In contrast, we exploit a Convolutional Neural Network (CNN) to perform head pose estimation directly from depth data. We exploit a Siamese architecture and we propose a novel loss function to improve the learning of the regression network layer. The system has been tested on two public datasets, Biwi Kinect Head Pose and ICT-3DHP database. The reported results demonstrate the improvement in accuracy with respect to current state-of-the-art approaches and the real time capabilities of the overall framework.
Multiregion Bilinear Convolutional Neural Networks for Person Re-Identification

In this work we explore the applicability of the recently proposed CNN architecture, called Bilinear CNN, and its new modification that we call multi-region Bilinear CNN to the person re-identification problem. Originally, Bilinear CNNs were introduced for fine-grained classification and proved to be both simple and high-performing architectures. Bilinear CNN allows to build an orderless descriptor for an image using outer product of features outputted from two separate feature extractors. Based on this approach, Multiregion Bilinear CNN, apply bilinear pooling over multiple regions for extracting rich and useful descriptors that retain some spatial information. We show than when embedded into a standard “siamese” type learning, bilinear CNNs and in particular their multi-region variants can improve re-identification performance compared to standard CNNs and achieve stateof-the-art accuracy on the largest person re-identification datasets available at the moment, namely CUHK03 and Market-1501.
The psychology of whistleblowing

Whistleblowing — reporting another person’s unethical behavior to a third party — represents an ethical quandary. In some cases whistleblowing appears heroic whereas in other cases it appears reprehensible. This article describes how the decision to blow the whistle rests on the tradeoff that people make between fairness and loyalty. When fairness increases in value, whistleblowing is more likely whereas when loyalty increases in value, whistleblowing is less likely. Furthermore, we describe systematic personal, situational, and cultural factors stemming from the fairness-loyalty tradeoff that drive whistleblowing. Finally, we describe how minimizing this tradeoff and prioritizing constructive dissent can encourage whistleblowing and strengthen collectives.
Comparing Statistical and Machine Learning Classifiers: Alternatives for Predictive Modeling in Human Factors Research

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.
Mixing Qualitative and Quantitative Methods : Triangulation in Action *

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WordFreak: An Open Tool for Linguistic Annotation

WordFreak is a natural language annotation tool that has been designed to be easy to extend to new domains and tasks. Specifically, a plug-in architecture has been developed which allows components to be added to WordFreak for customized visualization, annotation specification, and automatic annotation, without re-compilation. The APIs for these plug-ins provide mechanisms to allow automatic annotators or taggers to guide future annotation to supports active learning. At present WordFreak can be used to annotate a number of different types of annotation in English, Chinese, and Arabic including: constituent parse structure and dependent annotations, and ACE named-entity and coreference annotation. The Java source code for WordFreak is distributed under the Mozilla Public License 1.1 via SourceForge at: http://wordfreak.sourceforge.net. This site also provides screenshots, and a web deployable version of WordFreak.
Advances in the Inspection of Unpiggable Pipelines

The field of in-pipe robotics covers a vast and varied number of approaches to the inspection of pipelines with robots specialising in pipes ranging anywhere from 10 mm to 1200 mm in diameter. Many of these developed systems focus on overcoming in-pipe obstacles such as T-sections and elbows, as a result important aspects of exploration are treated as sub-systems, namely shape adaptability. One of the most prevalent methods of hybridised locomotion today is wall-pressing; generating traction using the encompassing pipe walls. A review of wall-pressing systems has been performed, covering the different approaches taken since their introduction. The advantages and disadvantages of these systems is discussed as well as their effectiveness in the inspection of networks with highly varying pipe diameters. When compared to unconventional in-pipe robotic techniques, traditional full-bore wall-pressing robots were found to be at a disadvantage.
On Type-Aware Entity Retrieval

Today, the practice of returning entities from a knowledge base in response to search queries has become widespread. One of the distinctive characteristics of entities is that they are typed, i.e., assigned to some hierarchically organized type system (type taxonomy). The primary objective of this paper is to gain a better understanding of how entity type information can be utilized in entity retrieval. We perform this investigation in an idealized "oracle" setting, assuming that we know the distribution of target types of the relevant entities for a given query. We perform a thorough analysis of three main aspects: (i) the choice of type taxonomy, (ii) the representation of hierarchical type information, and (iii) the combination of type-based and term-based similarity in the retrieval model. Using a standard entity search test collection based on DBpedia, we find that type information proves most useful when using large type taxonomies that provide very specific types. We provide further insights on the extensional coverage of entities and on the utility of target types.
Energy-efficient target coverage in wireless sensor networks

