nils-herrmann/clean_scirepeval huggingface dataset:
What is wrong with topic modeling? And how to fix it using search-based software engineering

Topic modeling finds human-readable structures in unstructured textual data. A widely used topic modeler is Latent Dirichlet allocation. When run on different datasets, LDA suffers from “order effects” i.e. different topics are generated if the order of training data is shuffled. Such order effects introduce a systematic error for any study. This error can relate to misleading results; specifically, inaccurate topic descriptions and a reduction in the efficacy of text mining classification results.  To provide a method in which distributions generated by LDA are more stable and can be used for further analysis.  We use LDADE, a search-based software engineering tool that tunes LDA’s parameters using DE (Differential Evolution). LDADE is evaluated on data from a programmer information exchange site (Stackoverflow), title and abstract text of thousands of Software Engineering (SE) papers, and software defect reports from NASA. Results were collected across different implementations of LDA (Python+Scikit-Learn, Scala+Spark); across different platforms (Linux, Macintosh) and for different kinds of LDAs (VEM, or using Gibbs sampling). Results were scored via topic stability and text mining classification accuracy.  In all treatments: (i) standard LDA exhibits very large topic instability; (ii) LDADE’s tunings dramatically reduce cluster instability; (iii) LDADE also leads to improved performances for supervised as well as unsupervised learning.  Due to topic instability, using standard LDA with its “off-the-shelf” settings should now be depreciated. Also, in future, we should require SE papers that use LDA to test and (if needed) mitigate LDA topic instability. Finally, LDADE is a candidate technology for effectively and efficiently reducing that instability.
Design of reconfigurable manufacturing systems

This paper explains the rationale for the development of reconfigurable manufacturing systems, which possess the advantages both of dedicated lines and of flexible systems. The paper defines the core characteristics and design principles of reconfigurable manufacturing systems (RMS) and describes the structure recommended for practical RMS with RMS core characteristics. After that, a rigorous mathematical method is introduced for designing RMS with this recommended structure. An example is provided to demonstrate how this RMS design method is used. The paper concludes with a discussion of reconfigurable assembly systems.
A Graph-Based Global Registration for 2D Mosaics

We describe a graph-based global registration method for creating 2D mosaic images. When multi-frames overlap in space, global registration is necessary to minimize the accumulated registration errors. We use a graph to represent the temporal and spatial connectivity and show that global registration can be obtained through the search for an optimal path in the constructed graph. The definition of an adequate objective function characterizing the global registration provides a direct manipulation of the graph. The framework presented here allows the automatic construction of the graph, and the construction of a consistent mosaic from a collection of frames using projective transformations.
Reconciling Manual and Automated Testing: The AutoTest Experience

Software can be tested either manually or automatically. The two approaches are complementary: automated testing can perform a large number of tests in little time, whereas manual testing uses the knowledge of the testing engineer to target testing to the parts of the system that are assumed to be more error-prone. Despite this complementarity, tools for manual and automatic testing are usually different, leading to decreased productivity and reliability of the testing process. AutoTest is a testing tool that provides a "best of both worlds" strategy: it integrates developers' test cases into an automated process of systematic contract-driven testing. This allows it to combine the benefits of both approaches while keeping a simple interface, and to treat the two types of tests in a unified fashion: evaluation of results is the same, coverage measures are added up, and both types of tests can be saved in the same format
Computer-Based Clinical Decision Support System for Prediction of Heart Diseases Using Naïve Bayes Algorithm

Our healthcare sector daily collects a huge data including clinical examination, vital parameters, investigation reports, treatment follow-up and drug decisions etc. But very unfortunately it is not analyzed and mined in an appropriate way. The Health care industry collects the huge amounts of health care data which unfortunately are not “mined” to discover hidden information for effective decision making for health care practitioners. Data mining refers to using a variety of techniques to identify suggest of information or decision making knowledge in database and extracting these in a way that they can put to use in areas such as decision support , Clustering ,Classification and Prediction. This paper has developed a Computer-Based Clinical Decision Support System for Prediction of Heart Diseases (CCDSS) using Naïve Bayes data mining algorithm. CCDSS can answer complex “what if” queries which traditional decision support systems cannot. Using medical profiles such as age, sex, spO2,chest pain type, heart rate, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. CCDSS is Webbased, user-friendly, scalable, reliable and expandable. It is implemented on the PHPplatform. Computer-Based Clinical Decision Support System(CCDSS), Heart disease, Data mining, Naïve Bayes.
Tactile display device using distributed lateral skin stretch

