nils-herrmann/clean_scirepeval huggingface dataset:
Set Point Identification and Robustness Testing of Electrostatic Separation Processes

Identification of the optimal operating conditions and evaluation of their robustness are critical issues for the industrial application of electrostatic separation techniques. In spite of extensive investigations performed in recent years, no standard procedure is available for guiding the research of the set point and for minimizing the process sensibility to changes in certain critical factors. The aim of this paper is to formulate a set of recommendations regarding the choice of high-voltage, roll-speed, and feed-rate values for an important class of electrostatic separation applications: the selective sorting of conductive and nonconductive constituents of granular industrial wastes. The experiments were carried out on a laboratory separator, built by one of the authors, with various samples of chopped wire wastes furnished by l'Entreprise des Industries des Cacircbles, Biskra, Algeria. Several one-factor-at-a-time experiments, followed by two factorial designs (one composite, the other fractional), were performed based on the following three-step strategy: 1) identifying the domain of variation of the controlled variables; 2) finding the best choice of the set point; and 3) assessing the robustness of the process, i.e., testing whether the performance of the system remains satisfactory even when the factors vary slightly around that point. The results presented in this paper are strictly valid only for a well-defined category of processed materials, but a similar approach could be adopted for a wider range of electrostatic separation applications
Automatic Collocation Suggestion in Academic Writing

In recent years, collocation has been widely acknowledged as an essential characteristic to distinguish native speakers from non-native speakers. Research on academic writing has also shown that collocations are not only common but serve a particularly important discourse function within the academic community. In our study, we propose a machine learning approach to implementing an online collocation writing assistant. We use a data-driven classifier to provide collocation suggestions to improve word choices, based on the result of classification. The system generates and ranks suggestions to assist learners’ collocation usages in their academic writing with satisfactory results.
Biohashing: two factor authentication featuring fingerprint data and tokenised random number

Human authentication is the security task whose job is to limit access to physical locations or computer network only to those with authorisation. This is done by equipped authorised users with passwords, tokens or using their biometrics. Unfortunately, the first two suffer a lack of security as they are easy being forgotten and stolen; even biometrics also suffers from some inherent limitation and specific security threats. A more practical approach is to combine two or more factor authenticator to reap benefits in security or convenient or both. This paper proposed a novel two factor authenticator based on iterated inner products between tokenised pseudo-random number and the user specific fingerprint feature, which generated from the integrated wavelet and Fourier–Mellin transform, and hence produce a set of user specific compact code that coined as BioHashing. BioHashing highly tolerant of data capture offsets, with same user fingerprint data resulting in highly correlated bitstrings. Moreover, there is no deterministic way to get the user specific code without having both token with random data and user fingerprint feature. This would protect us for instance against biometric fabrication by changing the user specific credential, is as simple as changing the token containing the random data. The BioHashing has significant functional advantages over solely biometrics i.e. zero equal error rate point and clean separation of the genuine and imposter populations, thereby allowing elimination of false accept rates without suffering from increased occurrence of false reject rates. 2004 Pattern Recognition Society.
Wastewater treatment high rate algal ponds for biofuel production.

While research and development of algal biofuels are currently receiving much interest and funding, they are still not commercially viable at today's fossil fuel prices. However, a niche opportunity may exist where algae are grown as a by-product of high rate algal ponds (HRAPs) operated for wastewater treatment. In addition to significantly better economics, algal biofuel production from wastewater treatment HRAPs has a much smaller environmental footprint compared to commercial algal production HRAPs which consume freshwater and fertilisers. In this paper the critical parameters that limit algal cultivation, production and harvest are reviewed and practical options that may enhance the net harvestable algal production from wastewater treatment HRAPs including CO(2) addition, species control, control of grazers and parasites and bioflocculation are discussed.
A Systematic Literature Review: Code Bad Smells in Java Source Code

Code smell is an indication of a software designing problem. The presence of code smells can have a severe impact on the software quality. Smells basically refers to the structure of the code which violates few of the design principals and so has negative effect on the quality of the software. Larger the source code, more is its presence. Software needs to be reliable, robust and easily maintainable so that it can minimize the cost of its development as well as maintenance. Smells may increase the chances of failure of the system during maintenance. A SLR has been performed based on the search of digital libraries that includes the publications since 1999 to 2016. 60 research papers are deeply analyzed that are most relevant. The objective of this paper is to provide an extensive overview of existing research in the field of bad smells, identify the detection techniques and correlation between the detection techniques, in addition to find the name of the code smells that need more attention in detection approaches. This SLR identified that code clone (code smell) receives most research attention. Our findings also show that very few papers report on the impact of code bad smells. Most of the papers focused on the detection techniques and tools. A significant correlation between detection techniques has been calculated. There are four code smells that are not yet detected are Primitive Obsession, Inappropriate Intimacy, Incomplete library class and Comments.
A deep learning framework for brain extraction in humans and animals with traumatic brain injury