A critical aspect of applications with wireless sensor networks is network lifetime. Power-constrained wireless sensor networks are usable as long as they can communicate sensed data to a processing node. Sensing and communications consume energy, therefore judicious power management and sensor scheduling can effectively extend network lifetime. To cover a set of targets with known locations when ground access in the remote area is prohibited, one solution is to deploy the sensors remotely, from an aircraft. The lack of precise sensor placement is compensated by a large sensor population deployed in the drop zone, that would improve the probability of target coverage. The data collected from the sensors is sent to a central node (e.g. cluster head) for processing. In this paper we propose un efficient method to extend the sensor network life time by organizing the sensors into a maximal number of set covers that are activated successively. Only the sensors from the current active set are responsible for monitoring all targets and for transmitting the collected data, while all other nodes are in a low-energy sleep mode. By allowing sensors to participate in multiple sets, our problem formulation increases the network lifetime compared with related work [M. Cardei et al], that has the additional requirements of sensor sets being disjoint and operating equal time intervals. In this paper we model the solution as the maximum set covers problem and design two heuristics that efficiently compute the sets, using linear programming and a greedy approach. Simulation results are presented to verify our approaches.
Camera handoff and placement for automated tracking systems with multiple omnidirectional cameras

1077-3142/$ see front matter 2009  A doi:10.1016/j.cviu.2009.04.004 * Corresponding author.  E-mail addresses: cchen10@utk.edu (C.-H. Chen dpage@utk.edu (D. Page), besma@utk.edu (B. Abidi), ak abidi@utk.edu (M. Abidi). In a multi-camera surveillance system, both camera handoff and placement play an important role in generating an automated and persistent object tracking, typical of most surveillance requirements. Camera handoff should comprise three fundamental components, time to trigger handoff process, the execution of consistent labeling, and the selection of the next optimal camera. In this paper, we design an observation measure to quantitatively formulate the effectiveness of object tracking so that we can trigger camera handoff timely and select the next camera appropriately before the tracked object falls out of the field of view (FOV) of the currently observing camera. In the meantime, we present a novel solution to the consistent labeling problem in omnidirectional cameras. A spatial mapping procedure is proposed to consider both the noise inherent to the tracking algorithms used by the system and the lens distortion introduced by omnidirectional cameras. This does not only avoid the tedious process, but also increases the accuracy, to obtain the correspondence between omnidirectional cameras without human interventions. We also propose to use the Wilcoxon Signed-Rank Test to improve the accuracy of trajectory association between pairs of objects. In addition, since we need a certain amount of time to successfully carry out the camera handoff procedure, we introduce an additional constraint to optimally reserve sufficient cameras’ overlapped FOVs for the camera placement. Experiments show that our proposed observation measure can quantitatively formulate the effectiveness of tracking, so that camera handoff can smoothly transfer objects of interest. Meanwhile, our proposed consistent labeling approach can perform as accurately as the geometry-based approach without tedious calibration processes and outperform Calderara’s homography-based approach. Our proposed camera placement method exhibits a significant increase in the camera handoff success rate at the cost of slightly decreased coverage, as compared to Erdem and Sclaroff’s method without considering the requirement on overlapped FOVs.
Piezoelectric nanogenerators based on zinc oxide nanowire arrays.

We have converted nanoscale mechanical energy into electrical energy by means of piezoelectric zinc oxide nanowire (NW) arrays. The aligned NWs are deflected with a conductive atomic force microscope tip in contact mode. The coupling of piezoelectric and semiconducting properties in zinc oxide creates a strain field and charge separation across the NW as a result of its bending. The rectifying characteristic of the Schottky barrier formed between the metal tip and the NW leads to electrical current generation. The efficiency of the NW-based piezoelectric power generator is estimated to be 17 to 30%. This approach has the potential of converting mechanical, vibrational, and/or hydraulic energy into electricity for powering nanodevices.
Learning to recognize reliable users and content in social media with coupled mutual reinforcement

Community Question Answering (CQA) has emerged as a popular forum for users to pose questions for other users to answer. Over the last few years, CQA portals such as Naver and Yahoo! Answers have exploded in popularity, and now provide a viable alternative to general purpose Web search. At the same time, the answers to past questions submitted in CQA sites comprise a valuable knowledge repository which could be a gold mine for information retrieval and automatic question answering. Unfortunately, the quality of the submitted questions and answers varies widely - increasingly so that a large fraction of the content is not usable for answering queries. Previous approaches for retrieving relevant and high quality content have been proposed, but they require large amounts of manually labeled data -- which limits the applicability of the supervised approaches to new sites and domains. In this paper we address this problem by developing a semi-supervised coupled mutual reinforcement framework for simultaneously calculating content quality and user reputation, that requires relatively few labeled examples to initialize the training process. Results of a large scale evaluation demonstrate that our methods are more effective than previous approaches for finding high-quality answers, questions, and users. More importantly, our quality estimation significantly improves the accuracy of search over CQA archives over the state-of-the-art methods.
BuzzSaw at SemEval-2017 Task 7: Global vs. Local Context for Interpreting and Locating Homographic English Puns with Sense Embeddings

