nils-herrmann/clean_scirepeval huggingface dataset:
Behavioral activation and inhibition systems and the severity and course of depression.

Theorists have proposed that depression is associated with abnormalities in the behavioral activation (BAS) and behavioral inhibition (BIS) systems. In particular, depressed individuals are hypothesized to exhibit deficient BAS and overactive BIS functioning. Self-reported levels of BAS and BIS were examined in 62 depressed participants and 27 nondepressed controls. Clinical functioning was assessed at intake and at 8-month follow-up. Relative to nondepressed controls, depressed participants reported lower BAS levels and higher BIS levels. Within the depressed group, lower BAS levels were associated with greater concurrent depression severity and predicted worse 8-month outcome. Levels of both BIS and BAS showed considerable stability over time and clinical state. Overall, results suggest that BAS dysregulation exacerbates the presentation and course of depressive illness.
Reverse engineering: a roadmap

By the early 1990s the need for reengineering legacy systems was already acute, but recently the demand has increased significantly with the shift toward web-based user interfaces. The demand by all business sectors to adapt their information systems to the Web has created a tremendous need for methods, tools, and infrastructures to evolve and exploit existing applications efficiently and cost-effectively. Reverse engineering has been heralded as one of the most promising technologies to combat this legacy systems problem. This paper presents a roadmap for reverse engineering research for the first decade of the new millennium, building on the program comprehension theories of the 1980s and the reverse engineering technology of the 1990s.
Learning CNN-LSTM Architectures for Image Caption Generation

Automatic image caption generation brings together recent advances in natural language processing and computer vision. This work implements a generative CNN-LSTM model that beats human baselines by 2.7 BLEU-4 points and is close to matching (3.8 CIDEr points lower) the current state of the art. Experiments on the MSCOCO dataset set shows that it generates sensible and accurate captions in a majority of cases, and hyperparameter tuning using dropout and number of LSTM layers allow us to alleviate the effects of overfitting. We also demonstrate that semantically-close emitted words (e.g. ’plate’ and ’bowl’) move the LSTM hidden state in similar ways despite differing previous contexts, and that divergences in hidden state occur only upon emission of semantically-distant words (e.g. ’vase’ and ’food’). This gives semantic meaning to the interaction between learned word embeddings and the LSTM hidden states. To our knowledge, this is a novel contribution to the literature.
Decoding as Continuous Optimization in Neural Machine Translation

In this work, we propose a novel decoding approach for neural machine translation (NMT) based on continuous optimisation. The resulting optimisation problem can then be tackled using a whole range of continuous optimisation algorithms which have been developed and used in the literature mainly for training. Our approach is general and can be applied to other sequence-to-sequence neural models as well. We make use of this powerful decoding approach to intersect an underlying NMT with a language model, to intersect left-to-right and right-to-left NMT models, and to decode with soft constraints involving coverage and fertility of the source sentence words. The experimental results show the promise of the proposed framework.
Combining Formal Concept Analysis with Information Retrieval for Concept Location in Source Code

The paper addresses the problem of concept location in source code by presenting an approach which combines formal concept analysis (FCA) and latent semantic indexing (LSI). In the proposed approach, LSI is used to map the concepts expressed in queries written by the programmer to relevant parts of the source code, presented as a ranked list of search results. Given the ranked list of source code elements, our approach selects most relevant attributes from these documents and organizes the results in a concept lattice, generated via FCA. The approach is evaluated in a case study on concept location in the source code of eclipse, an industrial size integrated development environment. The results of the case study show that the proposed approach is effective in organizing different concepts and their relationships present in the subset of the search results. The proposed concept location method outperforms the simple ranking of the search results, reducing the programmers' effort.
Improving accuracy for cancer classification with a new algorithm for genes selection

Even though the classification of cancer tissue samples based on gene expression data has advanced considerably in recent years, it faces great challenges to improve accuracy. One of the challenges is to establish an effective method that can select a parsimonious set of relevant genes. So far, most methods for gene selection in literature focus on screening individual or pairs of genes without considering the possible interactions among genes. Here we introduce a new computational method named the Binary Matrix Shuffling Filter (BMSF). It not only overcomes the difficulty associated with the search schemes of traditional wrapper methods and overfitting problem in large dimensional search space but also takes potential gene interactions into account during gene selection. This method, coupled with Support Vector Machine (SVM) for implementation, often selects very small number of genes for easy model interpretability. We applied our method to 9 two-class gene expression datasets involving human cancers. During the gene selection process, the set of genes to be kept in the model was recursively refined and repeatedly updated according to the effect of a given gene on the contributions of other genes in reference to their usefulness in cancer classification. The small number of informative genes selected from each dataset leads to significantly improved leave-one-out (LOOCV) classification accuracy across all 9 datasets for multiple classifiers. Our method also exhibits broad generalization in the genes selected since multiple commonly used classifiers achieved either equivalent or much higher LOOCV accuracy than those reported in literature. Evaluation of a gene’s contribution to binary cancer classification is better to be considered after adjusting for the joint effect of a large number of other genes. A computationally efficient search scheme was provided to perform effective search in the extensive feature space that includes possible interactions of many genes. Performance of the algorithm applied to 9 datasets suggests that it is possible to improve the accuracy of cancer classification by a big margin when joint effects of many genes are considered.
Dispatching optimization and routing guidance for emergency vehicles in disaster

