nils-herrmann/clean_scirepeval huggingface dataset:
An in-depth study of LTE: effect of network protocol and application behavior on performance

With lower latency and higher bandwidth than its predecessor 3G networks, the latest cellular technology 4G LTE has been attracting many new users. However, the interactions among applications, network transport protocol, and the radio layer still remain unexplored. In this work, we conduct an in-depth study of these interactions and their impact on performance, using a combination of active and passive measurements. We observed that LTE has significantly shorter state promotion delays and lower RTTs than those of 3G networks. We discovered various inefficiencies in TCP over LTE such as undesired slow start. We further developed a novel and lightweight passive bandwidth estimation technique for LTE networks. Using this tool, we discovered that many TCP connections significantly under-utilize the available bandwidth. On average, the actually used bandwidth is less than 50% of the available bandwidth. This causes data downloads to be longer, and incur additional energy overhead. We found that the under-utilization can be caused by both application behavior and TCP parameter setting. We found that 52.6% of all downlink TCP flows have been throttled by limited TCP receive window, and that data transfer patterns for some popular applications are both energy and network unfriendly. All these findings highlight the need to develop transport protocol mechanisms and applications that are more LTE-friendly.
Central Control Over Distributed Routing ( Extended Version ) http : / / fibbing . net

Centralizing routing decisions offers tremendous flexibility, but sacrifices the robustness of distributed protocols. In this paper, we present Fibbing, an architecture that achieves both flexibility and robustness through central control over distributed routing. Fibbing introduces fake nodes and links into an underlying link-state routing protocol, so that routers compute their own forwarding tables based on the augmented topology. Fibbing is expressive, and readily supports flexible load balancing, traffic engineering, and backup routes. Based on high-level forwarding requirements, the Fibbing controller computes a compact augmented topology and injects the fake components through standard routing-protocol messages. Fibbing works with any unmodified commercial routers speaking OSPF. Our experiments also show that it can scale to large networks with many forwarding requirements, introduces minimal overhead, and quickly reacts to network and controller failures.
Code clones in feature-oriented software product lines

Some limitations of object-oriented mechanisms are known to cause code clones (e.g., extension using inheritance). Novel programming paradigms such as feature-oriented programming (FOP) aim at alleviating these limitations. However, it is an open issue whether FOP is really able to avoid code clones or whether it even facilitates (FOP-related) clones. To address this issue, we conduct an empirical analysis on ten feature-oriented software product lines with respect to code cloning. We found that there is a considerable number of clones in feature-oriented software product lines and that a large fraction of these clones is FOP-related (i.e., caused by limitations of feature-oriented mechanisms). Based on our results, we initiate a discussion on the reasons for FOP-related clones and on how to cope with them. We show by means of examples how such clones can be removed by applying refactorings.
Empirical Evaluation of Smartphone Augmented Reality Browsers in an Urban Tourism Destination Context

Today, exposure to new and unfamiliar environments is a necessary part of daily life. Effective communication of location-based information through location-based services has become a key concern for cartographers, geographers, human-computer interaction and professional designers alike. Recently, much attention was directed towards Augmented Reality (AR) interfaces. Current research, however, focuses primarily on computer vision and tracking, or investigates the needs of urban residents, already familiar with their environment. Adopting a user-centred design approach, this paper reports findings from an empirical mobile study investigating how tourists acquire knowledge about an unfamiliar urban environment through AR browsers. Qualitative and quantitative data was used in the development of a framework that shifts the perspective towards a more thorough understanding of the overall design space for such interfaces. The authors analysis provides a frame of reference for the design and evaluation of mobile AR interfaces. The authors demonstrate the application of the framework with respect to optimization of current design of AR.
Joint optimization of signal constellation bit labeling for bit-interleaved coded modulation with iterative decoding

We optimize signal constellation and bit labeling for bit-interleaved coded modulation with iterative decoding (BICM-ID). Target is to minimize bit error rate floor and signal to noise ratio for the turbo cliff. Various optimal non-orthogonal 16-QAM mappings are presented. An improvement of 0.2 dB is shown compared to state of the art orthogonal QAM-constellation with approx. the same error floor. To obtain these results, we derive the probability density function of extrinsic L-values of the demapper for perfect a priori knowledge in closed form. This allows fast computation of mutual information in the EXIT chart.
Reinforcement Learning in Robotics: Applications and Real-World Challenges

