nils-herrmann/clean_scirepeval huggingface dataset:
An image procesing approach for calorie intake measurement

Obesity in the world has spread to epidemic proportions. In 2008 the World Health Organization (WHO) reported that 1.5 billion adults were suffering from some sort of overweightness. Obesity treatment requires constant monitoring and a rigorous control and diet to measure daily calorie intake. These controls are expensive for the health care system, and the patient regularly rejects the treatment because of the excessive control over the user. Recently, studies have suggested that the usage of technology such as smartphones may enhance the treatments of obesity and overweight patients; this will generate a degree of comfort for the patient, while the dietitian can count on a better option to record the food intake for the patient. In this paper we propose a smart system that takes advantage of the technologies available for the Smartphones, to build an application to measure and monitor the daily calorie intake for obese and overweight patients. Via a special technique, the system records a photo of the food before and after eating in order to estimate the consumption calorie of the selected food and its nutrient components. Our system presents a new instrument in food intake measuring which can be more useful and effective.
High-Performance and High-Data-Rate Quasi-Coaxial LTCC Vertical Interconnect Transitions for Multichip Modules and System-on-Package Applications

A new design of stripline transition structures and flip-chip interconnects for high-speed digital communication systems implemented in low-temperature cofired ceramic (LTCC) substrates is presented. Simplified fabrication, suitability for LTCC machining, suitability for integration with other components, and connection to integrated stripline or microstrip interconnects for LTCC multichip modules and system on package make this approach well suited for miniaturized, advanced broadband, and highly integrated multichip ceramic modules. The transition provides excellent signal integrity at high-speed digital data rates up to 28 Gbits/s. Full-wave simulations and experimental results demonstrate a cost-effective solution for a wide frequency range from dc to 30 GHz and beyond. Signal integrity and high-speed digital data rate performances are verified through eye diagram and time-domain reflectometry and time-domain transmissometry measurements over a 10-cm long stripline.
Pyramidal directional filter banks and curvelets

A flexible multiscale and directional representation for images is proposed. The scheme combines directional filter banks with the Laplacian pyramid to provides a sparse representation for twodimensional piecewise smooth signals resembling images. The underlying expansion is a frame and can be designed to be a tight frame. Pyramidal directional filter banks provide an effective method to implement the digital curvelet transform. The regularity issue of the iterated filters in the directional filter bank is examined.
Compact Multiresonator-Loaded Planar Antenna for Multiband Operation

A compact multiresonator-loaded planar antenna for multiband operation is presented. Multiresonator-loaded structures including a pair of symmetrical edge resonators and a T-shaped stub resonator are employed. By loading these resonators, a U-shaped monopole antenna can yield three independent resonances while achieving a compact size of <formula formulatype="inline"><tex Notation="TeX">$18\times 19\ {\rm mm}^{2}$</tex></formula>. The experimental results demonstrate that the proposed antenna is capable of operating over the 2.40–2.51 GHz, 3.35–3.94 GHz and 5.02–6.63 GHz frequency ranges, which is suitable for WLAN 2.4/5.2/5.8 GHz and WiMAX 3.5 GHz applications.
The role of hyaluronidase in the treatment of complications from hyaluronic acid dermal fillers.

Hyaluronidases, a family of enzymes that are able to degrade hyaluronic acid (HA), are employed in medicine to increase drug diffusion and reverse the effects of HA filler injections. Hyaluronidases are able to dissolve subcutaneous nodules or to correct excessive quantities of injected filler. Knowledge of the use, methods of application, and adverse effects of hyaluronidases is essential for the aesthetic practitioner. Therefore, we performed an extensive review of the available literature from 1928 to 2011 and compared the different enzymes available, recording each author's indications regarding usage and side effects.
A transformerless high boost DC-DC converter for use in medium / high voltage applications