Based on the problem that disasters occur frequently all over the world recently. This paper aims to develop dispatching optimization and dynamical routing guidance techniques for emergency vehicles under disaster conditions, so as to reduce emergency response time and avoid further possible deterioration of disaster situation. As to dispatching for emergency vehicles, firstly, classify the casualties into several regions based on the pickup locations, quantity and severity of casualties by an adaptive spectral clustering method, and then work out dispatching strategies for emergency vehicles by k-means clustering method based on the distance among casualties regions, emergency supply stations and hospitals. As to routing guidance for emergency vehicles, centrally dynamic route guidance system based on parallel computing technology is presented to offer safe, reliable and fast routes for emergency vehicles, which are subject to the network's impedance function based on real-time forecasted travel time. Finally, the algorithms presented in this paper are validated based on the platform of ArcGIS by generating casualties randomly in random areas and damaging the simulation network of Changchun city randomly.
Structure tensor Log-Euclidean statistical models for texture analysis

This paper presents local structure tensor (LST) based methods for extracting the anisotropy and orientation information characterizing a textured image sample. A Log-Euclidean (LE) multivariate Gaussian model is proposed for representing the marginal distribution of the LST field of a texture. An extended model is considered as well for describing its spatial dependencies. The potential of these approaches are tested in a rotation invariant content based image retrieval (CBIR) context. Experiments are conducted on real data presenting anisotropic textures: very high resolution (VHR) remote sensing maritime pine forest images and material images representing dense carbons at nanometric scale.
Simulation and analysis of an ultra-wideband TEM horn antenna with ridge

An ultra-wideband TEM horn antenna with ridge is simulated and designed. The feed mode of the antenna adopts the form that the coaxial line is parallel to the radiation direction. By modeling and simulating in CST Microwave Studio® software, it can realize that the impedance bandwidth of |S11|< −10dB is 1‱30 GHz or more. Within this operating bandwidth, the antenna has directional radiation pattern with the gain 6.70–19.40dBi. The simulation results of the antenna are analyzed, and it shows the principle of ultra-wideband operation and directional radiation. The antenna's bandwidth covers L/S/C/X/Ku/K band and it can be widely used in communication and radar system of the band mentioned above.
A Miniaturized, Dual-Band, Circularly Polarized Microstrip Antenna for Installation Into Satellite Mobile Phones

A miniaturized, dual-band, circularly polarized microstrip antenna (CPMA) for satellite mobile phones was designed. Two miniaturized radiation elements using folded structures are stacked vertically to achieve dual bands. Each radiation element shows left-handed circular polarization. The total volume of this dual-band, circularly polarized, microstrip antenna is 46 mm (length) x 24 mm (width) x 13 mm (height). The overall antenna is mounted on a phone-shaped ground plane to fit inside satellite mobile phones.
Multiple extended object tracking using Gaussian processes

The goal of multi-object tracking is to estimate the number of objects and their states recursively over time. In the presence of extended objects, i.e. objects with an extent that is not negligible in comparison to sensor resolution and which give rise to multiple measurements per time step, the tracking task is even more challenging: To fully utilize all available information and to achieve accurate estimation results, measurement models that are able to represent the object shape as well as algorithms that tackle the aggravated track-to-measurements association problem are necessary. In this work, the Gaussian process measurement model is integrated in the recently developed labeled multi-Bernoulli filter for extended objects. Thus, the ability of Gaussian processes to estimate and represent a wide range of free-from shapes is combined with a principled approach to the multiple extended objects tracking problem. The filter performance is demonstrated for simulated and experimental vehicle tracking using a laser scanner. For this particular application, a new, approximately axis-symmetric covariance function is additionally introduced.
“Anarchy is what states make of it: the social construction of power politics,”

