nils-herrmann/clean_scirepeval huggingface dataset:
Efficiently evaluating complex boolean expressions

The problem of efficiently evaluating a large collection of complex Boolean expressions - beyond simple conjunctions and Disjunctive/Conjunctive Normal Forms (DNF/CNF) - occurs in many emerging online advertising applications such as advertising exchanges and automatic targeting. The simple solution of normalizing complex Boolean expressions to DNF or CNF form, and then using existing methods for evaluating such expressions is not always effective because of the exponential blow-up in the size of expressions due to normalization. We thus propose a novel method for evaluating complex expressions, which leverages existing techniques for evaluating leaf-level conjunctions, and then uses a bottom-up evaluation technique to only process the relevant parts of the complex expressions that contain the matching conjunctions. We develop two such bottom-up evaluation techniques, one based on Dewey IDs and another based on mapping Boolean expressions to one-dimensional intervals. Our experimental evaluation based on data obtained from an online advertising exchange shows that the proposed techniques are efficient and scalable, both with respect to space usage as well as evaluation time.
TRESOR-HUNT: attacking CPU-bound encryption

Hard disk encryption is known to be vulnerable to a number of attacks that aim to directly extract cryptographic key material from system memory. Several approaches to preventing this class of attacks have been proposed, including Tresor [18] and LoopAmnesia . The common goal of these systems is to confine the encryption key and encryption process itself to the CPU, such that sensitive key material is never released into system memory where it could be accessed by a DMA attack.
 In this work, we demonstrate that these systems are nevertheless vulnerable to such DMA attacks. Our attack, which we call Tresor-Hunt, relies on the insight that DMA-capable adversaries are not restricted to simply reading physical memory, but can write arbitrary values to memory as well. Tresor-Hunt leverages this insight to inject a ring 0 attack payload that extracts disk encryption keys from the CPU into the target system's memory, from which it can be retrieved using a normal DMA transfer.
 Our implementation of this attack demonstrates that it can be constructed in a reliable and OS-independent manner that is applicable to any CPU-bound encryption technique, IA32-based system, and DMA-capable peripheral bus. Furthermore, it does not crash the target system or otherwise significantly compromise its integrity. Our evaluation supports the OS-independent nature of the attack, as well as its feasibility in real-world scenarios. Finally, we discuss several countermeasures that might be adopted to mitigate this attack and render CPU-bound encryption systems viable.
Drag Analysis of an Aircraft Wing Model with and without Bird Feather like Winglet

This work describes the aerodynamic characteristic for aircraft wing model with and without bird feather like winglet. The aerofoil used to construct the whole structure is NACA 653-218 Rectangular wing and this aerofoil has been used to compare the result with previous research using winglet. The model of the rectangular wing with bird feather like winglet has been fabricated using polystyrene before design using CATIA P3 V5R13 software and finally fabricated in wood. The experimental analysis for the aerodynamic characteristic for rectangular wing without winglet, wing with horizontal winglet and wing with 60 degree inclination winglet for Reynolds number 1.66×10, 2.08×10 and 2.50×10 have been carried out in open loop low speed wind tunnel at the Aerodynamics laboratory in Universiti Putra Malaysia. The experimental result shows 25-30 % reduction in drag coefficient and 10-20 % increase in lift coefficient by using bird feather like winglet for angle of attack of 8 degree. Aerofoil, Wind tunnel, Winglet, Drag Coefficient.
A Context Sensitive Offloading Scheme for Mobile Cloud Computing Service

Mobile cloud computing (MCC) has drawn significant research attention as the popularity and capability of mobile devices have been improved in recent years. In this paper, we propose a prototype MCC offloading system that considers multiple cloud resources such as mobile ad-hoc network, cloudlet and public clouds to provide an adaptive MCC service. We propose a context-aware offloading decision algorithm aiming to provide code offloading decisions at runtime on selecting wireless medium and which potential cloud resources as the offloading location based on the device context. We also conduct real experiments on the implemented system to evaluate the performance of the algorithm. Results indicate the system and embedded decision algorithm can select suitable wireless medium and cloud resources based on different context of the mobile devices, and achieve significant performance improvement.
High-efficiency solution-processed perovskite solar cells with millimeter-scale grains

