nils-herrmann/clean_scirepeval huggingface dataset:
Convolutional recurrent neural networks for bird audio detection

Bird sounds possess distinctive spectral structure which may exhibit small shifts in spectrum depending on the bird species and environmental conditions. In this paper, we propose using convolutional recurrent neural networks on the task of automated bird audio detection in real-life environments. In the proposed method, convolutional layers extract high dimensional, local frequency shift invariant features, while recurrent layers capture longer term dependencies between the features extracted from short time frames. This method achieves 88.5% Area Under ROC Curve (AUC) score on the unseen evaluation data and obtains the second place in the Bird Audio Detection challenge.
A comparison of pose estimation techniques : Hardware vs . video

Robotic navigation requires that the robotic platform have an idea of its location and orientation within the environment. This localization is known as pose estimation, and has been a much researched topic. There are currently two main categories of pose estimation techniques: pose from hardware, and pose from video (PfV). Hardware pose estimation utilizes specialized hardware such as Global Positioning Systems (GPS) and Inertial Navigation Systems (INS) to estimate the position and orientation of the platform at the specified times. PfV systems use video cameras to estimate the pose of the system by calculating the inter-frame motion of the camera from features present in the images. These pose estimation systems are readily integrated, and can be used to augment and/or supplant each other according to the needs of the application. Both pose from video and hardware pose estimation have their uses, but each also has its degenerate cases in which they fail to provide reliable data. Hardware solutions can provide extremely accurate data, but are usually quite pricey and can be restrictive in their environments of operation. Pose from video solutions can be implemented with low-cost off-the-shelf components, but the accuracy of the PfV results can be degraded by noisy imagery, ambiguity in the feature matching process, and moving objects. This paper attempts to evaluate the cost/benefit comparison between pose from video and hardware pose estimation experimentally, and to provide a guide as to which systems should be used under certain scenarios.
Can Android Applications Be Identified Using Only TCP/IP Headers of Their Launch Time Traffic?

The ability to identify mobile apps in network traffic has significant implications in many domains, including traffic management, malware detection, and maintaining user privacy. App identification methods in the literature typically use deep packet inspection (DPI) and analyze HTTP headers to extract app fingerprints. However, these methods cannot be used if HTTP traffic is encrypted. We investigate whether Android apps can be identified from their launch-time network traffic using only TCP/IP headers. We first capture network traffic of 86,109 app launches by repeatedly running 1,595 apps on 4 distinct Android devices. We then use supervised learning methods used previously in the web page identification literature, to identify the apps that generated the traffic. We find that: (i) popular Android apps can be identified with 88% accuracy, by using the packet sizes of the first 64 packets they generate, when the learning methods are trained and tested on the data collected from same device; (ii) when the data from an unseen device (but similar operating system/vendor) is used for testing, the apps can be identified with 67% accuracy; (iii) the app identification accuracy does not drop significantly even if the training data are stale by several days, and (iv) the accuracy does drop quite significantly if the operating system/vendor is very different. We discuss the implications of our findings as well as open issues.
A comparison of CPUs, GPUs, FPGAs, and massively parallel processor arrays for random number generation

The future of high-performance computing is likely to rely on the ability to efficiently exploit huge amounts of parallelism. One way of taking advantage of this parallelism is to formulate problems as "embarrassingly parallel" Monte-Carlo simulations, which allow applications to achieve a linear speedup over multiple computational nodes, without requiring a super-linear increase in inter-node communication. However, such applications are reliant on a cheap supply of high quality random numbers, particularly for the three main maximum entropy distributions: uniform, used as a general source of randomness; Gaussian, for discrete-time simulations; and exponential, for discrete-event simulations. In this paper we look at four different types of platform: conventional multi-core CPUs (Intel Core2); GPUs (NVidia GTX 200); FPGAs (Xilinx Virtex-5); and Massively Parallel Processor Arrays (Ambric AM2000). For each platform we determine the most appropriate algorithm for generating each type of number, then calculate the peak generation rate and estimated power efficiency for each device.
Universal Conceptual Cognitive Annotation (UCCA)