Wendt recasts the realist/liberal debate as an argument over the determinants of state action in the international system: i.e., whether state action is influenced by “structure” (anarchy and relative power) or “process” (interaction and learning). He puts forth a “constructivist” argument rejecting the realist belief that the structure of the international system – anarchy and “self-help” – forces states to “play competitive power politics.” Rather, Wendt argues that self-help and power politics do not follow “either logically or causally” from anarchy; if they exist, it is due to process, not structure (394). Thus, Wendt argues that state identities and interests are shaped and transformed within the international system, rather than (as the realists believe) existing as exogenous variables.
SquishedNets: Squishing SqueezeNet further for edge device scenarios via deep evolutionary synthesis

While deep neural networks have been shown in recent years to outperform other machine learning methods in a wide range of applications, one of the biggest challenges with enabling deep neural networks for widespread deployment on edge devices such as mobile and other consumer devices is high computational and memory requirements. Recently, there has been greater exploration into small deep neural network architectures that are more suitable for edge devices, with one of the most popular architectures being SqueezeNet, with an incredibly small model size of 4.8MB. Taking further advantage of the notion that many applications of machine learning on edge devices are often characterized by a low number of target classes, this study explores the utility of combining architectural modifications and an evolutionary synthesis strategy for synthesizing even smaller deep neural architectures based on the more recent SqueezeNet v1.1 macroarchitecture (considered state-of-the-art in efficient architectures) for applications with fewer target classes. In particular, architectural modifications are first made to SqueezeNet v1.1 to accommodate for a 10-class ImageNet-10 dataset, and then an evolutionary synthesis strategy is leveraged to synthesize more efficient deep neural networks based on this modified macroarchitecture. The resulting SquishedNets possess model sizes ranging from 2.4MB to 0.95MB (∼5.17X smaller than SqueezeNet v1.1, or 253X smaller than AlexNet). Furthermore, the SquishedNets are still able to achieve accuracies ranging from 81.2% to 77%, and able to process at speeds of 156 images/sec to as much as 256 images/sec on a Nvidia Jetson TX1 embedded chip. These preliminary results show that a combination of architectural modifications and an evolutionary synthesis strategy can be a useful tool for producing very small deep neural network architectures that are well-suited for edge device scenarios without the need for compression or quantization.
An effective hybridized classifier for breast cancer diagnosis

After lung cancer, breast cancer is known to be the greatest cause for death among females [20]. The improving effectiveness of machine learning approaches is being given a lot of importance by medical practitioners for breast cancer diagnosis. The paper proposes an effective hybridized classifier for breast cancer diagnosis. The classifier is made by combining an unsupervised artificial neural network (ANN) method named self organizing maps (SOM) with a supervised classifier called stochastic gradient descent (SGD). Also a comparative analysis is performed between the proposed approach and three supervised state of the art machine learning techniques decision tree (DTs), random forests (RF) and support vector machine (SVM). Initially SGD method is used in isolation for the classification task and then it is made to perform the classification after being hybridized with the unsupervised ANN technique on Wisconsin Breast Cancer Database (WBCD) . The comparison is based up on classification accuracy that is produced by generating a confusion matrix. For verifying consistency of accuracy values, the classification task was repeated with Internet Advertisements Dataset [11]. The results of the classification experimentation using hybridization of SOM with SGD are much more superior to SGD in isolation. All the accuracy values have been computed after achieving a ten-fold cross validation on the both the datasets to further verify the classifier's performance.
CRISP-DM 1.0: Step-by-step data mining guide

This document describes the CRISP-DM process model, including an introduction to the CRISP-DM methodology, the CRISP-DM reference model, the CRISP-DM user guide and the CRISP-DM reports, as well as an appendix with additional useful and related information. This document and information herein, are the exclusive property of the partners of the CRISP-DM All trademarks and service marks mentioned in this document are marks of their respective owners and are as such acknowledged by the members of the CRISP-DM consortium. Foreword CRISP-DM was conceived in late 1996 by three " veterans " of the young and immature data mining market. DaimlerChrysler (then Daimler-Benz) was already experienced, ahead of most industrial and commercial organizations, in applying data mining in its business operations. SPSS (then ISL) had been providing services based on data mining since 1990 and had launched the first commercial data mining workbench – Clementine – in 1994. NCR, as part of its aim to deliver added value to its Teradata data warehouse customers, had established teams of data mining consultants and technology specialists to service its clients' requirements. At that time, early market interest in data mining was showing signs of exploding into widespread uptake. This was both exciting and terrifying. All of us had developed our approaches to data mining as we went along. Were we doing it right? Was every new adopter of data mining going to have to learn, as we had initially, by trial and error? And from a supplier's perspective, how could we demonstrate to prospective customers that data mining was sufficiently mature to be adopted as a key part of their business processes? A standard process model, we reasoned, non-proprietary and freely available, would address these issues for us and for all practitioners. A year later we had formed a consortium, invented an acronym (CRoss-Industry Standard Process for Data Mining), obtained funding from the European Commission and begun to set out our initial ideas. As CRISP-DM was intended to be industry-, tool-and application-neutral, we knew we had to get input from as wide a range as possible of practitioners and others (such as data warehouse vendors and management consultancies) with a vested interest in data mining. We did this by creating the CRISP-DM Special Interest Group (" The SIG " , as it became known). We launched the SIG by broadcasting an invitation to interested parties to join us in Amsterdam for a day-long …
The social impact of a systematic floor cleaner