In the past, tactile displays were of one of two kinds: they were either shape displays, or relied on distributed vibrotactile stimulation. A tactile display device is described in this paper which is distinguished by the fact that it relies exclusively on lateral skin stretch stimulation. It is constructed from an array of 64 closely packed piezoelectric actuators connected to a membrane. The deformations of this membrane cause an array of 112 skin contactors to create programmable lateral stress fields in the skin of the finger pad. Some preliminary observations are reported with respect to the sensations that this kind of display can produce.  Tactile displays are devices used to provide subjects with the sensation of touching objects directly with the skin. Previously reported tactile displays portray distributed tactile stimulation as a one of two possibilities . One class of displays, termed “shape displays”, typically consists of devices having a dense array of skin contactors which can move orthogonally to the surface of the skin in an attempt to display the shape of objects via its spatially sampled approximation. There exist numerous examples of such displays, for recent designs see [2; 3; 4; 5]. In the interest of brevity, the distinction between “pressure displays” and shape displays is not made here. However, an important distinction with regard to the focus of this paper must be made between displays intended to cause no slip between the contactors and the skin and those intended for the opposite  Displays which are intended to be used without slip can be mounted on a carrier device [6; 2]. 1Braille displays can be found in this later category. Another class of displays takes advantage of vibrotactile stimulation. With this technique, an array of tactilly active sites stimulates the skin using an array of contactors vibrating at a fixed frequency. This frequency is selected to maximize the loudness of the sensation (200–300 Hz). Tactile images are associated, not to the quasi-static depth of indentation, but the amplitude of the vibration [ Shape displays control the rising movement of the contactors (resp. the force applied to). In a vibrotactile display, the contactors oscillate at a fixed frequency. Devices intended to be used as general purpose tactile displays cause stimulation by independently and simultaneously activated skin contactors according to patterns that depend both on space and on time. Such patterns may be thought of as “tactile images”, but because of the rapid adaptation of the skin mechanoreceptors, the images should more accurately be described as “tactile movies”. It is also accepted that the separation between these contactors needs to be of the order of one millimeter so that the resulting percept fuse into one single continuous image. In addition, when contactors apply vibratory signals to the skin at a frequency, which may range from a few Hertz to a few kiloHertz, a perception is derived which may be described 2The Optacon device is a well known example .
Isotropic Radiation From a Compact Planar Antenna Using Two Crossed Dipoles

This letter presents a compact printed isotropic-radiated planar antenna for 2.4-GHz wireless local area network (WLAN) applications. The antenna consists of two crossed curved dipoles printed on one side of a substrate. By adjusting the lengths of the two crossed curved dipoles, an isotropic radiation pattern is achieved in the whole band of 2.4-2.48 GHz. The proposed antenna is simulated, fabricated, and measured. The simulation results show that the difference between the maximum and the minimum of gains is smaller than 5 dB in the full space. In the measurement, the difference between the maximum and the minimum of gains is smaller than 6 dB over 97% of the full space. The reflection coefficient and gains are also tested and compared to the simulation results.
Convolutional Neural Network Language Models

Convolutional Neural Networks (CNNs) have shown to yield very strong results in several Computer Vision tasks. Their application to language has received much less attention, and it has mainly focused on static classification tasks, such as sentence classification for Sentiment Analysis or relation extraction. In this work, we study the application of CNNs to language modeling, a dynamic, sequential prediction task that needs models to capture local as well as long-range dependency information. Our contribution is twofold. First, we show that CNNs achieve 11-26% better absolute performance than feed-forward neural language models, demonstrating their potential for language representation even in sequential tasks. As for recurrent models, our model outperforms RNNs but is below state of the art LSTM models. Second, we gain some understanding of the behavior of the model, showing that CNNs in language act as feature detectors at a high level of abstraction, like in Computer Vision, and that the model can profitably use information from as far as 16 words before the target.
Development and validation of the State Hope Scale.

Defining hope as a cognitive set comprising agency (belief in one's capacity to initiate and sustain actions) and pathways (belief in one's capacity to generate routes) to reach goals, the Hope Scale was developed and validated previously as a dispositional self-report measure of hope (Snyder et al., 1991). The present 4 studies were designed to develop and validate a measure of state hope. The 6-item State Hope Scale is internally consistent and reflects the theorized agency and pathways components. The relationships of the State Hope Scale to other measures demonstrate concurrent and discriminant validity; moreover, the scale is responsive to events in the lives of people as evidenced by data gathered through both correlational and causal designs. The State Hope Scale offers a brief, internally consistent, and valid self-report measure of ongoing goal-directed thinking that may be useful to researchers and applied professionals.
Resolving Complex Cases of Definite Pronouns: The Winograd Schema Challenge