Syntactic structures, by their nature, reflect first and foremost the formal constructions used for expressing meanings. This renders them sensitive to formal variation both within and across languages, and limits their value to semantic applications. We present UCCA, a novel multi-layered framework for semantic representation that aims to accommodate the semantic distinctions expressed through linguistic utterances. We demonstrate UCCA’s portability across domains and languages, and its relative insensitivity to meaning-preserving syntactic variation. We also show that UCCA can be effectively and quickly learned by annotators with no linguistic background, and describe the compilation of a UCCAannotated corpus.
Adaptive-latency DRAM: Optimizing DRAM timing for the common-case

In current systems, memory accesses to a DRAM chip must obey a set of minimum latency restrictions specified in the DRAM standard. Such timing parameters exist to guarantee reliable operation. When deciding the timing parameters, DRAM manufacturers incorporate a very large margin as a provision against two worst-case scenarios. First, due to process variation, some outlier chips are much slower than others and cannot be operated as fast. Second, chips become slower at higher temperatures, and all chips need to operate reliably at the highest supported (i.e., worst-case) DRAM temperature (85° C). In this paper, we show that typical DRAM chips operating at typical temperatures (e.g., 55° C) are capable of providing a much smaller access latency, but are nevertheless forced to operate at the largest latency of the worst-case. Our goal in this paper is to exploit the extra margin that is built into the DRAM timing parameters to improve performance. Using an FPGA-based testing platform, we first characterize the extra margin for 115 DRAM modules from three major manufacturers. Our results demonstrate that it is possible to reduce four of the most critical timing parameters by a minimum/maximum of 17.3%/54.8% at 55°C without sacrificing correctness. Based on this characterization, we propose Adaptive-Latency DRAM (AL-DRAM), a mechanism that adoptively reduces the timing parameters for DRAM modules based on the current operating condition. AL-DRAM does not require any changes to the DRAM chip or its interface. We evaluate AL-DRAM on a real system that allows us to reconfigure the timing parameters at runtime. We show that AL-DRAM improves the performance of memory-intensive workloads by an average of 14% without introducing any errors. We discuss and show why AL-DRAM does not compromise reliability. We conclude that dynamically optimizing the DRAM timing parameters can reliably improve system performance.
Augmented creativity: bridging the real and virtual worlds to enhance creative play

Augmented Reality (AR) holds unique and promising potential to bridge between real-world activities and digital experiences, allowing users to engage their imagination and boost their creativity. We propose the concept of Augmented Creativity as employing ar on modern mobile devices to enhance real-world creative activities, support education, and open new interaction possibilities. We present six prototype applications that explore and develop Augmented Creativity in different ways, cultivating creativity through ar interactivity. Our coloring book app bridges coloring and computer-generated animation by allowing children to create their own character design in an ar setting. Our music apps provide a tangible way for children to explore different music styles and instruments in order to arrange their own version of popular songs. In the gaming domain, we show how to transform passive game interaction into active real-world movement that requires coordination and cooperation between players, and how ar can be applied to city-wide gaming concepts. We employ the concept of Augmented Creativity to authoring interactive narratives with an interactive storytelling framework. Finally, we examine how Augmented Creativity can provide a more compelling way to understand complex concepts, such as computer programming.
Learning Steady-States of Iterative Algorithms over Graphs

Many graph analytics problems can be solved via iterative algorithms where the solutions are often characterized by a set of steady-state conditions. Different algorithms respect to different set of fixed point constraints, so instead of using these traditional algorithms, can we learn an algorithm which can obtain the same steady-state solutions automatically from examples, in an effective and scalable way? How to represent the meta learner for such algorithm and how to carry out the learning? In this paper, we propose an embedding representation for iterative algorithms over graphs, and design a learning method which alternates between updating the embeddings and projecting them onto the steadystate constraints. We demonstrate the effectiveness of our framework using a few commonly used graph algorithms, and show that in some cases, the learned algorithm can handle graphs with more than 100,000,000 nodes in a single machine.
Aircraft Control via Variable Cant-Angle Winglets

