nils-herrmann/clean_scirepeval huggingface dataset:
Localization for mobile sensor networks

Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, in this paper we introduce the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization. Our approach does not require additional hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. We analyze the properties of our technique and report experimental results from simulations. Our scheme outperforms the best known static localization schemes under a wide range of conditions.
Minimum Error Rate Training in Statistical Machine Translation

Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training criteria which directly optimize translation quality. These training criteria make use of recently proposed automatic evaluation metrics. We describe a new algorithm for efficient training an unsmoothed error count. We show that significantly better results can often be obtained if the final evaluation criterion is taken directly into account as part of the training procedure.
Analysis of H.264 Bitstream Prioritization for Dual TCP/UDP Streaming of HD video over WLANs

Flexible Dual-TCP/UDP Streaming Protocol (FDSP) is a new method for streaming H.264-encoded HD video over wireless networks. The method takes advantage of the hierarchical structure of H.264/AVC syntax and uses TCP to transmit important syntax elements of H.264/AVC video and UDP to transmit less important elements. FDSP was shown to outperform pure-UDP streaming in visual quality and pure-TCP streaming in delay. In this work, FDSP is expanded to include a new parameter called Bitstream Prioritization (BP). The newly modified algorithm, FDSP-BP, is analyzed to measure the impact of BP on the quality of streaming for partially and fully congested networks. Our analysis shows that FDSP-BP is superior to pure-TCP streaming methods with respect to rebuffering instances, while still maintaining high visual quality.
Challenges in software evolution

Today's information technology society increasingly relies on software at all levels. Nevertheless, software quality generally continues to fall short of expectations, and software systems continue to suffer from symptoms of aging as they are adapted to changing requirements and environments. The only way to overcome or avoid the negative effects of software aging is by placing change and evolution in the center of the software development process. In this article we describe what we believe to be some of the most important research challenges in software evolution. The goal of this document is to provide novel research directions in the software evolution domain.
Data Loss and Reconstruction in Wireless Sensor Networks

Reconstructing the environment by sensory data is a fundamental operation for understanding the physical world in depth. A lot of basic scientific work (e.g., nature discovery, organic evolution) heavily relies on the accuracy of environment reconstruction. However, data loss in wireless sensor networks is common and has its special patterns due to noise, collision, unreliable link, and unexpected damage, which greatly reduces the reconstruction accuracy. Existing interpolation methods do not consider these patterns and thus fail to provide a satisfactory accuracy when the missing data rate becomes large. To address this problem, this paper proposes a novel approach based on compressive sensing to reconstruct the massive missing data. Firstly, we analyze the real sensory data from Intel Indoor, GreenOrbs, and Ocean Sense projects. They all exhibit the features of low-rank structure, spatial similarity, temporal stability and multi-attribute correlation. Motivated by these observations, we then develop an environmental space time improved compressive sensing (ESTI-CS) algorithm with a multi-attribute assistant (MAA) component for data reconstruction. Finally, extensive simulation results on real sensory datasets show that the proposed approach significantly outperforms existing solutions in terms of reconstruction accuracy.
Generalized vulnerability extrapolation using abstract syntax trees

The discovery of vulnerabilities in source code is a key for securing computer systems. While specific types of security flaws can be identified automatically, in the general case the process of finding vulnerabilities cannot be automated and vulnerabilities are mainly discovered by manual analysis. In this paper, we propose a method for assisting a security analyst during auditing of source code. Our method proceeds by extracting abstract syntax trees from the code and determining structural patterns in these trees, such that each function in the code can be described as a mixture of these patterns. This representation enables us to decompose a known vulnerability and extrapolate it to a code base, such that functions potentially suffering from the same flaw can be suggested to the analyst. We evaluate our method on the source code of four popular open-source projects: LibTIFF, FFmpeg, Pidgin and Asterisk. For three of these projects, we are able to identify zero-day vulnerabilities by inspecting only a small fraction of the code bases.
Design of an adaptive sliding mode controller for a novel spherical rolling robot

This paper presents a novel spherical rolling robot and the design procedure of an adaptive sliding mode controller for the proposed robot. Towards containing the uncertainties, the sliding mode controller is utilized and simulation tests represent the effectiveness of this method. However, the chattering phenomenon in the robot operation is the main insufficiency of this controller. Hence, in order to present a more accurate controller, sliding mode controller is equipped with an identification method which works in an online manner for identifying the exact model of the robot. The provided identification procedure is based on Recursive Least Square approach, which is one of the most promising approaches toward identifying the model. It should be also noted that, this adaptive controller identify the model without taking into account any data from the derived model.
A Fast Floor Segmentation Algorithm for Visual-Based Robot Navigation