Automatic brain extraction or skull stripping from magnetic resonance images (MRI) is an important pre-processing step in many image processing pipelines. Most skull stripping methods are optimized for normal brains and applicable to single T1-w MR images. However, other contrasts, such as T2, can provide complementary information about the boundary. This is especially true in the presence of traumatic brain injury (TBI) and other diseases, where lesions can confound boundary definitions. In this paper, we propose a deep learning based framework to extract intracranial tissues from multi-contrast MR images in the presence of TBI. Our approach is based on state-of-the-art convolutional neural network architecture to learn a transformation from multi-contrast atlas MR images to their stripping masks without using any deformable registration. An advantage of our framework is that it can be applied to different species. We applied our approach to 19 human patients with mild to severe TBI, as well as 16 normal mice images, and another 10 mice brains with TBI. We compared the approach with 3 separate state-of-the-art human and rodent brain extraction methods. Using only a few manually delineated atlases, we showed significant improvement in brain extraction accuracy in both healthy and pathological human and rodent images.
Convolutional CRFs for Semantic Segmentation

For the challenging semantic image segmentation task the most efficient models have traditionally combined the structured modelling capabilities of Conditional Random Fields (CRFs) with the feature extraction power of CNNs. In more recent works however, CRF post-processing has fallen out of favour. We argue that this is mainly due to the slow training and inference speeds of CRFs, as well as the difficulty of learning the internal CRF parameters. To overcome both issues we propose to add the assumption of conditional independence to the framework of fully-connected CRFs. This allows us to reformulate the inference in terms of convolutions, which can be implemented highly efficiently on GPUs. Doing so speeds up inference and training by two orders of magnitude. All parameters of the convolutional CRFs can easily be optimized using backpropagation. To facilitating further CRF research we make our implementation publicly available. Please visit: https://github.com/MarvinTeichmann/ConvCRF
End-to-End Conversion of HTML Tables for Populating a Relational Database

Automating the conversion of human-readable HTML tables into machine-readable relational tables will enable end-user query processing of the millions of data tables found on the web. Theoretically sound and experimentally successful methods for index-based segmentation, extraction of category hierarchies, and construction of a canonical table suitable for direct input to a relational database are demonstrated on 200 heterogeneous web tables. The methods are scalable: the program generates the 198 Access compatible CSV files in ~0.1s per table (two tables could not be indexed).
Nonreciprocal Phase-Shift Composite Right/Left Handed Microstrip Lines Using Ferrite-Rod-Embedded Substrate

Nonreciprocal phase-shift composite right/left handed transmission lines with small nonreciprocity in the amplitude of the transmission coefficients are proposed. The present structures are composed of a microstrip line periodically loaded with series capacitance and shunt inductance, and a ferrite rectangular rod is embedded in the dielectric substrate just below the center strip. The balanced composite right/left handed transmission line with nonreciprocal phase characteristics is designed. The numerical simulation results validate that the nonreciprocity in magnitude of the transmission coefficients is negligibly small whereas the nonreciprocity in the phase is still kept relatively large for the present configuration.
Gait recognition using wifi signals

In this paper, we propose WifiU, which uses commercial WiFi devices to capture fine-grained gait patterns to recognize humans. The intuition is that due to the differences in gaits of different people, the WiFi signal reflected by a walking human generates unique variations in the Channel State Information (CSI) on the WiFi receiver. To profile human movement using CSI, we use signal processing techniques to generate spectrograms from CSI measurements so that the resulting spectrograms are similar to those generated by specifically designed Doppler radars. To extract features from spectrograms that best characterize the walking pattern, we perform autocorrelation on the torso reflection to remove imperfection in spectrograms. We evaluated WifiU on a dataset with 2,800 gait instances collected from 50 human subjects walking in a room with an area of 50 square meters. Experimental results show that WifiU achieves top-1, top-2, and top-3 recognition accuracies of 79.28%, 89.52%, and 93.05%, respectively.
Augmenting Naive Bayes Classifiers with Statistical Language Models

