nils-herrmann/clean_scirepeval huggingface dataset:
FakeMask: A Novel Privacy Preserving Approach for Smartphones

Users can enjoy personalized services provided by various context-aware applications that collect users' contexts through sensor-equipped smartphones. Meanwhile, serious privacy concerns arise due to the lack of privacy preservation mechanisms. Currently, most mechanisms apply passive defense policies in which the released contexts from a privacy preservation system are always real, leading to a great probability with which an adversary infers the hidden sensitive contexts about the users. In this paper, we apply a deception policy for privacy preservation and present a novel technique, FakeMask, in which fake contexts may be released to provably preserve users' privacy. The output sequence of contexts by FakeMask can be accessed by the untrusted context-aware applications or be used to answer queries from those applications. Since the output contexts may be different from the original contexts, an adversary has greater difficulty in inferring the real contexts. Therefore, FakeMask limits what adversaries can learn from the output sequence of contexts about the user being in sensitive contexts, even if the adversaries are powerful enough to have the knowledge about the system and the temporal correlations among the contexts. The essence of FakeMask is a privacy checking algorithm which decides whether to release a fake context for the current context of the user. We present a novel privacy checking algorithm and an efficient one to accelerate the privacy checking process. Extensive evaluation experiments on real smartphone context traces of users demonstrate the improved performance of FakeMask over other approaches.
Stability criteria for complex ecosystems

Forty years ago, May proved that sufficiently large or complex ecological networks have a probability of persisting that is close to zero, contrary to previous expectations. May analysed large networks in which species interact at random. However, in natural systems pairs of species have well-defined interactions (for example predator–prey, mutualistic or competitive). Here we extend May’s results to these relationships and find remarkable differences between predator–prey interactions, which are stabilizing, and mutualistic and competitive interactions, which are destabilizing. We provide analytic stability criteria for all cases. We use the criteria to prove that, counterintuitively, the probability of stability for predator–prey networks decreases when a realistic food web structure is imposed or if there is a large preponderance of weak interactions. Similarly, stability is negatively affected by nestedness in bipartite mutualistic networks. These results are found by separating the contribution of network structure and interaction strengths to stability. Stable predator–prey networks can be arbitrarily large and complex, provided that predator–prey pairs are tightly coupled. The stability criteria are widely applicable, because they hold for any system of differential equations.
Case for Caching and Model Predictive Control Quality Decision Algorithm for HTTP Adaptive Streaming: Is Cache-Awareness Actually Needed?

Presence of in-network opportunistic cache is known to cause undesirable effects on Quality of Experience when using HTTP Adaptive Streaming (HAS). While these effects on rate-based and buffer-based HAS algorithms have been studied, the recent near- optimal Model Predictive Control (MPC) has not yet been considered. We evaluate the impact of cache presence on MPC and show that caches may cause instabilities in common set-ups. To tackle this issue, we consider an idealized cache-aware extension to MPC and discuss its implementation. Our simulations show that overall cache- unaware MPC benefits from in-network caching with only video quality instability being increased, while cache-awareness brings noticeable improvement in this regard.
Oncomirs — microRNAs with a role in cancer

MicroRNAs (miRNAs) are an abundant class of small non-protein-coding RNAs that function as negative gene regulators. They regulate diverse biological processes, and bioinformatic data indicates that each miRNA can control hundreds of gene targets, underscoring the potential influence of miRNAs on almost every genetic pathway. Recent evidence has shown that miRNA mutations or mis-expression correlate with various human cancers and indicates that miRNAs can function as tumour suppressors and oncogenes. miRNAs have been shown to repress the expression of important cancer-related genes and might prove useful in the diagnosis and treatment of cancer.
How to Solve It: Modern Heuristics

The first € price and the £ and $ price are net prices, subject to local VAT. Prices indicated with * include VAT for books; the €(D) includes 7% for Germany, the €(A) includes 10% for Austria. Prices indicated with ** include VAT for electronic products; 19% for Germany, 20% for Austria. All prices exclusive of carriage charges. Prices and other details are subject to change without notice. All errors and omissions excepted. Z. Michalewicz, D.B. Fogel How to Solve It: Modern Heuristics
Implementation of Text clustering using Genetic Algorithm

Text clustering is an important area of interest in the field of Text summarization, sentiment analysis etc. There have been a lot of algorithms experimented during the past years, which have a wide range of performances. One of the most popular method used is k-means, where an initial assumption is made about k, which is the number of clusters to be generated. Now a new method is introduced where the number of clusters is found using a modified spectral bisection and then the output is given to a genetic algorithm where the final solution is obtained.  Cluster, Spectral Bisection, Genetic Algorithm, kmeans.
A Voronoi Diagram Based Classifier for Multiclass Imbalanced Data Sets

