nils-herrmann/clean_scirepeval huggingface dataset:
Which User Interactions Predict Levels of Expertise in Work-Integrated Learning?

Predicting knowledge levels from user’s implicit interactions with an adaptive system is a difficult task, particularly in learning systems that are used in the context of daily work tasks. We have collected interactions of six persons working with the adaptive work-integrated learning system APOSDLE over a period of two months to find out whether naturally occurring interactions with the system can be used to predict their level of expertise. One set of interactions is based on the tasks they performed, the other on a number of additional Knowledge Indicating Events (KIE). We find that the addition of KIE significantly improves the prediction as compared to using tasks only. Both approaches are superior to a model that uses only the frequencies of events.
Feature selection in text classification

In recent years, text classification have been widely used. Dimension of text data has increased more and more. Working of almost all classification algorithms is directly related to dimension. In high dimension data set, working of classification algorithms both takes time and occurs over fitting problem. So feature selection is crucial for machine learning techniques. In this study, frequently used feature selection metrics Chi Square (CHI), Information Gain (IG) and Odds Ratio (OR) have been applied. At the same time the method Relevancy Frequency (RF) proposed as term weighting method has been used as feature selection method in this study. It is used for tf.idf term as weighting method, Sequential Minimal Optimization (SMO) and Naive Bayes (NB) in the classification algorithm. Experimental results show that RF gives successful results.
Design and implementation of a parallel priority queue on many-core architectures

An efficient parallel priority queue is at the core of the effort in parallelizing important non-numeric irregular computations such as discrete event simulation scheduling and branch-and-bound algorithms. GPGPUs can provide powerful computing platform for such non-numeric computations if an efficient parallel priority queue implementation is available. In this paper, aiming at fine-grained applications, we develop an efficient parallel heap system employing CUDA. To our knowledge, this is the first parallel priority queue implementation on many-core architectures, thus represents a breakthrough. By allowing wide heap nodes to enable thousands of simultaneous deletions of highest priority items and insertions of new items, and taking full advantage of CUDA's data parallel SIMT architecture, we demonstrate up to 30-fold absolute speedup for relatively fine-grained compute loads compared to optimized sequential priority queue implementation on fast multicores. Compared to this, our optimized multicore parallelization of parallel heap yields only 2–3 fold speedup for such fine-grained loads. This parallelization of a tree-based data structure on GPGPUs provides a roadmap for future parallelizations of other such data structures.
Design and experimental validation of HyTAQ, a Hybrid Terrestrial and Aerial Quadrotor

This paper details the design, modeling, and experimental validation of a novel mobile robot capable of both aerial and terrestrial locomotion. Flight is achieved through a quadrotor configuration; four actuators provide the required thrust. Adding a rolling cage to the quadrotor makes terrestrial locomotion possible using the same actuator set and control system. Thus, neither the mass nor the system complexity is increased by inclusion of separate actuators for terrestrial and aerial locomotion. An analysis of the system's energy consumption demonstrates that during terrestrial locomotion, the robot only needs to overcome rolling resistance and consumes much less energy compared to the aerial mode. This solves one of the most vexing problems of quadrotors and rotorcraft in general - their short operation time. Experimental results show that the hybrid robot can travel a distance four times greater and operate almost six times longer than an aerial only system. It also solves one of the most challenging problems in terrestrial robot design - obstacle avoidance. When an obstacle is encountered, the system simply flies over it.
Research synthesis in software engineering: A tertiary study

0950-5849/$ see front matter 2011  A doi:10.1016/j.infsof.2011.01.004 ⇑ Corresponding author. E-mail addresses: dcruzes@idi.ntnu.no ( Comparing and contrasting evidence from multiple studies is necessary to build knowledge and reach conclusions about the empirical support for a phenomenon. Therefore, research synthesis is at the center of the scientific enterprise in the software engineering discipline.  The objective of this article is to contribute to a better understanding of the challenges in synthesizing software engineering research and their implications for the progress of research and practice.  A tertiary study of journal articles and full proceedings papers from the inception of evidencebased software engineering was performed to assess the types and methods of research synthesis in systematic reviews in software engineering.  As many as half of the 49 reviews included in the study did not contain any synthesis. Of the studies that did contain synthesis, two thirds performed a narrative or a thematic synthesis. Only a few studies adequately demonstrated a robust, academic approach to research synthesis.  We concluded that, despite the focus on systematic reviews, there is limited attention paid to research synthesis in software engineering. This trend needs to change and a repertoire of synthesis methods needs to be an integral part of systematic reviews to increase their significance and utility for research and practice.
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping

The lack of reliable data in developing countries is a major obstacle to sustainable development, food security, and disaster relief. Poverty data, for example, is typically scarce, sparse in coverage, and labor-intensive to obtain. Remote sensing data such as high-resolution satellite imagery, on the other hand, is becoming increasingly available and inexpensive. Unfortunately, such data is highly unstructured and currently no techniques exist to automatically extract useful insights to inform policy decisions and help direct humanitarian efforts. We propose a novel machine learning approach to extract large-scale socioeconomic indicators from highresolution satellite imagery. The main challenge is that training data is very scarce, making it difficult to apply modern techniques such as Convolutional Neural Networks (CNN). We therefore propose a transfer learning approach where nighttime light intensities are used as a data-rich proxy. We train a fully convolutional CNN model to predict nighttime lights from daytime imagery, simultaneously learning features that are useful for poverty prediction. The model learns filters identifying different terrains and man-made structures, including roads, buildings, and farmlands, without any supervision beyond nighttime lights. We demonstrate that these learned features are highly informative for poverty mapping, even approaching the predictive performance of survey data collected in the field.
Synthesis and analysis of a flexible elephant trunk robot

This paper presents a novel synthesis and analysis of a flexible elephant trunk robot (biological continuum–style manipulator). The robot includes eight flexible segments, although it can be extended to more segments as necessary. In this study the gravity of the springs is neglected due to the fact that the manipulation force is much larger than these gravity forces. This mechanism exhibits a wide range of maneuverability and has a large number of degrees of freedom. Each segment is designed using a novel flexible mechanism based on the loading of a compression spring in both transverse and axial directions, and using cable–conduit systems. The rotational motion is transformed to tendon-like behavior, which enables the location of the actuators away from the trunk (e.g. at the end of the trunk). The forward kinematics of the mechanism is also presented and lends itself well to computer control. It is shown that the solution of the transverse deflection of each segment is obtained in a general form, while the stiffness coefficients are obtained in closed form from a two-dimensional model (small and large deflection angles) and from a three-dimensional model used in a finite element method to verify results. The friction in the analysis between the cable and the conduit is neglected in the analysis. A prototype trunk segment is experimentally tested, the results are verified and the elephant trunk robot is built. A bench-top actuation system has been developed and a control scheme used in prosthetic hand control has been implemented to control the mechanism.
Cognition and Docition in OFDMA-Based Femtocell Networks

We address the coexistence problem between macrocell and femtocell systems by controlling the aggregated interference generated by multiple femtocell base stations at the macrocell receivers in a distributed fashion. We propose a solution based on intelligent and self-organized femtocells implementing a realtime multi-agent reinforcement learning technique, known as decentralized Q- learning. We compare this cognitive approach to a non-cognitive algorithm and to the well known iterative water- filling, showing the general superiority of our scheme in terms of (non-jeopardized) macrocell capacity. Furthermore, in distributed settings of such femtocell networks, the learning may be complex and slow due to mutually impacting decision making processes, which results in a non-stationary environment. We propose a timely solution -referred to as docition- to improve the learning process based on the concept of teaching and expert knowledge sharing in wireless environments. We demonstrate that such an approach improves the femtocells' learning ability and accuracy. We evaluate the docitive paradigm in the context of a 3GPP compliant OFDMA (Orthogonal Frequency Division Multiple Access) femtocell network modeled as a multi-agent system. We propose different docitive algorithms and we show their superiority to the well known paradigm of independent learning in terms of speed of convergence and precision.
Using Word Embeddings for Automatic Query Expansion

In this paper a framework for Automatic Query Expansion (AQE) is proposed using distributed neural language model word2vec. Using semantic and contextual relation in a distributed and unsupervised framework, word2vec learns a low dimensional embedding for each vocabulary entry. Using such a framework, we devise a query expansion technique, where related terms to a query are obtained by K-nearest neighbor approach. We explore the performance of the AQE methods, with and without feedback query expansion, and a variant of simple K-nearest neighbor in the proposed framework. Experiments on standard TREC ad-hoc data (Disk 4, 5 with query sets 301-450, 601-700) and web data (WT10G data with query set 451-550) shows significant improvement over standard term-overlapping based retrieval methods. However the proposed method fails to achieve comparable performance with statistical oc-occurrence based feedback method such as RM3. We have also found that the word2vec based query expansion methods perform similarly with and without any feedback information.
Security challenges in software defined network and their solutions

The main purpose of Software Defined Networking (SDN) is to allow network engineers to respond quickly to changing network industrial requirements. This network technology focuses on making network as adaptable and active as virtual server. SDN is physical separation of Control plane from Data plane and control plane is centralized to manage underlying infrastructure. Hence, the SDN permit network administrator to adjust wide traffic flow from centralized control console without having to touch Switches and Routers, and can provide the services to wherever they are needed in the network. As in SDN the control plane is disassociated from underlying forwarding plane, however, susceptible to many security challenges like Denial of Service (DoS) attack, Distributed DoS (DDoS) attack, Volumetric attack. In this paper, we highlight some security challenges and evaluate some security solutions.
Distributed energy balanced routing for wireless sensor networks

