nils-herrmann/clean_scirepeval huggingface dataset:
Do code smells hamper novice programming? A controlled experiment on Scratch programs

Recently, block-based programming languages like Alice, Scratch and Blockly have become popular tools for programming education. There is substantial research showing that block-based languages are suitable for early programming education. But can block-based programs be smelly too? And does that matter to learners? In this paper we explore the code smells metaphor in the context of block-based programming language Scratch. We conduct a controlled experiment with 61 novice Scratch programmers, in which we divided the novices into three groups. One third receive a non-smelly program, while the other groups receive a program suffering from the Duplication or the Long Method smell respectively. All subjects then perform the same comprehension tasks on their program, after which we measure their time and correctness. The results of the experiment show that code smell indeed influence performance: subjects working on the program exhibiting code smells perform significantly worse, but the smells did not affect the time subjects needed. Investigating different types of tasks in more detail, we find that Long Method mainly decreases system understanding, while Duplication decreases the ease with which subjects modify Scratch programs.
MIMO Radar Using Compressive Sampling

A multiple-input multiple-output (MIMO) radar system is proposed for obtaining angle and Doppler information on potential targets. Transmitters and receivers are nodes of a small scale wireless network and are assumed to be randomly scattered on a disk. The transmit nodes transmit uncorrelated waveforms. Each receive node applies compressive sampling to the received signal to obtain a small number of samples, which the node subsequently forwards to a fusion center. Assuming that the targets are sparsely located in the angle-Doppler space, based on the samples forwarded by the receive nodes the fusion center formulates an l 1 -optimization problem, the solution of which yields target angle and Doppler information. The proposed approach achieves the superior resolution of MIMO radar with far fewer samples than required by other approaches. This implies power savings during the communication phase between the receive nodes and the fusion center. Performance in the presence of a jammer is analyzed for the case of slowly moving targets. Issues related to forming the basis matrix that spans the angle-Doppler space, and for selecting a grid for that space are discussed. Extensive simulation results are provided to demonstrate the performance of the proposed approach at difference jammer and noise levels.
Automated Detection of Dark and Bright Lesions in Retinal Images for Early Detection of Diabetic Retinopathy

There is an ever-increasing interest in the development of automatic medical diagnosis systems due to the advancement in computing technology and also to improve the service by medical community. The knowledge about health and disease is required for reliable and accurate medical diagnosis. Diabetic Retinopathy (DR) is one of the most common causes of blindness and it can be prevented if detected and treated early. DR has different signs and the most distinctive are microaneurysm and haemorrhage which are dark lesions and hard exudates and cotton wool spots which are bright lesions. Location and structure of blood vessels and optic disk play important role in accurate detection and classification of dark and bright lesions for early detection of DR. In this article, we propose a computer aided system for the early detection of DR. The article presents algorithms for retinal image preprocessing, blood vessel enhancement and segmentation and optic disk localization and detection which eventually lead to detection of different DR lesions using proposed hybrid fuzzy classifier. The developed methods are tested on four different publicly available databases. The presented methods are compared with recently published methods and the results show that presented methods outperform all others.
A method to define an Enterprise Architecture using the Zachman Framework

The proliferation of IT and its consequent dispersion is an enterprise reality, however, most organizations do not have adequate tools and/or methodologies that enable the management and coordination of their Information Systems. The Zachman Framework provides a structured way for any organization to acquire the necessary knowledge about itself with respect to the Enterprise Architecture. Zachman proposes a logical structure for classifying and organizing the descriptive representations of an enterprise, in different dimensions, and each dimension can be perceived in different perspectives.In this paper, we propose a method for achieving an Enterprise Architecture Framework, based on the Zachman Framework Business and IS perspectives, that defines the several artifacts for each cell, and a method which defines the sequence of filling up each cell in a top-down and incremental approach. We also present a tool developed for the purpose of supporting the Zachman Framework concepts. The tool: (i) behaves as an information repository for the framework's concepts; (ii) produces the proposed artifacts that represent each cell contents, (iii) allows multi-dimensional analysis among cell's elements, which is concerned with perspectives (rows) and/or dimensions (columns) dependency; and (iv) finally, evaluate the integrity, dependency and, business and information systems alignment level, through the answers defined for each framework dimension.
Millimeter-Wave and Submillimeter-Wave Imaging for Security and Surveillance