Mint is an automatic cleaning robot that sweeps and mops hard-surface floors using dusting and mopping cloths. Thanks to the Northstar navigation technology it systematically cleans and navigates in people's homes. Since it first became commercially available in mid 2010, hundreds of thousands of Mint cleaners are nowadays in use at home. In this paper we investigate the product's social impact with respect to the attitude of customers towards a systematic floor cleaner and how such a robot influences their lifestyle. We first report feedback from users owning the product, and demonstrate how Mint changed their everyday life at home. We then evaluate the results of a survey launched in 2012 that addresses the technical understanding of the product and what impact it has on the social life of their users. Our findings suggest that Mint, on average, saves more than one hour of people's time per week, floors are cleaner leading to healthier homes and lives, systematic cleaning is seen as an important feature, and modifications to the environment to support the navigation of the robot are largely accepted.
A Boosted Bayesian Multiresolution Classifier for Prostate Cancer Detection From Digitized Needle Biopsies

Diagnosis of prostate cancer (CaP) currently involves examining tissue samples for CaP presence and extent via a microscope, a time-consuming and subjective process. With the advent of digital pathology, computer-aided algorithms can now be applied to disease detection on digitized glass slides. The size of these digitized histology images (hundreds of millions of pixels) presents a formidable challenge for any computerized image analysis program. In this paper, we present a boosted Bayesian multiresolution (BBMR) system to identify regions of CaP on digital biopsy slides. Such a system would serve as an important preceding step to a Gleason grading algorithm, where the objective would be to score the invasiveness and severity of the disease. In the first step, our algorithm decomposes the whole-slide image into an image pyramid comprising multiple resolution levels. Regions identified as cancer via a Bayesian classifier at lower resolution levels are subsequently examined in greater detail at higher resolution levels, thereby allowing for rapid and efficient analysis of large images. At each resolution level, ten image features are chosen from a pool of over 900 first-order statistical, second-order co-occurrence, and Gabor filter features using an AdaBoost ensemble method. The BBMR scheme, operating on 100 images obtained from 58 patients, yielded: 1) areas under the receiver operating characteristic curve (AUC) of 0.84, 0.83, and 0.76, respectively, at the lowest, intermediate, and highest resolution levels and 2) an eightfold savings in terms of computational time compared to running the algorithm directly at full (highest) resolution. The BBMR model outperformed (in terms of AUC): 1) individual features (no ensemble) and 2) a random forest classifier ensemble obtained by bagging multiple decision tree classifiers. The apparent drop-off in AUC at higher image resolutions is due to lack of fine detail in the expert annotation of CaP and is not an artifact of the classifier. The implicit feature selection done via the AdaBoost component of the BBMR classifier reveals that different classes and types of image features become more relevant for discriminating between CaP and benign areas at different image resolutions.
Visualizing patterns of student engagement and performance in MOOCs

In the last five years, the world has seen a remarkable level of interest in Massive Open Online Courses, or MOOCs. A consistent message from universities participating in MOOC delivery is their eagerness to understand students' online learning processes. This paper reports on an exploratory investigation of students' learning processes in two MOOCs which have different curriculum and assessment designs. When viewed through the lens of common MOOC learning analytics, the high level of initial student interest and, ultimately, the high level of attrition, makes these two courses appear very similar to each other, and to MOOCs in general. With the goal of developing a greater understanding of students' patterns of learning behavior in these courses, we investigated alternative learning analytic approaches and visual representations of the output of these analyses. Using these approaches we were able to meaningfully classify student types and visualize patterns of student engagement which were previously unclear. The findings from this research contribute to the educational community's understanding of students' engagement and performance in MOOCs, and also provide the broader learning analytics community with suggestions of new ways to approach learning analytic data analysis and visualization.
Lower limb rehabilitation robot

This paper describes a new prototype of lower limb rehabilitation robot (for short: LLRR), including its detailed structure, operative principles, manipulative manual and control mode which give considerate protection to patients. It implements the programmable process during the course of the limbs rehabilitation, furthermore, renders variable and predetermined step posture and force sensing. LLRR could assist patient in-- simulating normal people's footsteps, exercising leg muscles, gradually recovering the neural control toward walking function and finally walking in normal way utterly. Such robot is comprised with steps posture controlling system and weight alleviation controlling mechanism.