This paper describes our system participating in the SemEval-2017 Task 7, for the subtasks of homographic pun location and homographic pun interpretation. For pun interpretation, we use a knowledgebased Word Sense Disambiguation (WSD) method based on sense embeddings. Punbased jokes can be divided into two parts, each containing information about the two distinct senses of the pun. To exploit this structure we split the context that is input to the WSD system into two local contexts and find the best sense for each of them. We use the output of pun interpretation for pun location. As we expect the two meanings of a pun to be very dissimilar, we compute sense embedding cosine distances for each sense-pair and select the word that has the highest distance. We describe experiments on different methods of splitting the context and compare our method to several baselines. We find evidence supporting our hypotheses and obtain competitive results for pun interpretation.
Scale Space Technique for Word Segmentation in Handwritten Documents

Indexing large archives of historical manuscripts, like the papers of George Washington, is required to allow rapid perusal by scholars and researchers who wish to consult the original manuscripts. Presently, such large archives are indexed manually. Since optical character recognition (OCR) works poorly with handwriting, a scheme based on matching word images called word spotting has been suggested previously for indexing such documents. The important steps in this scheme are segmentation of a document page into words and creation of lists containing instances of the same word by word image matching. We have developed a novel methodology for segmenting handwritten document images by analyzing the extent of “blobs” in a scale space representationof the image. We believe this is the first application of scale space to this problem. The algorithm has been applied to around 30 grey level images randomly picked from different sections of the George Washington corpus of 6,400 handwritten document images. An accuracy of 77 − 96 percent was observed with an average accuracy of around 87 percent. The algorithm works well in the presence of noise, shine through and other artifacts which may arise due aging and degradation of the page over a couple of centuries or through the man made processes of photocopying and scanning.
Data Mining and Knowledge Discovery : Applications , Techniques , Challenges and Process Models in Healthcare

Many healthcare leaders find themselves overwhelmed with data, but lack the information they need to make right decisions. Knowledge Discovery in Databases (KDD) can help organizations turn their data into information. Organizations that take advantage of KDD techniques will find that they can lower the healthcare costs while improving healthcare quality by using fast and better clinical decision making. In this paper, a review study is done on existing data mining and knowledge discovery techniques, applications and process models that are applicable to healthcare environments. The challenges for applying data mining techniques in healthcare environment will also be discussed.
Semantic Document Clustering Using a Similarity Graph

Document clustering addresses the problem of identifying groups of similar documents without human supervision. Unlike most existing solutions that perform document clustering based on keywords matching, we propose an algorithm that considers the meaning of the terms in the documents. For example, a document that contains the words "dog" and "cat" multiple times may be placed in the same category as a document that contains the word "pet" even if the two documents share only noise words in common. Our semantic clustering algorithm is based on a similarity graph that stores the degree of semantic relationship between terms (extracted from WordNet), where a term can be a word or a phrase. We experimentally validate our algorithm on the Reuters-21578 benchmark, which contains 11,362 newswire stories that are grouped in 82 categories using human judgment. We apply the k-means clustering algorithm to group the documents using a similarity metric that is based on keywords matching and one that uses the similarity graph. We show that the second approach produces higher precision and recall, which means that this approach matches closer the results of the human study.
Shape distributions

Measuring the similarity between 3D shapes is a fundamental problem, with applications in computer graphics, computer vision, molecular biology, and a variety of other fields. A challenging aspect of this problem is to find a suitable shape signature that can be constructed and compared quickly, while still discriminating between similar and dissimilar shapes.In this paper, we propose and analyze a method for computing shape signatures for arbitrary (possibly degenerate) 3D polygonal models. The key idea is to represent the signature of an object as a shape distribution sampled from a shape function measuring global geometric properties of an object. The primary motivation for this approach is to reduce the shape matching problem to the comparison of probability distributions, which is simpler than traditional shape matching methods that require pose registration, feature correspondence, or model fitting.We find that the dissimilarities between sampled distributions of simple shape functions (e.g., the distance between two random points on a surface) provide a robust method for discriminating between classes of objects (e.g., cars versus airplanes) in a moderately sized database, despite the presence of arbitrary translations, rotations, scales, mirrors, tessellations, simplifications, and model degeneracies. They can be evaluated quickly, and thus the proposed method could be applied as a pre-classifier in a complete shape-based retrieval or analysis system concerned with finding similar whole objects. The paper describes our early experiences using shape distributions for object classification and for interactive web-based retrieval of 3D models.