This paper investigates a novel method for the control of “morphing” aircraft. The concept consists of a pair of winglets with adjustable cant angle, independently actuated and mounted at the tips of a baseline flying wing. The general philosophybehind the conceptwas that for specificflight conditions such as a coordinated turn, the use of two control devices would be sufficient for adequate control. Computations with a vortex lattice model and subsequent wind-tunnel tests demonstrate the viability of the concept, with individual and/or dual winglet deflection producing multi-axis coupled control moments. Comparisons between the experimental and computational results showed reasonable to good agreement, with the major discrepancies thought to be due to wind-tunnel model aeroelastic effects.
Systematic assessment of clinical outcomes in bone-level and tissue-level endosseous dental implants.

The aim of the present systematic review was to address the following question: in patients treated with dental implants placed in pristine bone, what are the clinical and radiographic outcomes of bone-level (BL) implants in comparison to tissue-level (TL) implants after restoration with dental prostheses?



Scanning of online literature databases from 1966 to January 2012, supplemented by hand searching, was conducted to identify relevant prospective randomized controlled trials, controlled clinical trials, and cohort studies. Sequential screenings at the title, abstract, and full-text levels were performed independently and in duplicate. A meta-analysis was conducted to compile data from the primary studies included in this systematic review.



The search strategy revealed a total of 5,998. Screening at the title level resulted in 752 papers, while screening at the abstract level yielded 92 publications. Full-text reading identified nine articles that fulfilled the inclusion criteria of this review. The pooled estimated difference between BL and TL implants in mean marginal bone loss was 0.05 mm (95% confidence interval [CI], -0.03 to 0.13 mm), with no statistically significant difference between the groups at 1 year after placement of the definitive prostheses. The relative risk of implant loss was estimated at 1.00 (95% CI, 0.99 to 1.02) at 1 year and at 1.01 (95% CI, 0.99 to 1.03) at 3 years after restoration, indicating no evidence of an increased risk of implant loss in BL compared to TL implants.



No statistically significant differences in bone loss and survival rates were detected between BL and TL dental implants over a short-term observation period (1 to 3 years). Thus, both implant systems fulfill the requirements for the replacement of missing teeth in implant dentistry.
Decapitation in suicidal hanging--vital reaction patterns.

Complete or incomplete decapitation as a consequence of suicidal hanging is very rare, few cases having been reported in the worldwide literature. Posthanging decapitation is typically related to a drop of several meters. Three cases of complete decapitation and one case of incomplete decapitation by suicidal hanging are reported with particular emphasis on internal findings and vital reaction patterns. Personal, circumstantial, autopsy, and toxicological data were analyzed to define basic characteristics of such extreme injuries. The crucial factor for the state of decapitation itself is the kinetic energy of the falling body, the strength of the human neck tissue, and the diameter and elasticity of the used ligature. Results of our case study suggest Simon's hemorrhage and air embolism as useful autopsy findings in posthanging beheading cases. Simon's hemorrhage was demonstrated in three cases of four. The test for air embolism was positive in all four cases.
A Closed-Form Solution to Tensor Voting: Theory and Applications