We augment naive Bayes models with statistical n-gram language models to address short-comings of the standard naive Bayes text classifier. The result is a generalized naive Bayes classifier which allows for a local Markov dependence among observations; a model we refer to as the C hain A ugmented N aive Bayes (CAN) Bayes classifier. CAN models have two advantages over standard naive Bayes classifiers. First, they relax some of the independence assumptions of naive Bayes—allowing a local Markov chain dependence in the observed variables—while still permitting efficient inference and learning. Second, they permit straightforward application of sophisticated smoothing techniques from statistical language modeling, which allows one to obtain better parameter estimates than the standard Laplace smoothing used in naive Bayes classification. In this paper, we introduce CAN models and apply them to various text classification problems. To demonstrate the language independent and task independent nature of these classifiers, we present experimental results on several text classification problems—authorship attribution, text genre classification, and topic detection—in several languages—Greek, English, Japanese and Chinese. We then systematically study the key factors in the CAN model that can influence the classification performance, and analyze the strengths and weaknesses of the model.
A model transformation from NL to SBVR

In Requirement Engineering, requirements are usually written in sentences of natural language and natural languages are ambiguous and inconsistent, so the requirements written in natural languages also tend to be ambiguous. To avoid this problem of ambiguity we present an approach of model transformation to generate requirements based on SBVR (Semantics of Business Vocabulary and Business Rules). The information provided in source metamodel (NL) is automatically transformed into target metamodel (SBVR). SBVR metamodel can not only be processed by machine but also provides precise and reliable model for software design. The standard SBVR metamodel is already available but for natural language we proposed our own metamodel because there is no standard metamodel available for natural languages.
Higher-order, adversary-aware, double JPEG-detection via selected training on attacked samples

In this paper we present an adversary-aware double JPEG detector which is capable of detecting the presence of two JPEG compression steps even in the presence of heterogeneous processing and counter-forensic (C-F) attacks. The detector is based on an SVM classifier fed with a large number of features and trained to recognise the traces left by double JPEG detection in the presence of attacks. Since it is not possible to train the SVM on all possible kinds of processing and C-F attacks, a selected set of images, manipulated with a limited number of attacks is added to the training set. The processing tools used for training are chosen among those that proved to be most effective in disabling double JPEG detection. Experimental results prove that training on such a kind of most powerful attacks allows good detection in the presence of a much wider variety of attacks and processing. Good performance are retained over a wide range of compression quality factors.
CQAVis: Visual Text Analytics for Community Question Answering

Community question answering (CQA) forums can provide effective means for sharing information and addressing a user's information needs about particular topics. However, many such online forums are not moderated, resulting in many low quality and redundant comments, which makes it very challenging for users to find the appropriate answers to their questions. In this paper, we apply a user-centered design approach to develop a system, CQAVis, which supports users in identifying high quality comments and get their questions answered. Informed by the user's requirements, the system combines both text analytics and interactive visualization techniques together in a synergistic way. Given a new question posed by the user, the text analytic module automatically finds relevant answers by exploring existing related questions and the comments within their threads. Then the visualization module presents the search results to the user and supports the exploration of related comments. We have evaluated the system in the wild by deploying it within a CQA forum among thousands of real users. Through the online study, we gained deeper insights about the potential utility of the system, as well as learned generalizable lessons for designing visual text analytics systems for the domain of CQA forums.
The Delphi method as a research tool: an example, design considerations and applications

The Delphi method has proven a popular tool in information systems research for identifying and prioritizing issues for managerial decision-making. However, many past studies have not adopted a systematic approach to conduct a Delphi study. This article provides rigorous guidelines for the process of selecting appropriate experts for the study and gives detailed principles for making design choices during the process that ensure a valid study. A detailed example of a study to identify key factors affecting the diffusion of e-commerce in Sub-Saharan Africa illustrates the design choices that may be involved. We conclude with suggestions for theoretical applications.
Exploring Deep Learning Methods for Discovering Features in Speech Signals