State-of-the-art photovoltaics use high-purity, large-area, wafer-scale single-crystalline semiconductors grown by sophisticated, high-temperature crystal growth processes. We demonstrate a solution-based hot-casting technique to grow continuous, pinhole-free thin films of organometallic perovskites with millimeter-scale crystalline grains. We fabricated planar solar cells with efficiencies approaching 18%, with little cell-to-cell variability. The devices show hysteresis-free photovoltaic response, which had been a fundamental bottleneck for the stable operation of perovskite devices. Characterization and modeling attribute the improved performance to reduced bulk defects and improved charge carrier mobility in large-grain devices. We anticipate that this technique will lead the field toward synthesis of wafer-scale crystalline perovskites, necessary for the fabrication of high-efficiency solar cells, and will be applicable to several other material systems plagued by polydispersity, defects, and grain boundary recombination in solution-processed thin films.
Hulk: Eliciting Malicious Behavior in Browser Extensions

We present Hulk, a dynamic analysis system that detects malicious behavior in browser extensions by monitoring their execution and corresponding network activity. Hulk elicits malicious behavior in extensions in two ways. First, Hulk leverages HoneyPages, which are dynamic pages that adapt to an extension’s expectations in web page structure and content. Second, Hulk employs a fuzzer to drive the numerous event handlers that modern extensions heavily rely upon. We analyzed 48K extensions from the Chrome Web store, driving each with over 1M URLs. We identify a number of malicious extensions, including one with 5.5 million affected users, stressing the risks that extensions pose for today’s web security ecosystem, and the need to further strengthen browser security to protect user data and privacy.
Probabilistic topic models for sequence data

Probabilistic topic models are widely used in different contexts to uncover the hidden structure in large text corpora. One of the main (and perhaps strong) assumption of these models is that generative process follows a bag-of-words assumption, i.e. each token is independent from the previous one. We extend the popular Latent Dirichlet Allocation model by exploiting three different conditional Markovian assumptions: (i) the token generation depends on the current topic and on the previous token; (ii) the topic associated with each observation depends on topic associated with the previous one; (iii) the token generation depends on the current and previous topic. For each of these modeling assumptions we present a Gibbs Sampling procedure for parameter estimation. Experimental evaluation over real-word data shows the performance advantages, in terms of recall and precision, of the sequence-modeling approaches.
PLoS Computational Biology: A New Community Journal

Welcome to PLoS Computational Biology, a community journal from the Public Library of Science dedicated to reporting biological advances achieved through computation. The journal is published in partnership with the International Society for Computational Biology (ISCB). The importance of this partnership is described in the accompanying letter from Michael Gribskov, ISCB president. What motivates us to start a new journal at this time? Computation, driven in part by the influx of large amounts of data at all biological scales, has become a central feature of research and discovery in the life sciences. This work tends to be published either in methods journals that are not read by experimentalists or in one of the numerous journals reporting novel biology, each of which publishes only small amounts of computational research. Hence, the impact of this research is diluted.  This quality is reflected in the editorial board and the editorial staff. The editorial board members are leaders in their respective scientific areas and have agreed to give their valuable time to support a quality journal in their field. Behind the scenes, through a rigorous presubmission process, three quality reviews for each paper, and an acceptance rate below 20%, the editors and staff already knew in the six months since the journal was launched that we were producing a first-rate product. The scientific content is now here for all of you to see and will continue to build in the months and years to come.
High Accuracy Head Pose Tracking Survey

Head pose estimation is recently a more and more popular area of research. For the last three decades new approaches have constantly been developed, and steadily better accuracy was achieved. Unsurprisingly, a very broad range of methods was explored statistical, geometrical and tracking-based to name a few. This paper presents a brief summary of the evolution of head pose estimation and a glimpse at the current state-of-the-art in this eld.
Bird detection in audio: A survey and a challenge

Many biological monitoring projects rely on acoustic detection of birds. Despite increasingly large datasets, this detection is often manual or semi-automatic, requiring manual tuning/postprocessing. We review the state of the art in automatic bird sound detection, and identify a widespread need for tuning-free and species-agnostic approaches. We introduce new datasets and an IEEE research challenge to address this need, to make possible the development of fully automatic algorithms for bird sound detection.
S.10.03 Computerized training of working memory in children with ADHD

Working memory (WM) capacity is the ability to retain and manipulate information during a short period of time. This ability underlies complex reasoning and has generally been regarded as a fixed trait of the individual. Children with attention deficit hyperactivity disorder (ADHD) represent one group of subjects with a WM deficit, attributed to an impairment of the frontal lobe. In the present study, we used a new training paradigm with intensive and adaptive training of WM tasks and evaluated the effect of training with a double blind, placebo controlled design. Training significantly enhanced performance on the trained WM tasks. More importantly, the training significantly improved performance on a nontrained visuo-spatial WM task and on Raven's Progressive Matrices, which is a nonverbal complex reasoning task. In addition, motor activity--as measured by the number of head movements during a computerized test--was significantly reduced in the treatment group. A second experiment showed that similar training-induced improvements on cognitive tasks are also possible in young adults without ADHD. These results demonstrate that performance on WM tasks can be significantly improved by training, and that the training effect also generalizes to nontrained tasks requiring WM. Training improved performance on tasks related to prefrontal functioning and had also a significant effect on motor activity in children with ADHD. The results thus suggest that WM training potentially could be of clinical use for ameliorating the symptoms in ADHD.
The Internet of Flying-Things: Opportunities and Challenges with Airborne Fog Computing and Mobile Cloud in the Clouds