In robotics, the ultimate goal of reinforcement learning is to endow robots with the ability to learn, improve, adapt and reproduce tasks with dynamically changing constraints based on exploration and autonomous learning. We give a summary of the state-of-the-art of reinforcement learning in the context of robotics, in terms of both algorithms and policy representations. Numerous challenges faced by the policy representation in robotics are identified. Three recent examples for the application of reinforcement learning to real-world robots are described: a pancake flipping task, a bipedal walking energy minimization task and an archery-based aiming task. In all examples, a state-of-the-art expectation-maximization-based reinforcement learning is used, and different policy representations are proposed and evaluated for each task. The proposed policy representations offer viable solutions to six rarely-addressed challenges in policy representations: correlations, adaptability, multi-resolution, globality, multi-dimensionality and convergence. Both the successes and the practical difficulties encountered in these examples are discussed. Based on insights from these particular cases, conclusions are drawn about the state-of-the-art and the future perspective directions for reinforcement learning in robotics.
Telecom customer segmentation based on cluster analysis

Telecom industry is a typical data-intensive industry where data mining applications will enable a good guidance on marketing strategies, and the cluster analysis can be used in customer segmentation. The paper makes a detailed and comprehensive analysis of classic K-means clustering algorithm, and proposes the modeling process for telecom customer segmentation according to the principle of the algorithm. A resolution of customer segmentation for the Changzhou telecom in Jiangsu province is proposed, and practical results show that the clustering technology based on the K-means method provides an effective and successful resolution of customer segmentation for Telecom, bringing services closer to the customers.
Massive hybrid antenna array for millimeter-wave cellular communications

A massive hybrid array consists of multiple analog subarrays, with each subarray having its digital processing chain. It offers the potential advantage of balancing cost and performance for massive arrays and therefore serves as an attractive solution for future millimeter-wave (mm- Wave) cellular communications. On one hand, using beamforming analog subarrays such as phased arrays, the hybrid configuration can effectively collect or distribute signal energy in sparse mm-Wave channels. On the other hand, multiple digital chains in the configuration provide multiplexing capability and more beamforming flexibility to the system. In this article, we discuss several important issues and the state-of-the-art development for mm-Wave hybrid arrays, such as channel modeling, capacity characterization, applications of various smart antenna techniques for single-user and multiuser communications, and practical hardware design. We investigate how the hybrid array architecture and special mm-Wave channel property can be exploited to design suboptimal but practical massive antenna array schemes. We also compare two main types of hybrid arrays, interleaved and localized arrays, and recommend that the localized array is a better option in terms of overall performance and hardware feasibility.
Automatic Scene Inference for 3D Object Compositing

We present a user-friendly image editing system that supports a drag-and-drop object insertion (where the user merely drags objects into the image, and the system automatically places them in 3D and relights them appropriately), postprocess illumination editing, and depth-of-field manipulation. Underlying our system is a fully automatic technique for recovering a comprehensive 3D scene model (geometry, illumination, diffuse albedo, and camera parameters) from a single, low dynamic range photograph. This is made possible by two novel contributions: an illumination inference algorithm that recovers a full lighting model of the scene (including light sources that are not directly visible in the photograph), and a depth estimation algorithm that combines data-driven depth transfer with geometric reasoning about the scene layout. A user study shows that our system produces perceptually convincing results, and achieves the same level of realism as techniques that require significant user interaction.
Identifying sports videos using replay, text, and camera motion features

Automated classiication of digital video is emerging as an important piece of the puzzle in the design of content management systems for digital libraries. The ability to classify videos into various classes such as sports, news, movies, or documentaries, increases the eeciency of indexing, browsing, and retrieval of video in large databases. In this paper, we discuss the extraction of features that enable identiication of sports videos directly from the compressed domain of MPEG video. These features include detecting the presence of action replays, determining the amount of scene text in video, and calculating various statistics on camera and/or object motion. The features are derived from the macroblock, motion, and bit-rate information that is readily accessible from MPEG video with very minimal decoding, leading to substantial gains in processing speeds. Full-decoding of selective frames is required only for text analysis. A decision tree classiier built using these features is able to identify sports clips with an accuracy of about 93%.
How, and why, process metrics are better