We present a novel technique that robustly segments free-space for robot navigation purposes. In particular, we are interested in a reactive visual navigation, in which the rapid and accurate detection of free space where the robot can navigate is crucial. Contrary to existing methods that use multiple cameras in different configurations, we use a downward-facing monocular camera to search for free space in a large and complicated room environment. The proposed approach combines two techniques. First, we apply the Simple Linear Iterative Clustering super-pixel algorithm to the input images. Then, by relying on particular characteristics of floor superpixels, we use a simple classification method based on a normalized SSD similarity measure to group together those superpixels that belongs to the floor (considered as free space). The method intermittently examines low resolution images (80 × 60) in the CIE Lab color model. Experimental results show that our segmentation approach is robust, even in the presence of severe specular reflections and allows for real-time navigation.
MixFab: a mixed-reality environment for personal fabrication

Personal fabrication machines, such as 3D printers and laser cutters, are becoming increasingly ubiquitous. However, designing objects for fabrication still requires 3D modeling skills, thereby rendering such technologies inaccessible to a wide user-group. In this paper, we introduce MixFab, a mixed-reality environment for personal fabrication that lowers the barrier for users to engage in personal fabrication. Users design objects in an immersive augmented reality environment, interact with virtual objects in a direct gestural manner and can introduce existing physical objects effortlessly into their designs. We describe the design and implementation of MixFab, a user-defined gesture study that informed this design, show artifacts designed with the system and describe a user study evaluating the system's prototype.
Statistical Arbitrage Mining for Display Advertising

We study and formulate arbitrage in display advertising. Real-Time Bidding (RTB) mimics stock spot exchanges and utilises computers to algorithmically buy display ads per impression via a real-time auction. Despite the new automation, the ad markets are still informationally inefficient due to the heavily fragmented marketplaces. Two display impressions with similar or identical effectiveness (e.g., measured by conversion or click-through rates for a targeted audience) may sell for quite different prices at different market segments or pricing schemes. In this paper, we propose a novel data mining paradigm called Statistical Arbitrage Mining (SAM) focusing on mining and exploiting price discrepancies between two pricing schemes. In essence, our SAMer is a meta-bidder that hedges advertisers' risk between CPA (cost per action)-based campaigns and CPM (cost per mille impressions)-based ad inventories; it statistically assesses the potential profit and cost for an incoming CPM bid request against a portfolio of CPA campaigns based on the estimated conversion rate, bid landscape and other statistics learned from historical data. In SAM, (i) functional optimisation is utilised to seek for optimal bidding to maximise the expected arbitrage net profit, and (ii) a portfolio-based risk management solution is leveraged to reallocate bid volume and budget across the set of campaigns to make a risk and return trade-off. We propose to jointly optimise both components in an EM fashion with high efficiency to help the meta-bidder successfully catch the transient statistical arbitrage opportunities in RTB. Both the offline experiments on a real-world large-scale dataset and online A/B tests on a commercial platform demonstrate the effectiveness of our proposed solution in exploiting arbitrage in various model settings and market environments.
Persistent Homology: An Introduction and a New Text Representation for Natural Language Processing

Persistent homology is a mathematical tool from topological data analysis. It performs multi-scale analysis on a set of points and identifies clusters, holes, and voids therein. These latter topological structures complement standard feature representations, making persistent homology an attractive feature extractor for artificial intelligence. Research on persistent homology for AI is in its infancy, and is currently hindered by two issues: the lack of an accessible introduction to AI researchers, and the paucity of applications. In response, the first part of this paper presents a tutorial on persistent homology specifically aimed at a broader audience without sacrificing mathematical rigor. The second part contains one of the first applications of persistent homology to natural language processing. Specifically, our Similarity Filtration with Time Skeleton (SIFTS) algorithm identifies holes that can be interpreted as semantic “tie-backs” in a text document, providing a new document structure representation. We illustrate our algorithm on documents ranging from nursery rhymes to novels, and on a corpus with child and adolescent writings.
Maximum Entropy Inverse Reinforcement Learning

Recent research has shown the benefit of framing problems of imitation learning as solutions to Markov Decision Problems. This approach reduces learning to the problem of recovering a utility function that makes the behavior induced by a near-optimal policy closely mimic demonstrated behavior. In this work, we develop a probabilistic approach based on the principle of maximum entropy. Our approach provides a well-defined, globally normalized distribution over decision sequences, while providing the same performance guarantees as existing methods. We develop our technique in the context of modeling realworld navigation and driving behaviors where collected data is inherently noisy and imperfect. Our probabilistic approach enables modeling of route preferences as well as a powerful new approach to inferring destinations and routes based on partial trajectories.
On the statistics of natural stochastic textures and their application in image processing