Exploring Deep Learning Methods for discovering features in speech signals. Navdeep Jaitly Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2014 This thesis makes three main contributions to the area of speech recognition with Deep Neural Network Hidden Markov Models (DNN-HMMs). Firstly, we explore the effectiveness of features learnt from speech databases using Deep Learning for speech recognition. This contrasts with prior works that have largely confined themselves to using traditional features such as Mel Cepstral Coefficients and Mel log filter banks for speech recognition. We start by showing that features learnt on raw signals using Gaussian-ReLU Restricted Boltzmann Machines can achieve accuracy close to that achieved with the best traditional features. These features are, however, learnt using a generative model that ignores domain knowledge. We develop methods to discover features that are endowed with meaningful semantics that are relevant to the domain using capsules. To this end, we extend previous work on transforming autoencoders and propose a new autoencoder with a domain-specific decoder to learn capsules from speech databases. We show that capsule instantiation parameters can be combined with Mel log filter banks to produce improvements in phone recognition on TIMIT. On WSJ the word error rate does not improve, even though we get strong gains in classification accuracy. We speculate this may be because of the mismatched objectives of word error rate over an utterance and frame error rate on the sub-phonetic class for a frame. Secondly, we develop a method for data augmentation in speech datasets. Such methods result in strong gains in object recognition, but have largely been ignored in speech recognition. Our data augmentation encourages the learning of invariance to vocal tract length of speakers. The method is shown to improve the phone error rate on TIMIT and the word error rate on a 14 hour subset of WSJ. Lastly, we develop a method for learning and using a longer range model of targets, conditioned on the input. This method predicts the labels for multiple frames together and uses a geometric average of these predictions during decoding. It produces state of the art results on phone recognition with TIMIT and also produces significant gains on WSJ.
An Integral Model to Provide Reactive and Proactive Services in an Academic CSIRT Based on Business Intelligence

Cyber-attacks have increased in severity and complexity. That requires, that the CERT/CSIRT research and develops new security tools. Therefore, our study focuses on the design of an integral model based on Business Intelligence (BI), which provides reactive and proactive services in a CSIRT, in order to alert and reduce any suspicious or malicious activity on information systems and data networks. To achieve this purpose, a solution has been assembled, that generates information stores, being compiled from a continuous network transmission of several internal and external sources of an organization. However, it contemplates a data warehouse, which is focused like a correlator of logs, being formed by the information of feeds with diverse formats. Furthermore, it analyzed attack detection and port scanning, obtained from sensors such as Snort and Passive Vulnerability Scanner, which are stored in a database, where the logs have been generated by the systems. With such inputs, we designed and implemented BI systems using the phases of the Ralph Kimball methodology, ETL and OLAP processes. In addition, a software application has been implemented using the SCRUM methodology, which allowed to link the obtained logs to the BI system for visualization in dynamic dashboards, with the purpose of generating early alerts and constructing complex queries using the user interface through objects structures. The results demonstrate, that this solution has generated early warnings based on the level of criticality and level of sensitivity of malware and vulnerabilities as well as monitoring efficiency, increasing the level of security of member institutions.
The happy and unhappy faces of narcissism

Several theorists have argued in favor of a distinction between overt and covert narcissism, and factor analytic studies have supported this distinction. In this paper I demonstrate that overt narcissists report higher self-esteem and higher satisfaction with life, whereas covert narcissists report lower self-esteem and lower satisfaction with life. I also present mediational models to explain why overt narcissists are relatively happy and covert narcissists are relatively unhappy. In analyses using both partial correlations and structural equation modeling, self-esteem consistently mediated the associations between both types of narcissism and happiness, whereas self-deception did not. These results further demonstrate some of the selfcentered benefits associated with overt narcissism and some of the strong psychological costs associated with covert narcissism.
An Unbounded Nonblocking Double-Ended Queue

We introduce a new algorithm for an unbounded concurrent double-ended queue (deque). Like the bounded deque of Herlihy, Luchangco, and Moir on which it is based, the new algorithm is simple and obstruction free, has no pathological long-latency scenarios, avoids interference between operations at opposite ends, and requires no special hardware support beyond the usual compare-and-swap. To the best of our knowledge, no prior concurrent deque combines these properties with unbounded capacity, or provides consistently better performance across a wide range of concurrent workloads.
Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention

Deep neural perception and control networks are likely to be a key component of self-driving vehicles. These models need to be explainable - they should provide easy-tointerpret rationales for their behavior - so that passengers, insurance companies, law enforcement, developers etc., can understand what triggered a particular behavior. Here we explore the use of visual explanations. These explanations take the form of real-time highlighted regions of an image that causally influence the network’s output (steering control). Our approach is two-stage. In the first stage, we use a visual attention model to train a convolution network endto- end from images to steering angle. The attention model highlights image regions that potentially influence the network’s output. Some of these are true influences, but some are spurious. We then apply a causal filtering step to determine which input regions actually influence the output. This produces more succinct visual explanations and more accurately exposes the network’s behavior. We demonstrate the effectiveness of our model on three datasets totaling 16 hours of driving. We first show that training with attention does not degrade the performance of the end-to-end network. Then we show that the network causally cues on a variety of features that are used by humans while driving.