Passive equipments operating in the 30-300 GHz (millimeter wave) band are compared to those in the 300 GHz-3 THz (submillimeter band). Equipments operating in the submillimeter band can measure distance and also spectral information and have been used to address new opportunities in security. Solid state spectral information is available in the submillimeter region making it possible to identify materials, whereas in millimeter region bulk optical properties determine the image contrast. The optical properties in the region from 30 GHz to 3 THz are discussed for some typical inorganic and organic solids. In the millimeter-wave region of the spectrum, obscurants such as poor weather, dust, and smoke can be penetrated and useful imagery generated for surveillance. In the 30 GHz-3 THz region dielectrics such as plastic and cloth are also transparent and the detection of contraband hidden under clothing is possible. A passive millimeter-wave imaging concept based on a folded Schmidt camera has been developed and applied to poor weather navigation and security. The optical design uses a rotating mirror and is folded using polarization techniques. The design is very well corrected over a wide field of view making it ideal for surveillance and security. This produces a relatively compact imager which minimizes the receiver count.
An Autofocus Method for Backprojection Imagery in Synthetic Aperture Radar

In this letter, we present an autofocus routine for backprojection imagery from spotlight-mode synthetic aperture radar data. The approach is based on maximizing image sharpness and supports the flexible collection and imaging geometries of BP, including wide-angle apertures and the ability to image directly onto a digital elevation map. While image-quality-based autofocus approaches can be computationally intensive, in the backprojection setting, we demonstrate a natural geometric interpretation that allows for optimal single-pulse phase corrections to be derived in closed form as the solution of a quartic polynomial. The approach is applicable to focusing standard backprojection imagery, as well as providing incremental focusing in sequential imaging applications based on autoregressive backprojection. An example demonstrates the efficacy of the approach applied to real data for a wide-aperture backprojection image.
Warped Convolutions: Efficient Invariance to Spatial Transformations

Convolutional Neural Networks (CNNs) are extremely efficient, since they exploit the inherent translation-invariance of natural images. However, translation is just one of a myriad of useful spatial transformations. Can the same efficiency be attained when considering other spatial invariances? Such generalized convolutions have been considered in the past, but at a high computational cost. We present a construction that is simple and exact, yet has the same computational complexity that standard convolutions enjoy. It consists of a constant image warp followed by a simple convolution, which are standard blocks in deep learning toolboxes. With a carefully crafted warp, the resulting architecture can be made invariant to one of a wide range of spatial transformations. We show encouraging results in realistic scenarios, including the estimation of vehicle poses in the Google Earth dataset (rotation and scale), and face poses in Annotated Facial Landmarks in the Wild (3D rotations under perspective).
Feature Models, Grammars, and Propositional Formulas

Feature models are used to specify members of a product-line. Despite years of progress, contemporary tools often provide limited support for feature constraints and offer little or no support for debugging feature models. We integrate prior results to connect feature models, grammars, and propositional formulas. This connection allows arbitrary propositional constraints to be defined among features and enables off-the-shelf satisfiability solvers to debug feature models. We also show how our ideas can generalize recent results on the staged configuration of feature models.
Mixcoin: Anonymity for Bitcoin with accountable mixes

We propose Mixcoin, a protocol to facilitate anonymous payments in Bitcoin and similar cryptocurrencies. We build on the emergent phenomenon of currency mixes, adding an accountability mechanism to expose theft. We demonstrate that incentives of mixes and clients can be aligned to ensure that rational mixes will not steal. Our scheme is efficient and fully compatible with Bitcoin. Against a passive attacker, our scheme provides an anonymity set of all other users mixing coins contemporaneously. This is an interesting new property with no clear analog in better-studied communication mixes. Against active attackers our scheme offers similar anonymity to traditional communication mixes.
Vision-Based Hand Gesture Recognition for Human-Computer Interaction

In recent years, research efforts seeking to provide more natural, human-centered means of interacting with computers have gained growing interest. A particularly important direction is that of perceptive user interfaces, where the computer is endowed with perceptive capabilities that allow it to acquire both implicit and explicit information about the user and the environment. Vision has the potential of carrying a wealth of information in a non-intrusive manner and at a low cost, therefore it constitutes a very attractive sensing modality for developing perceptive user interfaces. Proposed approaches for vision-driven interactive user interfaces resort to technologies such as head tracking, face and facial expression recognition, eye tracking and gesture recognition. In this paper, we focus our attention to vision-based recognition of hand gestures. The first part of the paper provides an overview of the current state of the art regarding the recognition of hand gestures as these are observed and recorded by typical video cameras. In order to make the review of the related literature tractable, this paper does not discuss:
Vision and Learning for Deliberative Monocular Cluttered Flight