Most routing algorithms for sensor networks focus on finding energy efficient paths to prolong the lifetime of sensor networks. As a result, the power of sensors on efficient paths depletes quickly, and consequently sensor networks become incapable of monitoring events from some parts of their target areas. In many sensor network applications, the events that must be tracked occur at random locations and have non-deterministic generation patterns. Therefore, ideally, routing algorithms should consider not only energy efficiency, but also the amount of energy remaining in each sensor, thus avoiding non-functioning sensors due to early power depletion. This paper introduces a new metric, energy cost, devised to consider a balance of sensors’ remaining energies, as well as energy efficiency. This metric gives rise to the design of the distributed energy balanced routing (DEBR) algorithm devised to balance the data traffic of sensor networks in a decentralized manner and consequently prolong the lifetime of the networks. DEBR is scalable in the number of sensors and also robust to the variations in the dynamics of event generation. We demonstrate the effectiveness of the proposed algorithm by comparing three existing routing algorithms: direct communication approach, minimum transmission energy, and self-organized routing and find that energy balance should be considered to extend lifetime of sensor network and increase robustness of sensor network for diverse event generation patterns.
End-user feature labeling: Supervised and semi-supervised approaches based on locally-weighted logistic regression

When intelligent interfaces, such as intelligent desktop assistants, email classif iers, and recommender systems, customize themselves to a particular end user , such customizations can decrease productivity and increase frustration due to inacc urate predictions—especially in early stages when training data is limited. The end user can improve the learning algorithm by tediously labeling a substantial amoun t of additional training data, but this takes time and is too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose new supervised and semi-supervised learning algorithms based on locally weighted logistic regression for feature labeling by end users , enabling them to point out which features are important for a class, rath er than provide new training instances. We first evaluate our algorithms against other feature labeling algorit hms under idealized conditions using feature labels generated by an oracle . In addition, another of our contributions is an evaluation of feature labeling algorithms under real world conditions using feature labels harvested from actual end users in our user study. Our user study is the first statistical user study for feature labeling invo lvi g a large number of end users (43 participants), all of whom have no background in machine learning. Our supervised and semi-supervised algorithms were among the best perfo rmers when compared to other feature labeling algorithms in the idealized setting and they are also robust to poor quality feature labels provided by ordinary end u sers in our study. We also perform an analysis to investigate the relative gains of incorporatin g the different sources of knowledge available in the labeled training set, the feature labels and the unlabeled data. Together, our results strongly suggest that feature labeling by e d users is both viable and effective for allowing end users to improve the learnin g algorithm behind their customized applicati ons.  Corresponding author E-mail address: dassh@eecs.oregonstate.edu (Shubhomoy Das, 1148 Kelley Engineering Center, Corvallis, OR 97331-5501, USA, Ph: 1-541-908-6949). 1 Early versions of portions of this work appeared in [36, 37] S. Das, T. Moore, W-K. Wong et. al./Artificial Intelligence
A survey of wireless sensor technologies applied to precision agriculture

This paper gives a state-of-art of wireless sensor network (WSN) technologies and solutions applied to precision agriculture (PA). The paper first considers applications and existing experiences that show how WSN technologies have been introduced in to agricultural applications. Then, a survey in hardware and software solutions is related with special emphasis on technological aspects. Finally, the paper shows how five networking and technological solutions may impact the next generation of sensors. These are (i) scalar wireless sensor networks, (ii) wireless multimedia sensor networks, (iii) Mobility of nodes, (iv) tag-based systems, and (v) smart-phone applications.
Spam filtering email classification (SFECM) using gain and graph mining algorithm

This paper proposes a hybrid solution of spam email classifier using context based email classification model as main algorithm complimented by information gain calculation to increase spam classification accuracy. Proposed solution consists of three stages email pre-processing, feature extraction and email classification. Research has found that LingerIG spam filter is highly effective at separating spam emails from cluster of homogenous work emails. Also experiment result proved the accuracy of spam filtering is 100% as recorded by the team of developers at University of Sydney. The study has shown that implementing the spam filter in the context-based email classification model is feasible. Experiment of the study has confirmed that spam filtering aspect of context-based classification model can be improved.
CodeSum: Translate Program Language to Natural Language