The imbalance problem is receiving an increasing attention in the literature. Studies in binary cases are recurrent, however there still are several real world problems with more than two classes. The known solutions for binary datasets may not be applicable in this case. Some efforts are being applied in decomposition techniques which transforms a multiclass problem into some binary problems. However it is also possible to face a multiclass problem with an ad hoc approach, i.e., a classifier able to handle all classes at once. In this work a method able to handle several classes is proposed. This new method is based on the Voronoi diagram. We try to dynamically divide the feature space into several regions, each one assigned to a different class. It is expected for the method to be able to construct a complex classification model. However, as it is in its beginning, some tests need to be performed in order to evaluate its feasibility. Experiments with some classical classifiers confirm its feasibility, and comparisons with ad hoc methods found in literature show its potentiality.
Continuous-time trajectory optimization for online UAV replanning

Multirotor unmanned aerial vehicles (UAVs) are rapidly gaining popularity for many applications. However, safe operation in partially unknown, unstructured environments remains an open question. In this paper, we present a continuous-time trajectory optimization method for real-time collision avoidance on multirotor UAVs. We then propose a system where this motion planning method is used as a local replanner, that runs at a high rate to continuously recompute safe trajectories as the robot gains information about its environment. We validate our approach by comparing against existing methods and demonstrate the complete system avoiding obstacles on a multirotor UAV platform.
Dynamic thermal management of air cooled data centers

Increases in server power dissipation time placed significant pressure on traditional data center thermal management systems. Traditional systems utilize computer room air conditioning (CRAC) units to pressurize a raised floor plenum with cool air that is passed to equipment racks via ventilation tiles distributed throughout the raised floor. Temperature is typically controlled at the hot air return of the CRAC units away from the equipment racks. Due primarily to a lack of distributed environmental sensing, these CRAC systems are often operated conservatively resulting in reduced computational density and added operational expense. This paper introduces a data center environmental control system that utilizes a distributed sensor network to manipulate conventional CRAC units within an air-cooled environment. The sensor network is attached to standard racks and provides a direct measurement of the environment in close proximity to the computational resources. A calibration routine is used to characterize the response of each sensor in the network to individual CRAC actuators. A cascaded control algorithm is used to evaluate the data from the sensor network and manipulate supply air temperature and flow rate from individual CRACs to ensure thermal management while reducing operational expense. The combined controller and sensor network has been deployed in a production data center environment. Results from the algorithm will be presented that demonstrate the performance of the system and evaluate the energy savings compared with conventional data center environmental control architecture
A Perspective on Bio-Mediated Material Structuring.

Bioinspiration, biomorphy, biomimicry, biomimetics, bionics, and biotemplating are terms used to describe the fabrication of materials or, more generally, systems to solve technological problems by abstracting, emulating, using, or transferring structures from biological paradigms. Herein, a brief overview of how the different terminologies are being typically applied is provided. It is proposed that there is a rich field of research that can be expanded by utilizing various novel approaches for the guidance of living organisms for "bio-mediated" material structuring purposes. As examples of contact-based or contact-free guidance, such as substrate patterning, the application of light, magnetic fields, or chemical gradients, potentially interesting methods of creating hierarchically structured monolithic engineering materials, using live patterned biomass, biofilms, or extracellular substances as scaffolds, are presented. The potential advantages of such materials are discussed, and examples of live self-patterning of materials are given.
Antagonism also Flows through Retweets: The Impact of Out-of-Context Quotes in Opinion Polarization Analysis

In this paper, we study the implications of the commonplace assumption that most social media studies make with respect to the nature of message shares (such as retweets) as a predominantly positive interaction. By analyzing two large longitudinal Brazilian Twitter datasets containing 5 years of conversations on two polarizing topics – Politics and Sports, we empirically demonstrate that groups holding antagonistic views can actually retweet each other more often than they retweet other groups. We show that assuming retweets as endorsement interactions can lead to misleading conclusions with respect to the level of antagonism among social communities, and that this apparent paradox is explained in part by the use of retweets to quote the original content creator out of the message’s original temporal context, for humor and criticism purposes. As a consequence, messages diffused on online media can have their polarity reversed over time, what poses challenges for social and computer scientists aiming to classify and track opinion groups on online media. On the other hand, we found that the time users take to retweet a message after it has been originally posted can be a useful signal to infer antagonism in social platforms, and that surges of out-of-context retweets correlate with sentiment drifts triggered by real-world events. We also discuss how such evidences can be embedded in sentiment analysis models.
Enhancing social collaborative filtering through the application of non-negative matrix factorization and exponential random graph models