Statistics of natural images has become an important subject of research in recent years. The highly kurtotic, non-Gaussian, statistics known to be characteristic of many natural images are exploited in various image processing tasks. In this paper, we focus on natural stochastic textures (NST) and substantiate our finding that NST have Gaussian statistics. Using the well-known statistical self-similarity property of natural images, exhibited even more profoundly in NST, we exploit a Gaussian self-similar process known as the fractional Brownian motion, to derive a fBm-PDE-based singleimage superresolution scheme for textured images. Using the same process as a prior, we also apply it in denoising of NST.
Emergence of Fatal PRRSV Variants: Unparalleled Outbreaks of Atypical PRRS in China and Molecular Dissection of the Unique Hallmark

Porcine reproductive and respiratory syndrome (PRRS) is a severe viral disease in pigs, causing great economic losses worldwide each year. The causative agent of the disease, PRRS virus (PRRSV), is a member of the family Arteriviridae. Here we report our investigation of the unparalleled large-scale outbreaks of an originally unknown, but so-called "high fever" disease in China in 2006 with the essence of PRRS, which spread to more than 10 provinces (autonomous cities or regions) and affected over 2,000,000 pigs with about 400,000 fatal cases. Different from the typical PRRS, numerous adult sows were also infected by the "high fever" disease. This atypical PRRS pandemic was initially identified as a hog cholera-like disease manifesting neurological symptoms (e.g., shivering), high fever (40-42 degrees C), erythematous blanching rash, etc. Autopsies combined with immunological analyses clearly showed that multiple organs were infected by highly pathogenic PRRSVs with severe pathological changes observed. Whole-genome analysis of the isolated viruses revealed that these PRRSV isolates are grouped into Type II and are highly homologous to HB-1, a Chinese strain of PRRSV (96.5% nucleotide identity). More importantly, we observed a unique molecular hallmark in these viral isolates, namely a discontinuous deletion of 30 amino acids in nonstructural protein 2 (NSP2). Taken together, this is the first comprehensive report documenting the 2006 epidemic of atypical PRRS outbreak in China and identifying the 30 amino-acid deletion in NSP2, a novel determining factor for virulence which may be implicated in the high pathogenicity of PRRSV, and will stimulate further study by using the infectious cDNA clone technique.
Automatic Topic Discovery for Multi-Object Tracking

This paper proposes a new approach to multi-object tracking by semantic topic discovery. We dynamically cluster frame-by-frame detections and treat objects as topics, allowing the application of the Dirichlet Process Mixture Model (DPMM). The tracking problem is cast as a topic-discovery task where the video sequence is treated analogously to a document. This formulation addresses tracking issues such as object exclusivity constraints as well as cannot-link constraints which are integrated without the need for heuristic thresholds. The video is temporally segmented into epochs to model the dynamics of word (superpixel) co-occurrences and to model the temporal damping effect. In experiments on public data sets we demonstrate the effectiveness of the proposed algorithm.
DTC-SVM Scheme for Induction Motors Fedwith a Three-level Inverter

Direct Torque Control is a control technique in AC drive systems to obtain high performance torque control. The conventional DTC drive contains a pair of hysteresis comparators. DTC drives utilizing hysteresis comparators suffer from high torque ripple and variable switching frequency. The most common solution to those problems is to use the space vector depends on the reference torque and flux. In this Paper The space vector modulation technique (SVPWM) is applied to 2 level inverter control in the proposed DTC-based induction motor drive system, thereby dramatically reducing the torque ripple. Then the controller based on space vector modulation is designed to be applied in the control of Induction Motor (IM) with a three-level Inverter. This type of Inverter has several advantages over the standard two-level VSI, such as a greater number of levels in the output voltage waveforms, Lower dV/dt, less harmonic distortion in voltage and current waveforms and lower switching frequencies. This paper proposes a general SVPWM algorithm for three-level based on standard two-level SVPWM. The proposed scheme is described clearly and simulation results are reported to demonstrate its effectiveness. The entire control scheme is implemented with Matlab/Simulink. Direct torque control, space vector Pulsewidth modulation(SVPWM), neutral point clamped(NPC), two-level inverter.
Incorporating Loose-Structured Knowledge into LSTM with Recall Gate for Conversation Modeling