During software maintenance, programmers spend a lot of time on code comprehension. Reading comments is an effective way for programmers to reduce the reading and navigating time when comprehending source code. Therefore, as a critical task in software engineering, code summarization aims to generate brief natural language descriptions for source code. In this paper, we propose a new code summarization model named CodeSum. CodeSum exploits the attention-based sequence-to-sequence (Seq2Seq) neural network with Structure-based Traversal (SBT) of Abstract Syntax Trees (AST). The AST sequences generated by SBT can better present the structure of ASTs and keep unambiguous. We conduct experiments on three large-scale corpora in different program languages, i.e., Java, C#, and SQL, in which Java corpus is our new proposed industry code extracted from Github. Experimental results show that our method CodeSum outperforms the state-of-the-art significantly.  Source code summarization is the task of creating readable natural language summaries that describe the functionality of software. It is important in the field of source code comprehension. During the software maintenance, programmers spend a lot of time reading and understanding the source code snippets to comprehend them. Studies of program comprehension indicate that programmers often read a summary which is a comment describing the function of the code (e.g., JavaDoc1 descriptions for Java methods) or skim source code (e.g., read important keywords) to save time (Rodeghero et al. 2014; Sim, Clarke, and Holt 1998). For example, Figure 1 shows a Java method named toIndexName extracted from Github2. Through the summary and name of the method, developers can easily understand the method aiming to “convert the index of an attacker into a readable name in a battle”. However, these summaries are sometimes missing, incomplete or outdated. Therefore, automated source code summarization becomes an emerging technology in software engineering. Predicting these source code summarizations can be used in improving code search by natural language queries, code comprehension, and code
Spectrogram Enhancement Using Multiple Window Savitzky-Golay (MWSG) Filter for Robust Bird Sound Detection

Bird sound detection from real-field recordings is essential for identifying bird species in bioacoustic monitoring. Variations in the recording devices, environmental conditions, and the presence of vocalizations from other animals make the bird sound detection very challenging. In order to overcome these challenges, we propose an unsupervised algorithm comprising two main stages. In the first stage, a spectrogram enhancement technique is proposed using a multiple window Savitzky-Golay MWSG filter. We show that the spectrogram estimate using MWSG filter is unbiased and has lower variance compared with its single window counterpart. It is known that bird sounds are highly structured in the time-frequency T-F plane. We exploit these cues of prominence of T-F activity in specific directions from the enhanced spectrogram, in the second stage of the proposed method, for bird sound detection. In this regard, we use a set of four moving average filters that when applied to the enhanced spectrogram, yield directional spectrograms that capture the direction specific information. We propose a thresholding scheme on the time varying energy profile computed from each of these directional spectrograms to obtain frame-level binary decisions of bird sound activity. These individual decisions are then combined to obtain the final decision. Experiments are performed with three different datasets, with varying recording and noise conditions. Frame level F-score is used as the evaluation metric for bird sound detection. We find that the proposed method, on average, achieves higher F-score $10.24\%$ relative compared to the best of the six baseline schemes considered in this work.
A 60-GHz Active Receiving Switched-Beam Antenna Array With Integrated Butler Matrix and GaAs Amplifiers

This paper presents for the first time a 60-GHz receiving switched-beam antenna on organic liquid crystal polymer (LCP) platform. A 4 × 1 quasi-Yagi array is incorporated with a 4 × 4 Butler matrix beamforming network and GaAs low-noise amplifiers on an LCP substrate. The active beam is controlled by GaAs single-pole-double-throw switches to access the four output states of the Butler matrix. The entire 4 × 1 active array is 1.4 cm × 1.75 cm and consumes 1.1 W of dc power. Successful comparisons of the measured and simulated results verify a working phased array with a return loss better than 10 dB across the frequency band of 56.7-63.7 GHz. A comparison of radiation patterns demonstrate beam steering of ±40° with a peak active gain of 27.5 dB. The combined antenna and receiver noise performance at 60 GHz exhibits an estimated merit G/T of -18.6 dB/K and noise figure of 5.4 dB.
FPGA implementation of polar code based encoder architecture

Polar codes, introduced by Arikan, achieves the capacity of symmetric channels with “low encoding and decoding complexity” for a large class of underlying channels. Recently, polar code has become the most favourable error correcting code in the viewpoint of information theory due to its property of channel achieving capacity. Although the fully parallel polar code based encoder architecture processes the bits in a fully parallel fashion but suffers with huge hardware complexity with increasing code length. As fully parallel polar code based architecture will cause logic complexity problem, while partial parallel polar code based architecture is limited by memory units of high-throughput applications. In this paper, efficient polar code based encoder architecture is designed and implemented on a FPGA using Vertex 5 for the polar encoding scheme. Here we analyse the encoding process of polar code based encoder architecture and propose a new architecture that is suitable for encoding long polar codes with less hardware complexity.