We prove a closed-form solution to tensor voting (CFTV): Given a point set in any dimensions, our closed-form solution provides an exact, continuous, and efficient algorithm for computing a structure-aware tensor that simultaneously achieves salient structure detection and outlier attenuation. Using CFTV, we prove the convergence of tensor voting on a Markov random field (MRF), thus termed as MRFTV, where the structure-aware tensor at each input site reaches a stationary state upon convergence in structure propagation. We then embed structure-aware tensor into expectation maximization (EM) for optimizing a single linear structure to achieve efficient and robust parameter estimation. Specifically, our EMTV algorithm optimizes both the tensor and fitting parameters and does not require random sampling consensus typically used in existing robust statistical techniques. We performed quantitative evaluation on its accuracy and robustness, showing that EMTV performs better than the original TV and other state-of-the-art techniques in fundamental matrix estimation for multiview stereo matching. The extensions of CFTV and EMTV for extracting multiple and nonlinear structures are underway.
Deciphering malware’s use of TLS (without decryption)

The use of TLS by malware poses new challenges to network threat detection because traditional pattern-matching techniques can no longer be applied to its messages. However, TLS also introduces a complex set of observable data features that allow many inferences to be made about both the client and the server. We show that these features can be used to detect and understand malware communication, while at the same time preserving the privacy of the benign uses of encryption. These data features also allow for accurate malware family attribution of network communication, even when restricted to a single, encrypted flow. To demonstrate this, we performed a detailed study of how TLS is used by malware and enterprise applications. We provide a general analysis on millions of TLS encrypted flows, and a targeted study on 18 malware families composed of thousands of unique malware samples and tens-of-thousands of malicious TLS flows. Importantly, we identify and accommodate for the bias introduced by the use of a malware sandbox. We show that the performance of a malware classifier is correlated with a malware family’s use of TLS, i.e., malware families that actively evolve their use of cryptography are more difficult to classify. We conclude that malware’s usage of TLS is distinct in an enterprise setting, and that these differences can be effectively used in rules and machine learning classifiers.
A 56 Gb/s PAM-4 linear transimpedance amplifier in 0.13-μm SiGe BiCMOS technology for optical receivers

This paper describes the implementation of a state-of-the art 56Gb/s single-channel linear transimpedance amplifier (TIA) integrated circuit for PAM-4/NRZ/DMT modulation formats used in data center interconnects (DCI) and base station front haul applications. Fabricated in 130nm SiGe BiCMOS process, the TIA has a single-ended input, differential output configuration, with nominal maximum DC transimpedance gain of ∼5.8 kQ (75 dB), gain dynamic range of 28 dB, average input referred noise (IRN) of 14.9 pA/VHz, typical bandwidth of 38 GHz across the entire gain dynamic range, Total Harmonic Distortion (THD) of 2% for output of 500mVppd, adjustable output voltage swing up to 1Vppd, all operated with typical supply of 3.3 V. At 2.8 V supply, bandwidth of 34 GHz has been achieved. Performance has been measured from −5C to 95C. The TIA has Receive Signal Strength Indicator (RSSI) for measuring the input signal strength, peak detect (PKD) function for measuring the output amplitude and in-built photo-diode (PD) cathode bias network. The chip can be operated in manual gain control (MGC) or automatic gain control (AGC) mode. The die area is 1.6 mmA2. A state-of-the art 4-level Pulse Amplitude Modulation (PAM-4) eye diagram at 56Gb/s has been demonstrated with this linear TIA. The Die has been integrated into an optical product from Discovery Semiconductors and several optical interconnect products.
Automated learning of muscle-actuated locomotion through control abstraction

We present a learning technique that automatically synthesizes realistic locomotion for the animation of physics-based models of animals. The method is especially suitable for animals with highly flexible, many-degree-of-freedom bodies and a considerable number of internal muscle actuators, such as snakes and fish. The multilevel learning process first performs repeated locomotion trials in search of actuator control functions that produce efficient locomotion, presuming virtually nothing about the form of these functions. Applying a short-time Fourier analysis, the learning process then abstracts control functions that produce effective locomotion into a compact representation which makes explicit the natural quasi-periodicities and coordination of the muscle actions. The artificial animals can finally put into practice the compact, efficient controllers that they have learned. Their locomotion learning abilities enable them to accomplish higher-level tasks specified by the animator while guided by sensory perception of their virtual world; e.g., locomotion to a visible target. We demonstrate physics-based animation of learned locomotion in dynamic models of land snakes, fishes, and even marine mammals that have trained themselves to perform “SeaWorld” stunts.
Competencies of exceptional and nonexceptional software engineers