Social collaborative filtering recommender systems extend the traditional user-to-item interaction with explicit user-to-user relationships, thereby allowing for a wider exploration of correlations among users and items, that potentially lead to better recommendations. A number of methods have been proposed in the direction of exploring the social network, either locally (i.e. the vicinity of each user) or globally. In this paper, we propose a novel methodology for collaborative filtering social recommendation that tries to combine the merits of both the aforementioned approaches, based on the soft-clustering of the Friend-of-a-Friend (FoaF) network of each user. This task is accomplished by the non-negative factorization of the adjacency matrix of the FoaF graph, while the edge-centric logic of the factorization algorithm is ameliorated by incorporating more general structural properties of the graph, such as the number of edges and stars, through the introduction of the exponential random graph models. The preliminary results obtained reveal the potential of this idea.
The value of acupuncture in cancer care.

Clinical research on acupuncture in cancer care is a new and challenging field in oncology. The results of clinical research will continue to provide clinically relevant answers for patients and oncologists. The evidence currently available has suggested that acupuncture is a safe and effective therapy to manage cancer and treatment related symptoms, while giving patients the ability to actively participate in their own care plan. The article explains the potential benefits of acupuncture and describes the difficulties in studying its effectiveness.
Light-Field Surface Color Segmentation with an Application to Intrinsic Decomposition

To enable light fields of large environments to be captured, they would have to be sparse, i.e. with a relatively large distance between views. Such sparseness, however, causes subsequent processing to be much more difficult than would be the case with dense light fields. This includes segmentation. In this paper, we address the problem of meaningful segmentation of a sparse planar light field, leading to segments that are coherent between views. In addition, uniquely our method does not make the assumption that all surfaces in the environment are perfect Lambertian reflectors, which further broadens its applicability. Our fully automatic segmentation pipeline leverages scene structure, and does not require the user to navigate through the views to fix inconsistencies. The key idea is to combine coarse estimations given by an over-segmentation of the scene into super-rays, with detailed ray-based processing. We show the merit of our algorithm by means of a novel way to perform intrinsic light field decomposition, outperforming state-of-the-art methods.
Regression testing of web applications using Record/Replay tools

Software engineers often use record/replay tools to enable the automated testing of web applications. Tests created in this man- ner can then be used to regression test new versions of the web applications as they evolve. Web application tests recorded by record/replay tools, however, can be quite brittle; they can easily break as applications change. For this reason, researchers have be- gun to seek approaches for automatically repairing record/replay tests. This research investigates different aspects in relation to test- ing web applications using record/replay tools. The areas that we are interested in include taxonomizing the causes behind breakages and developing automated techniques to repair breakages, creating prevention techniques to stop the occurrence of breakages and de- veloping automated frameworks for root cause analysis. Finally, we intend to evaluate all of these activities via controlled studies involving software engineers and real web application tests.
How to use real-valued sparse recovery algorithms for complex-valued sparse recovery?

Finding the sparse solution of an underdetermined system of linear equations (the so called sparse recovery problem) has been extensively studied in the last decade because of its applications in many different areas. So, there are now many sparse recovery algorithms (and program codes) available. However, most of these algorithms have been developed for real-valued systems. This paper discusses an approach for using available real-valued algorithms (or program codes) to solve complex-valued problems, too. The basic idea is to convert the complex-valued problem to an equivalent real-valued problem and solve this new real-valued problem using any real-valued sparse recovery algorithm. Theoretical guarantees for the success of this approach will be discussed, too. On the other hand, a widely used sparse recovery idea is finding the minimum ℓ1 norm solution. For real-valued systems, this idea requires to solve a linear programming (LP) problem, but for complex-valued systems it needs to solve a second-order cone programming (SOCP) problem, which demands more computational load. However, based on the approach of this paper, the complex case can also be solved by linear programming, although the theoretical guarantee for finding the sparse solution is more limited.
Automatic multiviewface detection and pose estimationfrom videos based on mixture-of-trees model and optical flow