Modeling human conversations is the essence for building satisfying chat-bots with multi-turn dialog ability. Conversation modeling will notably benefit from domain knowledge since the relationships between sentences can be clarified due to semantic hints introduced by knowledge. In this paper, a deep neural network is proposed to incorporate background knowledge for conversation modeling. Through a specially designed Recall gate, domain knowledge can be transformed into the extra global memory of Long Short-Term Memory (LSTM), so as to enhance LSTM by cooperating with its local memory to capture the implicit semantic relevance between sentences within conversations. In addition, this paper introduces the loose structured domain knowledge base, which can be built with slight amount of manual work and easily adopted by the Recall gate. Our model is evaluated on the context-oriented response selecting task, and experimental results on both two datasets have shown that our approach is promising for modeling human conversations and building key components of automatic chatting systems.
Risk and risk management in software projects: A reassessment

0164-1212/$ see front matter 2008 Elsevier Inc. A doi:10.1016/j.jss.2008.03.059 E-mail address: paul.bannerman@nicta.com.au Controlling risk in software projects is considered to be a major contributor to project success. This paper reconsiders the status of risk and risk management in the literature and practice. The analysis is supported by a study of risk practices in government agencies in an Australian State, contributing to a gap in research in the public sector. It is found that risk is narrowly conceived in research, and risk management is under-performed in practice. The findings challenge some conventional conceptions of risk management and project management. For example, it was found that software projects do not conform to a uniform structure, as assumed in much of the literature. This introduces variations in the risk and project management challenges they face. Findings also suggest that formal project management is neither necessary nor sufficient for project success. It is concluded that risk management research lags the needs of practice, and risk management as practiced lags the prescriptions of research. Implications and directions for future research and practice are discussed.
Massively Parallel NUMA-aware Hash Joins

Driven by the two main hardware trends increasing main memory and massively parallel multi-core processing in the past few years, there has been much research e ort in parallelizing well-known join algorithms. However, the non-uniform memory access (NUMA) of these architectures to main memory has only gained limited attention in the design of these algorithms. We study recent proposals of main memory hash join implementations and identify their major performance problems on NUMA architectures. We then develop a NUMA-aware hash join for massively parallel environments, and show how the speci c implementation details a ect the performance on a NUMA system. Our experimental evaluation shows that a carefully engineered hash join implementation outperforms previous high performance hash joins by a factor of more than two, resulting in an unprecedented throughput of 3/4 billion join argument tuples per second.
A New Marketing Paradigm for Electronic Commerce

The World Wide Web possesses unique characteristics which distinguish it in important ways from traditional commercial communications environments. Because the Web presents a fundamentally different environment for marketing activities than traditional media, conventional marketing activities are becoming transformed, as they are often difficult to implement in their present form. In this paper, we assert that these changes portend an evolution in the "marketing concept" and argue that in order for marketing efforts to be successful in this new medium, a new business paradigm is required in which the marketing function is reconstructed to facilitate electronic commerce in the emerging electronic society underlying the Web.
Compact Thermal Model for Vertical Nanowire Phase-Change Memory Cells

We introduce a compact model for the temperature distribution in cylindrical nanowire (NW) phase-change memory (PCM) cells for both transient (nanoseconds) and steady-state time scales. The model takes advantage of the symmetry of the cell to efficiently calculate temperature distribution dependence on geometry and material/interface properties. The results are compared with data from the literature and with finite-element simulations, showing improved computation speed by two orders of magnitude. Programming current sensitivity to cell dimensions and material properties is investigated, indicating that NW diameter (D) and thermal boundary resistance (TBR) play the strongest role in enhancing PCM energy efficiency.
Exploiting Emoticons in Polarity Classification of Text

With people increasingly using emoticons in written text on the Web in order to express, stress, or disambiguate their sentiment, it is crucial for automated sentiment analysis tools to correctly account for such graphical cues for sentiment. We analyze how emoticons typically convey sentiment and we subsequently propose and evaluate a novel method for exploiting this with a manually created emoticon sentiment lexicon in a lexicon-based polarity classification method. We evaluate our approach on 2,080 Dutch tweets and forum messages, which all contain emoticons. We validate our findings on 10,069 English reviews of apps, some of which contain emoticons. We find that accounting for the sentiment conveyed by emoticons on a paragraph level – and, to a lesser extent, on a sentence level – significantly improves polarity classification performance. Whenever emoticons are used, their associated sentiment tends to dominate the sentiment conveyed by textual cues and forms a good proxy for the polarity of text.