Cameras provide a rich source of information while being passive, cheap and lightweight for small and medium Unmanned Aerial Vehicles (UAVs). In this work we present the first implementation of receding horizon control, which is widely used in ground vehicles, with monocular vision as the only sensing mode for autonomous UAV flight in dense clutter. We make it feasible on UAVs via a number of contributions: novel coupling of perception and control via relevant and diverse, multiple interpretations of the scene around the robot, leveraging recent advances in machine learning to showcase anytime budgeted cost-sensitive feature selection, and fast non-linear regression for monocular depth prediction. We empirically demonstrate the efficacy of our novel pipeline via real world experiments of more than 2 kms through dense trees with a quadrotor built from off-the-shelf parts. Moreover our pipeline is designed to combine information from other modalities like stereo and lidar as well if available.
Deep Learning for Automated Extraction of Primary Sites From Cancer Pathology Reports

Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. In this study, we investigated deep learning and a convolutional neural network (CNN), for extracting ICD-O-3 topographic codes from a corpus of breast and lung cancer pathology reports. We performed two experiments, using a CNN and a more conventional term frequency vector approach, to assess the effects of class prevalence and inter-class transfer learning. The experiments were based on a set of 942 pathology reports with human expert annotations as the gold standard. CNN performance was compared against a more conventional term frequency vector space approach. We observed that the deep learning models consistently outperformed the conventional approaches in the class prevalence experiment, resulting in micro- and macro-F score increases of up to 0.132 and 0.226, respectively, when class labels were well populated. Specifically, the best performing CNN achieved a micro-F score of 0.722 over 12 ICD-O-3 topography codes. Transfer learning provided a consistent but modest performance boost for the deep learning methods but trends were contingent on the CNN method and cancer site. These encouraging results demonstrate the potential of deep learning for automated abstraction of pathology reports.
Learning to Paraphrase: An Unsupervised Approach Using Multiple-Sequence Alignment

We address the text-to-text generation problem of sentence-level paraphrasing — a phenomenon distinct from and more difficult than wordor phrase-level paraphrasing. Our approach applies multiple-sequence alignment to sentences gathered from unannotated comparable corpora: it learns a set of paraphrasing patterns represented by word lattice pairs and automatically determines how to apply these patterns to rewrite new sentences. The results of our evaluation experiments show that the system derives accurate paraphrases, outperforming baseline systems.
IsoRankN: spectral methods for global alignment of multiple protein networks

With the increasing availability of large protein-protein interaction networks, the question of protein network alignment is becoming central to systems biology. Network alignment is further delineated into two sub-problems: local alignment, to find small conserved motifs across networks, and global alignment, which attempts to find a best mapping between all nodes of the two networks. In this article, our aim is to improve upon existing global alignment results. Better network alignment will enable, among other things, more accurate identification of functional orthologs across species.



We introduce IsoRankN (IsoRank-Nibble) a global multiple-network alignment tool based on spectral clustering on the induced graph of pairwise alignment scores. IsoRankN outperforms existing algorithms for global network alignment in coverage and consistency on multiple alignments of the five available eukaryotic networks. Being based on spectral methods, IsoRankN is both error tolerant and computationally efficient.


AVAILABILITY
Our software is available freely for non-commercial purposes on request from: http://isorank.csail.mit.edu/.
DIANNE: Distributed Artificial Neural Networks for the Internet of Things

Nowadays artificial neural networks are widely used to accurately classify and recognize patterns. An interesting application area is the Internet of Things (IoT), where physical things are connected to the Internet, and generate a huge amount of sensor data that can be used for a myriad of new, pervasive applications. Neural networks' ability to comprehend unstructured data make them a useful building block for such IoT applications. As neural networks require a lot of processing power, especially during the training phase, these are most often deployed in a cloud environment, or on specialized servers with dedicated GPU hardware. However, for IoT applications, sending all raw data to a remote back-end might not be feasible, taking into account the high and variable latency to the cloud, or could lead to issues concerning privacy. In this paper the DIANNE middleware framework is presented that is optimized for single sample feed-forward execution and facilitates distributing artificial neural networks across multiple IoT devices. The modular approach enables executing neural network components on a large number of heterogeneous devices, allowing us to exploit the local compute power at hand, and mitigating the need for a large server-side infrastructure at runtime.
Multiparty Computation from Threshold Homomorphic Encryption