Defect prediction techniques could potentially help us to focus quality-assurance efforts on the most defect-prone files. Modern statistical tools make it very easy to quickly build and deploy prediction models. Software metrics are at the heart of prediction models; understanding how and especially why different types of metrics are effective is very important for successful model deployment. In this paper we analyze the applicability and efficacy of process and code metrics from several different perspectives. We build many prediction models across 85 releases of 12 large open source projects to address the performance, stability, portability and stasis of different sets of metrics. Our results suggest that code metrics, despite widespread use in the defect prediction literature, are generally less useful than process metrics for prediction. Second, we find that code metrics have high stasis; they dont change very much from release to release. This leads to stagnation in the prediction models, leading to the same files being repeatedly predicted as defective; unfortunately, these recurringly defective files turn out to be comparatively less defect-dense.
Interactive Proofs and the Hardness of Approximating Cliques

The contribution of this paper is two-fold. First, a connection is established between approximating the size of the largest clique in a graph and multi-prover interactive proofs. Second, an efficient multi-prover interactive proof for NP languages is constructed, where the verifier uses very few random bits and communication bits. Last, the connection between cliques and efficient multi-prover interaction proofs, is shown to yield hardness results on the complexity of approximating the size of the largest clique in a graph.
Of independent interest is our proof of correctness for the multilinearity test of functions.
Learning the Dynamics of Arterial Traffic From Probe Data Using a Dynamic Bayesian Network

Estimating and predicting traffic conditions in arterial networks using probe data has proven to be a substantial challenge. Sparse probe data represent the vast majority of the data available on arterial roads. This paper proposes a probabilistic modeling framework for estimating and predicting arterial travel-time distributions using sparsely observed probe vehicles. We introduce a model based on hydrodynamic traffic theory to learn the density of vehicles on arterial road segments, illustrating the distribution of delay within a road segment. The characterization of this distribution is essentially to use probe vehicles for traffic estimation: Probe vehicles report their location at random locations, and the travel times between location reports must be properly scaled to match the map discretization. A dynamic Bayesian network represents the spatiotemporal dependence on the network and provides a flexible framework to learn traffic dynamics from historical data and to perform real-time estimation with streaming data. The model is evaluated using data from a fleet of 500 probe vehicles in San Francisco, CA, which send Global Positioning System (GPS) data to our server every minute. The numerical experiments analyze the learning and estimation capabilities on a subnetwork with more than 800 links. The sampling rate of the probe vehicles does not provide detailed information about the location where vehicles encountered delay or the reason for any delay (i.e., signal delay, congestion delay, etc.). The model provides an increase in estimation accuracy of 35% when compared with a baseline approach to process probe-vehicle data.
Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs

Brain-computer interfaces (BCIs) have been gaining momentum in making human-computer interaction more natural, especially for people with neuro-muscular disabilities. Among the existing solutions the systems relying on electroencephalograms (EEG) occupy the most prominent place due to their non-invasiveness. However, the process of translating EEG signals into computer commands is far from trivial, since it requires the optimization of many different parameters that need to be tuned jointly. In this report, we focus on the category of EEG-based BCIs that rely on Steady-State-Visual-Evoked Potentials (SSVEPs) and perform a comparative evaluation of the most promising algorithms existing in the literature. More specifically, we define a set of algorithms for each of the various different parameters composing a BCI system (i.e. filtering, artifact removal, feature extraction, feature selection and classification) and study each parameter independently by keeping all other parameters fixed. The results obtained from this evaluation process are provided together with a dataset consisting of the 256-channel, EEG signals of 11 subjects, as well as a processing toolbox for reproducing the results and supporting further experimentation. In this way, we manage to make available for the community a state-of-the-art baseline for SSVEP-based BCIs that can be used as a basis for introducing novel methods and approaches.
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction

With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks. However, in chemistry data is inherently small and fragmented. In this work, we develop an approach of using rule-based knowledge for training ChemNet, a transferable and generalizable deep neural network for chemical property prediction that learns in a weak-supervised manner from large unlabeled chemical databases. When coupled with transfer learning approaches to predict other smaller datasets for chemical properties that it was not originally trained on, we show that ChemNet's accuracy outperforms contemporary DNN models that were trained using conventional supervised learning. Furthermore, we demonstrate that the ChemNet pre-training approach is equally effective on both CNN (Chemception) and RNN (SMILES2vec) models, indicating that this approach is network architecture agnostic and is effective across multiple data modalities. Our results indicate a pre-trained ChemNet that incorporates chemistry domain knowledge and enables the development of generalizable neural networks for more accurate prediction of novel chemical properties.
Semantic Mapping between Natural Language Questions and SQL Queries via Syntactic Pairing