We examine the task of resolving complex cases of definite pronouns, specifically those for which traditional linguistic constraints on coreference (e.g., Binding Constraints, gender and number agreement) as well as commonly-used resolution heuristics (e.g., string-matching facilities, syntactic salience) are not useful. Being able to solve this task has broader implications in artificial intelligence: a restricted version of it, sometimes referred to as the Winograd Schema Challenge, has been suggested as a conceptually and practically appealing alternative to the Turing Test. We employ a knowledge-rich approach to this task, which yields a pronoun resolver that outperforms state-of-the-art resolvers by nearly 18 points in accuracy on our dataset.
User-Specific Feature-Based Similarity Models for Top-n Recommendation of New Items

Recommending new items for suitable users is an important yet challenging problem due to the lack of preference history for the new items. Noncollaborative user modeling techniques that rely on the item features can be used to recommend new items. However, they only use the past preferences of each user to provide recommendations for that user. They do not utilize information from the past preferences of other users, which can potentially be ignoring useful information. More recent factor models transfer knowledge across users using their preference information in order to provide more accurate recommendations. These methods learn a low-rank approximation for the preference matrix, which can lead to loss of information. Moreover, they might not be able to learn useful patterns given very sparse datasets. In this work, we present <scp>UFSM</scp>, a method for top-<i>n</i> recommendation of new items given binary user preferences. <scp>UFSM</scp> learns <b>U</b>ser-specific <b>F</b>eature-based item-<b>S</b>imilarity <b>M</b>odels, and its strength lies in combining two points: (1) exploiting preference information across all users to learn multiple global item similarity functions and (2) learning user-specific weights that determine the contribution of each global similarity function in generating recommendations for each user. <scp>UFSM</scp> can be considered as a sparse high-dimensional factor model where the previous preferences of each user are incorporated within his or her latent representation. This way, <scp>UFSM</scp> combines the merits of item similarity models that capture local relations among items and factor models that learn global preference patterns. A comprehensive set of experiments was conduced to compare <scp>UFSM</scp> against state-of-the-art collaborative factor models and noncollaborative user modeling techniques. Results show that <scp>UFSM</scp> outperforms other techniques in terms of recommendation quality. <scp>UFSM</scp> manages to yield better recommendations even with very sparse datasets. Results also show that <scp>UFSM</scp> can efficiently handle high-dimensional as well as low-dimensional item feature spaces.
Early Warnings of Cyber Threats in Online Discussions

We introduce a system for automatically generating warnings of imminent or current cyber-threats. Our system leverages the communication of malicious actors on the darkweb, as well as activity of cyber security experts on social media platforms like Twitter. In a time period between September, 2016 and January, 2017, our method generated 661 alerts of which about 84% were relevant to current or imminent cyber-threats. In the paper, we first illustrate the rationale and workflow of our system, then we measure its performance. Our analysis is enriched by two case studies: the first shows how the method could predict DDoS attacks, and how it would have allowed organizations to prepare for the Mirai attacks that caused widespread disruption in October 2016. Second, we discuss the method's timely identification of various instances of data breaches.
Guidelines for Artificial Intelligence Containment

With almost daily improvements in capabilities of artificial intelligence it is more important than ever to develop safety software for use by the AI research community. Building on our previous work on AI Containment Problem we propose a number of guidelines which should help AI safety researchers to develop reliable sandboxing software for intelligent programs of all levels. Such safety container software will make it possible to study and analyze intelligent artificial agent while maintaining certain level of safety against information leakage, social engineering attacks and cyberattacks from within the container.
Far-Field ASR Without Parallel Data

In far-field speech recognition systems, training acoustic models with alignments generated from parallel close-talk microphone data provides significant improvements. However it is not practical to assume the availability of large corpora of parallel close-talk microphone data, for training. In this paper we explore methods to reduce the performance gap between far-field ASR systems trained with alignments from distant microphone data and those trained with alignments from parallel close-talk microphone data. These methods include the use of a lattice-free sequence objective function which tolerates minor mis-alignment errors; and the use of data selection techniques to discard badly aligned data. We present results on single distant microphone and multiple distant microphone scenarios of the AMI LVCSR task. We identify prominent causes of alignment errors in AMI data.
Innovative design of an air cooled ferrite permanent magnet assisted synchronous reluctance machine for automotive traction application