Face detection is an important task in the field of computer vision, which is widely used in the field of security, human-machine interaction, identity recognition, and etc. Many existing methods are developed for image based face pose estimation, but few of them can be directly extended to videos. However, video-based face pose estimation is much more important and frequently used in real applications. This paper describes a method of automatic face pose estimation from videos based on mixture-of-trees model and optical flow. Unlike the traditional mixture-of-trees model, which may easily incur errors in losing faces or with wrong angles for a sequence of faces in video, our method is much more robust by considering the spatio-temporal consistency on the face pose estimation for video. To preserve the spatio-temporal consistency from one frame to the next, this method employs an optical flow on the video to guide the face pose estimation based on mixture-of-trees. Our method is extensively evaluated on videos including different faces and with different pose angles. Both visual and statistics results demonstrated its effectiveness on automatic face pose estimation.
A compact microstrip antenna with tapered peripheral slits for CubeSat RF Payloads at 436MHz: Miniaturization techniques, design & numerical results

We elaborate the design and simulation of a planar antenna that is suitable for CubeSat picosatellites. The antenna operates at 436 MHz and its main features are miniature size and the built-in capability to produce circular polarization. The miniaturization procedure is given in detail, and the electrical performance of this small antenna is documented. Two main miniaturization techniques have been applied, i.e. dielectric loading and distortion of the current path. We have added an extra degree of freedom to the latter. The radiator is integrated with the chassis of the picosatellite and, at the same time, operates at the lower end of the UHF spectrum. In terms of electrical size, the structure presented herein is one of the smallest antennas that have been proposed for small satellites. Despite its small electrical size, the antenna maintains acceptable efficiency and gain performance in the band of interest.
Ring Resonator Bandpass Filter With Switchable Bandwidth Using Stepped-Impedance Stubs

This paper presents a novel ring resonator bandpass filter (BPF) with switchable passband bandwidth. Simple techniques for analyzing resonant frequencies and transmission zeros are shown, and the methods of designing the lengths of open, stepped-impedance stubs attached to the ring are introduced to obtain the tunability of the bandwidth. Interdigital-coupled feed lines are used for harmonic suppression by adding transmission zeros outside of the passband, resulting in wide upper and lower stopbands. After the lengths of the open stubs and the design of the feed lines are decided, the means of producing the bandwidth tunability are analyzed. By changing the characteristic impedances of the open stubs, mid-upper or mid-lower passband bandwidths can be separately varied. Simultaneous increases or decreases of each width of the stepped open stubs change the number of resonances, resulting in wide or narrow passband bandwidths. The measured results of the BPF using PIN diodes show that the tunable passband ratio is 1.22:1.13:1 with a maximum insertion loss of 1.35 dB.
A threshold based approach to technical debt

Nearly two decades ago, Ward Cunningham introduced us to the term "technical debt" as a means of describing the long term costs associated with a suboptimal software design and implementation. For most programs, especially those with a large legacy code baseline, achieving zero absolute debt is an unnecessary and unrealistic goal. It is important to recall that a primary reason for managing and eliminating debt is to drive down maintenance costs and to reduce defects. A sufficiently low, manageable level of debt can minimize the long-term impact, i.e., "low debt interest payments". In this article, we define an approach for establishing program specific thresholds to define manageable levels of technical debt.
Adaptive Diffusions for Scalable Learning Over Graphs

Diffusion-based classifiers such as those relying on the Personalized PageRank and the heat kernel enjoy remarkable classification accuracy at modest computational requirements. Their performance however is affected by the extent to which the chosen diffusion captures a typically unknown label propagation mechanism, which can be specific to the underlying graph, and potentially different for each class. This paper introduces a disciplined, data-efficient approach to learning class-specific diffusion functions adapted to the underlying network topology. The novel learning approach leverages the notion of “landing probabilities” of class-specific random walks, which can be computed efficiently, thereby ensuring scalability to large graphs. This is supported by rigorous analysis of the properties of the model as well as the proposed algorithms. Furthermore, a robust version of the classifier facilitates learning even in noisy environments. Classification tests on real networks demonstrate that adapting the diffusion function to the given graph and observed labels significantly improves the performance over fixed diffusions, reaching—and many times surpassing—the classification accuracy of computationally heavier state-of-the-art competing methods, which rely on node embeddings and deep neural networks.
Testing Graph Connectivity

An algorithm proposed by Dinic for finding maximum flows in networks and by Hopcroft and Karp for finding maximum bipartite matchings is applied to graph connectivity problems. It is shown that the algorithm requires 0(V<supscrpt>1/2</supscrpt>E) time to find a maximum set of node-disjoint paths in a graph, and 0(V<supscrpt>2/3</supscrpt>E) time to find a maximum set of edge disjoint paths. These bounds are tight. Thus the node connectivity of a graph may be tested in 0(V<supscrpt>5/2</supscrpt>E) time, and the edge connectivity of a graph may be tested in 0(V<supscrpt>5/3</supscrpt>E) time.