This paper focuses on services and applications provided to mobile users using airborne computing infrastructure. We present concepts such as drones-as-a-service and fl yin,fly-out infrastructure, and note data management and sys tem design issues that arise in these scenarios. Issues of Big Da ta arising from such applications, optimising the configuration of airborne and ground infrastructure to provide the best QoS and QoE, situation-awareness, scalability, reliability,scheduling for efficiency, interaction with users and drones using phys ical annotations are outlined.
The cortical topography of tonal structures underlying Western music.

Western tonal music relies on a formal geometric structure that determines distance relationships within a harmonic or tonal space. In functional magnetic resonance imaging experiments, we identified an area in the rostromedial prefrontal cortex that tracks activation in tonal space. Different voxels in this area exhibited selectivity for different keys. Within the same set of consistently activated voxels, the topography of tonality selectivity rearranged itself across scanning sessions. The tonality structure was thus maintained as a dynamic topography in cortical areas known to be at a nexus of cognitive, affective, and mnemonic processing.
A survey on Data Mining approaches for Healthcare

Mining is one of the most motivating area of research that is become increasingly popular in health organization. Data Mining plays an important role for uncovering new trends in healthcare organization which in turn helpful for all the parties associated with this field. This survey explores the utility of various Data Mining techniques such as classification, clustering, association, regression in health domain. In this paper, we present a brief introduction of these techniques and their advantages and disadvantages. This survey also highlights applications, challenges and future issues of Data Mining in healthcare. Recommendation regarding the suitable choice of available Data Mining technique is also discussed in this paper.
Community Structure in Graphs

Graph vertices are often organized into groups that seem to live fairly independently of the rest of the graph, with which they share but a few edges, whereas the relationships between group members are stronger, as shown by the large number of mutual connections. Such groups of vertices, or communities, can be considered as independent compartments of a graph. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. The task is very hard, though, both conceptually, due to the ambiguity in the definition of community and in the discrimination of different partitions and practically, because algorithms must find “good” partitions among an exponentially large number of them. Other complications are represented by the possible occurrence of hierarchies, i.e. communities which are nested inside larger communities, and by the existence of overlaps between communities, due to the presence of nodes belonging to more groups. All these aspects are dealt with in some detail and many methods are described, from traditional approaches used in computer science and sociology to recent techniques developed mostly within statistical physics.
A Frequent Concepts Based Document Clustering Algorithm

This paper presents a novel technique of document clustering based on frequent concepts. The proposed technique, FCDC (Frequent Concepts based document clustering), a clustering algorithm works with frequent concepts rather than frequent items used in traditional text mining techniques. Many well known clustering algorithms deal with documents as bag of words and ignore the important relationships between words like synonyms. the proposed FCDC algorithm utilizes the semantic relationship between words to create concepts. It exploits the WordNet ontology in turn to create low dimensional feature vector which allows us to develop a efficient clustering algorithm. It uses a hierarchical approach to cluster text documents having common concepts. FCDC found more accurate, scalable and effective when compared with existing clustering algorithms like Bisecting K-means , UPGMA and FIHC.
UWB CPW-Fed Fractal Patch Antenna With Band-Notched Function Employing Folded T-Shaped Element

A compact coplanar waveguide (CPW) monopole antenna is presented, comprising a fractal radiating patch in which a folded T-shaped element (FTSE) is embedded. The impedance match of the antenna is determined by the number of fractal unit cells, and the FTSE provides the necessary band-notch functionality. The filtering property can be tuned finely by controlling of length of FTSE. Inclusion of a pair of rectangular notches in the ground plane is shown to extend the antenna's impedance bandwidth for ultrawideband (UWB) performance. The antenna's parameters were investigated to fully understand their affect on the antenna. Salient parameters obtained from this analysis enabled the optimization of the antenna's overall characteristics. Experimental and simulation results demonstrate that the antenna exhibits the desired VSWR level and radiation patterns across the entire UWB frequency range. The measured results showed the antenna operates over a frequency band between 2.94–11.17 GHz with fractional bandwidth of 117% for <formula formulatype="inline"><tex Notation="TeX">${\rm VSWR} \leq 2$</tex></formula>, except at the notch band between 3.3–4.2 GHz. The antenna has dimensions of 14<formula formulatype="inline"><tex Notation="TeX">$\,\times\,$</tex> </formula>18<formula formulatype="inline"><tex Notation="TeX">$\,\times \,$</tex> </formula>1 mm<formula formulatype="inline"><tex Notation="TeX">$^{3}$</tex> </formula>.
Capacity and power allocation for fading MIMO channels with channel estimation error