This paper presents the innovative design of an air cooled permanent magnet assisted synchronous reluctance machine (PMaSyRM) for automotive traction application. Key design features include low cost ferrite magnets in an optimized rotor geometry with high saliency ratio, low weight and sufficient mechanical strength as well as a tailored hairpin stator winding in order to meet the demands of an A-segment battery electric vehicle (BEV). Effective torque ripple reduction techniques are analyzed and a suitable combination is chosen to keep additional manufacturing measures as low as possible. Although the ferrite magnets exhibit low remanence, it is shown that their contribution to the electrical machine's performance is essential in the field weakening region. Efficiency optimized torque-speed-characteristics are identified, including additional losses of the inverter, showing an overall system efficiency of more than 94 %. Lastly, the results of no load measurements of a prototype are compared to the FEM simulation results, indicating the proposed design of a PMaSyRM as a cost-effective alternative to state-of-the-art permanent magnet synchronous machines (PMSM) for vehicle traction purposes.
PositionRank: An Unsupervised Approach to Keyphrase Extraction from Scholarly Documents

The large and growing amounts of online scholarly data present both challenges and opportunities to enhance knowledge discovery. One such challenge is to automatically extract a small set of keyphrases from a document that can accurately describe the document’s content and can facilitate fast information processing. In this paper, we propose PositionRank, an unsupervised model for keyphrase extraction from scholarly documents that incorporates information from all positions of a word’s occurrences into a biased PageRank. Our model obtains remarkable improvements in performance over PageRank models that do not take into account word positions as well as over strong baselines for this task. Specifically, on several datasets of research papers, PositionRank achieves improvements as high as 29.09%.
Is Combining Classifiers with Stacking Better than Selecting the Best One?

We empirically evaluate several state-of-the-art methods for constructing ensembles of heterogeneous classifiers with stacking and show that they perform (at best) comparably to selecting the best classifier from the ensemble by cross validation. Among state-of-the-art stacking methods, stacking with probability distributions and multi-response linear regression performs best. We propose two extensions of this method, one using an extended set of meta-level features and the other using multi-response model trees to learn at the meta-level. We show that the latter extension performs better than existing stacking approaches and better than selecting the best classifier by cross validation.
Mobile-D: an agile approach for mobile application development

Mobile phones have been closed environments until recent years. The change brought by open platform technologies such as the Symbian operating system and Java technologies has opened up a significant business opportunity for anyone to develop application software such as games for mobile terminals. However, developing mobile applications is currently a challenging task due to the specific demands and technical constraints of mobile development. Furthermore, at the moment very little is known about the suitability of the different development processes for mobile application development. Due to these issues, we have developed an agile development approach called Mobile-D. The Mobile-D approach is briefly outlined here and the experiences gained from four case studies are discussed.
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

To achieve general intelligence, agents must learn how to interact with others in a shared environment: this is the challenge of multiagent reinforcement learning (MARL). The simplest form is independent reinforcement learning (InRL), where each agent treats its experience as part of its (non-stationary) environment. In this paper, we first observe that policies learned using InRL can overfit to the other agents’ policies during training, failing to sufficiently generalize during execution. We introduce a new metric, joint-policy correlation, to quantify this effect. We describe an algorithm for general MARL, based on approximate best responses to mixtures of policies generated using deep reinforcement learning, and empirical game-theoretic analysis to compute meta-strategies for policy selection. The algorithm generalizes previous ones such as InRL, iterated best response, double oracle, and fictitious play. Then, we present a scalable implementation which reduces the memory requirement using decoupled meta-solvers. Finally, we demonstrate the generality of the resulting policies in two partially observable settings: gridworld coordination games and poker.
OLINDDA: a cluster-based approach for detecting novelty and concept drift in data streams

A machine learning approach that is capable of treating data streams presents new challenges and enables the analysis of a variety of real problems in which concepts change over time. In this scenario, the ability to identify novel concepts as well as to deal with concept drift are two important attributes. This paper presents a technique based on the k-means clustering algorithm aimed at considering those two situations in a single learning strategy. Experimental results performed with data from various domains provide insight into how clustering algorithms can be used for the discovery of new concepts in streams of data.
Anti-Forensics and the Digital Investigator

Viewed generically, anti-forensics (AF) is that set of tactics and measures taken by someone who wants to thwart the digital investigation process. This paper describes some of the many AF tools and methods, under the broad classifications of data hiding, artefact wiping, trail obfuscation, and attacks on the forensics tools themselves. The concept of AF is neither new nor solely intended to be used by the criminal class; it also has legitimate use by those who wish to protect their privacy. This paper also introduces the concept of time-sensitive antiforensics, noting that AF procedures might be employed for the sole purpose of delaying rather than totally preventing the discovery of digital information.