The attributes of individual software engineers are perhaps the most important factors in determining the success of software development. Our goal is to identify the professional competencies that are most essential. In particular, we seek to identify the attributes that di erentiate between exceptional and non-exceptional software engineers. Phase 1 of our research is a qualitative study designed to identify competencies to be used in the quantitative analysis performed in Phase 2. In Phase 1, we conduct an in-depth review of ten exceptional and ten non-exceptional software engineers working for a major computing rm. We use biographical data and Myers-Briggs Type Indicator test results to characterize our sample. We conduct Critical Incident Interviews focusing on the subjects experience in software and identify 38 essential competencies of software engineers. Phase 2 of this study surveys 129 software engineers to determine the competencies that are di erential between exceptional and non-exceptional engineers. Years of experience in software is the only biographical predictor of performance. Analysis of the participants Q-Sort of the 38 competencies identi ed in Phase 1 reveals that nine of these competencies are di erentially related to engineer performance using a t-test. A ten variable Canonical Discrimination Function consisting of three biographical variables and seven competencies is capable of correctly classifying 81% of the cases. The statistical analyses indicate that exceptional engineers (at the company studied) can be distinguished by behaviors associated with an external focus | behaviors directed at people or objects outside the individual. Exceptional engineers are more likely than non-exceptional engineers to maintain a \big picture", have a bias for action, be driven by a sense of mission, exhibit and articulate strong convictions, play a pro-active role with management, and help other engineers. Authors addresses: R. Turley, Colorado Memory Systems, Inc., 800 S. Taft Ave., Loveland, CO 80537.  RICKTURL.COMEMSYS@CMS SMTP.gr.hp.com, (303) 635-6490, Fax: (303) 635-6613; J. Bieman, Department of Computer Science, Colorado State University, Fort Collins, CO 80523.  bieman@cs.colostate.edu, (303)4917096, Fax: (303) 491-6639.  Direct correspondence concerning this paper to: J. Bieman, Department of Computer Science, Colorado State University, Fort Collins, CO 80523, bieman@cs.colostate.edu, (303)491-7096, Fax: (303)491-6639.
Decentralized Access Control Mechanism with Temporal Dimension Based on Blockchain

Used mainly for the virtual money, the Blockchain technology adopts a decentralized network of peers to ensure a secure and transparent information storage and transmission. The basic use of Blockchain can be bypassed, and it is interesting to integrate it into other fields such as the Cloud storage. Cloud storage solutions ensure a continuous data synchronization and guarantee data sharing between different users. However, sharing user side encrypted data raises key and access control management challenges. Within this paper, we propose a novel access control model called Timely CP-ABE. Two main features come with our model. First, we introduce a decentralized access control mechanism where the user legitimacy is verified by Blockchain nodes. Second, we add temporal dimension to file sharing based on CP-ABE. In fact, we introduce a validity time to the access authorization without additional revocation cost. As a proof of concept, the implementation of the Timely CP-ABE based on Blockchain is performed on the CP-ABE toolkit and Multichain solution.
Superficial Acral Fibromyxoma involving the nail's apparatus. Case report
and literature review*


Superficial Acral Fibromyxoma is a rare tumor of soft tissues. It is a relatively new entity described in 2001 by Fetsch et al. It probably represents a fibrohistiocytic tumor with less than 170 described cases. We bring a new case of SAF on the 5th toe of the right foot, in a 43-year-old woman. After surgical excision with safety margins which included the nail apparatus, it has not recurred (22 months of follow up). We carried out a review of the location of all SAF published up to the present day.