Automatically mapping natural language into programming language semantics has always been a major and interesting challenge. In this paper, we approach such problem by carrying out mapping at syntactic level and then applying machine learning algorithms to derive an automatic translator of natural language questions into their associated SQL queries. For this purpose, we design a dataset of relational pairs containing syntactic trees of questions and queries and we encode them in Support Vector Machines by means of kernel functions. Pair classification experiments suggest that our approach is promising in deriving shared semantics between the languages above.
Music Mood and Theme Classification - a Hybrid Approach

Music perception is highly intertwined with both emotions and context. Not surprisingly, many of the users’ information seeking actions aim at retrieving music songs based on these perceptual dimensions – moods and themes, expressing how people feel about music or which situations they associate it with. In order to successfully support music retrieval along these dimensions, powerful methods are needed. Still, most existing approaches aiming at inferring some of the songs’ latent characteristics focus on identifying musical genres. In this paper we aim at bridging this gap between users’ information needs and indexed music features by developing algorithms for classifying music songs by moods and themes. We extend existing approaches by also considering the songs’ thematic dimensions and by using social data from the Last.fm music portal, as support for the classification tasks. Our methods exploit both audio features and collaborative user annotations, fusing them to improve overall performance. Evaluation performed against the AllMusic.com ground truth shows that both kinds of information are complementary and should be merged for enhanced classification accuracy.
A pilot study of cyber security and privacy related behavior and personality traits

Recent research has begun to focus on the factors that cause people to respond to phishing attacks as well as affect user behavior on social networks. This study examines the correlation between the Big Five personality traits and email phishing response. Another aspect examined is how these factors relate to users' tendency to share information and protect their privacy on Facebook (which is one of the most popular social networking sites).
 This research shows that when using a prize phishing email, neuroticism is the factor most correlated to responding to this email, in addition to a gender-based difference in the response. This study also found that people who score high on the openness factor tend to both post more information on Facebook as well as have less strict privacy settings, which may cause them to be susceptible to privacy attacks. In addition, this work detected no correlation between the participants estimate of being vulnerable to phishing attacks and actually being phished, which suggests susceptibility to phishing is not due to lack of awareness of the phishing risks and that real-time response to phishing is hard to predict in advance by online users.
 The goal of this study is to better understand the traits that contribute to online vulnerability, for the purpose of developing customized user interfaces and secure awareness education, designed to increase users' privacy and security in the future.
Power-Synchronization Control of Grid-Connected Voltage-Source Converters

In this paper, a novel control method of grid-connected voltage-source converters (VSCs) is proposed. The method can be generally applied for all grid-connected VSCs but may be of most importance in high-voltage dc (HVDC) applications. Different from the previous control methods, the proposed method utilizes the internal synchronization mechanism in ac systems, in principle, similar to the operation of a synchronous machine. By using this type of power-synchronization control, the VSC avoids the instability caused by a standard phase-locked loop in a weak AC-system connection. Moreover, a VSC terminal can give the weak ac system strong voltage support, just like a normal synchronous machine does. The control method is verified by both analytical models and time simulations.
Semantic Instance Segmentation with a Discriminative Loss Function

Semantic instance segmentation remains a challenging task. In this work we propose to tackle the problem with a discriminative loss function, operating at the pixel level, that encourages a convolutional network to produce a representation of the image that can easily be clustered into instances with a simple post-processing step. The loss function encourages the network to map each pixel to a point in feature space so that pixels belonging to the same instance lie close together while different instances are separated by a wide margin. Our approach of combining an offthe-shelf network with a principled loss function inspired by a metric learning objective is conceptually simple and distinct from recent efforts in instance segmentation. In contrast to previous works, our method does not rely on object proposals or recurrent mechanisms. A key contribution of our work is to demonstrate that such a simple setup without bells and whistles is effective and can perform onpar with more complex methods. Moreover, we show that it does not suffer from some of the limitations of the popular detect-and-segment approaches. We achieve competitive performance on the Cityscapes and CVPPP leaf segmentation benchmarks.
Determining Case in Arabic: Learning Complex Linguistic Behavior Requires Complex Linguistic Features

This paper discusses automatic determination of case in Arabic. This task is a major source of errors in full diacritization of Arabic. We use a gold-standard syntactic tree, and obtain an error rate of about 4.2%, with a machine learning based system outperforming a system using hand-written rules. A careful error analysis suggests that when we account for annotation errors in the gold standard, the error rate drops to 0.8%, with the hand-written rules outperforming the machine learning-based system.