We introduce a new approach to multiparty computation (MPC) basing it on homomorphic threshold crypto-systems. We show that given keys for any sufficiently efficient system of this type, general MPC protocols for n players can be devised which are secure against an active adversary that corrupts any minority of the players. The total number of bits sent is O(nk|C|), where k is the security parameter and |C| is the size of a (Boolean) circuit computing the function to be securely evaluated. An earlier proposal by Franklin and Haber with the same complexity was only secure for passive adversaries, while all earlier protocols with active security had complexity at least quadratic in n. We give two examples of threshold cryptosystems that can support our construction and lead to the claimed complexities. ∗Basic Research in Computer Science, Center of the Danish National Research Foundation
An Online Learning Approach to Generative Adversarial Networks

We consider the problem of training generative models with a Generative Adversarial Network (GAN). Although GANs can accurately model complex distributions, they are known to be difficult to train due to instabilities caused by a difficult minimax optimization problem. In this paper, we view the problem of training GANs as finding a mixed strategy in a zero-sum game. Building on ideas from online learning we propose a novel training method named Chekhov GAN 1. On the theory side, we show that our method provably converges to an equilibrium for semi-shallow GAN architectures, i.e. architectures where the discriminator is a one layer network and the generator is arbitrary. On the practical side, we develop an efficient heuristic guided by our theoretical results, which we apply to commonly used deep GAN architectures. On several real world tasks our approach exhibits improved stability and performance compared to standard GAN training. 1We base this name on the Chekhov’s gun (dramatic) principle that states that every element in a story must be necessary, and irrelevant elements should be removed. Analogously, our Chekhov GAN algorithm introduces a sequence of elements which are eventually composed to yield a generator. 1 ar X iv :1 70 6. 03 26 9v 1 [ cs .L G ] 1 0 Ju n 20 17
Automated Generation of Oracles for Testing User-Interaction Features of Mobile Apps

As the use of mobile devices becomes increasingly ubiquitous, the need for systematically testing applications (apps) that run on these devices grows more and more. However, testing mobile apps is particularly expensive and tedious, often requiring substantial manual effort. While researchers have made much progress in automated testing of mobile apps during recent years, a key problem that remains largely untracked is the classic oracle problem, i.e., to determine the correctness of test executions. This paper presents a novel approach to automatically generate test cases, that include test oracles, for mobile apps. The foundation for our approach is a comprehensive study that we conducted of real defects in mobile apps. Our key insight, from this study, is that there is a class of features that we term user-interaction features, which is implicated in a significant fraction of bugs and for which oracles can be constructed - in an application agnostic manner -- based on our common understanding of how apps behave. We present an extensible framework that supports such domain specific, yet application agnostic, test oracles, and allows generation of test sequences that leverage these oracles. Our tool embodies our approach for generating test cases that include oracles. Experimental results using 6 Android apps show the effectiveness of our tool in finding potentially serious bugs, while generating compact test suites for user-interaction features.
Exact Top-k Feature Selection via l2, 0-Norm Constraint

In this paper, we propose a novel robust and pragmatic feature selection approach. Unlike those sparse learning based feature selection methods which tackle the approximate problem by imposing sparsity regularization in the objective function, the proposed method only has one `2,1-norm loss term with an explicit `2,0-Norm equality constraint. An efficient algorithm based on augmented Lagrangian method will be derived to solve the above constrained optimization problem to find out the stable local solution. Extensive experiments on four biological datasets show that although our proposed model is not a convex problem, it outperforms the approximate convex counterparts and state-ofart feature selection methods evaluated in terms of classification accuracy by two popular classifiers. What is more, since the regularization parameter of our method has the explicit meaning, i.e. the number of feature selected, it avoids the burden of tuning the parameter, making it a pragmatic feature selection method.
SciHadoop: Array-based query processing in Hadoop

Hadoop has become the de facto platform for large-scale data analysis in commercial applications, and increasingly so in scientific applications. However, Hadoop's byte stream data model causes inefficiencies when used to process scientific data that is commonly stored in highly-structured, array-based binary file formats resulting in limited scalability of Hadoop applications in science. We introduce Sci-Hadoop, a Hadoop plugin allowing scientists to specify logical queries over array-based data models. Sci-Hadoop executes queries as map/reduce programs defined over the logical data model. We describe the implementation of a Sci-Hadoop prototype for NetCDF data sets and quantify the performance of five separate optimizations that address the following goals for several representative aggregate queries: reduce total data transfers, reduce remote reads, and reduce unnecessary reads. Two optimizations allow holistic aggregate queries to be evaluated opportunistically during the map phase; two additional optimizations intelligently partition input data to increase read locality, and one optimization avoids block scans by examining the data dependencies of an executing query to prune input partitions. Experiments involving a holistic function show run-time improvements of up to 8x, with drastic reductions of IO, both locally and over the network.