We investigate the effect of channel estimation error on the capacity of multiple input multiple output (MIMO) fading channels. We study lower and upper bounds of mutual information under channel estimation error, and show that the two bounds are tight for Gaussian inputs. Assuming Gaussian inputs we also derive tight lower bounds of ergodic and outage capacities and optimal transmitter power allocation strategies that achieve the bounds under perfect feedback. For the ergodic capacity, the optimal strategy is a modified waterfilling over the spatial (antenna) and temporal (fading) domains. This strategy is close to optimum under small feedback delays, but when the delay is large, equal powers should be allocated across spatial dimensions. For the outage capacity, the optimal scheme is a spatial waterfilling and temporal truncated channel inversion. Numerical results show that some capacity gain is obtained by spatial power allocation. Temporal power adaptation, on the other hand, gives negligible gain in terms of ergodic capacity, but greatly enhances outage performance. December 12, 2005 DRAFT
A pilot phase-II prospective study to test the 'efficacy' and tolerability of a penile-extender device in the treatment of 'short penis'.

To assess a commonly marketed brand of penile extender, the Andro-Penis(R) (Andromedical, Madrid, Spain), widely used devices which aim to increase penile size, in a phase II single-arm study powered to detect significant changes in penile size, as despite their widespread use, there is little scientific evidence to support their potential clinical utility in the treatment of patients with inadequate penile dimensions.



Fifteen patients were required to test the efficacy of the device, assuming an effect size of >0.8. Eligible patients were counselled how to use the penile extender for at least 4 h/day for 6 months. Penile dimensions were measured at baseline and after 1, 3, 6 and 12 months (end of study). The erectile function (EF) domain of the International Index of EF was administered at baseline and at the end of the study. Treatment satisfaction was assessed using an institutional unvalidated five-item questionnaire.



After 6 months the mean gain in length was significant, meeting the goals of the effect size, at 2.3 and 1.7 cm for the flaccid and stretched penis, respectively. No significant changes in penile girth were detected. The EF domain scores improved significantly at the end of study. Treatment satisfaction scores were consistent with acceptable to good improvement in all items, except for penile girth, where the score was either 'no change' or 'mild improvement'.



Penile extenders should be regarded as a minimally invasive and effective treatment option to elongate the penile shaft in patients seeking treatment for a short penis.
Hierarchical Change-Detection Tests

We present hierarchical change-detection tests (HCDTs), as effective online algorithms for detecting changes in datastreams. HCDTs are characterized by a hierarchical architecture composed of a detection layer and a validation layer. The detection layer steadily analyzes the input datastream by means of an online, sequential CDT, which operates as a low-complexity trigger that promptly detects possible changes in the process generating the data. The validation layer is activated when the detection one reveals a change, and performs an offline, more sophisticated analysis on recently acquired data to reduce false alarms. Our experiments show that, when the process generating the datastream is unknown, as it is mostly the case in the real world, HCDTs achieve a far more advantageous tradeoff between false-positive rate and detection delay than their single-layered, more traditional counterpart. Moreover, the successful interplay between the two layers permits HCDTs to automatically reconfigure after having detected and validated a change. Thus, HCDTs are able to reveal further departures from the postchange state of the data-generating process.
A Scalable and Efficient Outlier Detection Strategy for Categorical Data

Outlier detection has received significant attention in many applications, such as detecting credit card fraud or network intrusions. Most existing research focuses on numerical datasets, and cannot directly apply to categorical sets where there is little sense in calculating distances among data points. Furthermore, a number of outlier detection methods require quadratic time with respect to the dataset size and usually multiple dataset scans. These characteristics are undesirable for large datasets, potentially scattered over multiple distributed sites. In this paper, we introduce Attribute Value Frequency (A VF), a fast and scalable outlier detection strategy for categorical data. A VF scales linearly with the number of data points and attributes, and relies on a single data scan. AVF is compared with a list of representative outlier detection approaches that have not been contrasted against each other. Our proposed solution is experimentally shown to be significantly faster, and as effective in discovering outliers.