The proliferation of distributed energy resources has prompted interest in the expansion of DC power systems. One critical technological limitation that hinders this expansion is the absence of high step-down and high step-up DC converters for interconnecting DC systems. This work attempts to address the latter of these limitations. This paper presents a new transformerless high boost DC-DC converter intended for use as an interconnect between DC systems. With a conversion ratio of 1:10, the converter offers significantly higher boost ratio than the conventional non-isolated boost converter. It is designed to operate at medium to high voltage (>; 1kV), and provides high voltage dc/dc gain (>;5). Based on a current fed resonant topology, the design is well matched to available IGBT switch technology that enables use of relatively high switching frequencies yet accommodates the IGBTs inability to provide reverse blocking functionality. An advanced steady state model suitable for analysis of this converter is presented together with an experimental evaluation of the converter.
Securify: Practical Security Analysis of Smart Contracts

Permissionless blockchains allow the execution of arbitrary programs (called smart contracts), enabling mutually untrusted entities to interact without relying on trusted third parties. Despite their potential, repeated security concerns have shaken the trust in handling billions of USD by smart contracts. To address this problem, we present Securify, a security analyzer for Ethereum smart contracts that is scalable, fully automated, and able to prove contract behaviors as safe/unsafe with respect to a given property. Securify's analysis consists of two steps. First, it symbolically analyzes the contract's dependency graph to extract precise semantic information from the code. Then, it checks compliance and violation patterns that capture sufficient conditions for proving if a property holds or not. To enable extensibility, all patterns are specified in a designated domain-specific language. Securify is publicly released, it has analyzed >18K contracts submitted by its users, and is regularly used to conduct security audits by experts. We present an extensive evaluation of Securify over real-world Ethereum smart contracts and demonstrate that it can effectively prove the correctness of smart contracts and discover critical violations.
Subspace Linear Discriminant Analysis for Face RecognitionW

In this paper we describe a holistic face recognition method based on subspace Linear Dis-criminant Analysis (LDA). The method consists of two steps: rst we project the face image from the original vector space to a face subspace via Principal Component Analysis where the subspace dimension is carefully chosen, and then we use LDA to obtain a linear classiier in the subspace. The criterion we use to choose the subspace dimension enables us to generate class-separable features via LDA from the full subspace representation. Hence we are able to solve the generalization/overrtting problem when we perform face recognition on a large face dataset but with very few training face images available per testing person. In addition, we employ a weighted distance metric guided by the LDA eigenvalues to improve the performance of the subspace LDA method. Finally, the improved performance of the subspace LDA approach is demonstrated through experiments using the FERET dataset for face recognition/veriication, a large mugshot dataset for person veriication, and the MPEG-7 dataset. We believe that this approach provides a useful framework for other image recognition tasks as well.
Overlapping Community Detection based on Network Decomposition

Community detection in complex network has become a vital step to understand the structure and dynamics of networks in various fields. However, traditional node clustering and relatively new proposed link clustering methods have inherent drawbacks to discover overlapping communities. Node clustering is inadequate to capture the pervasive overlaps, while link clustering is often criticized due to the high computational cost and ambiguous definition of communities. So, overlapping community detection is still a formidable challenge. In this work, we propose a new overlapping community detection algorithm based on network decomposition, called NDOCD. Specifically, NDOCD iteratively splits the network by removing all links in derived link communities, which are identified by utilizing node clustering technique. The network decomposition contributes to reducing the computation time and noise link elimination conduces to improving the quality of obtained communities. Besides, we employ node clustering technique rather than link similarity measure to discover link communities, thus NDOCD avoids an ambiguous definition of community and becomes less time-consuming. We test our approach on both synthetic and real-world networks. Results demonstrate the superior performance of our approach both in computation time and accuracy compared to state-of-the-art algorithms.
An adaptive threshold framework for event detection using HMM-based life profiles

When an event occurs, it attracts attention of information sources to publish related documents along its lifespan. The task of event detection is to automatically identify events and their related documents from a document stream, which is a set of chronologically ordered documents collected from various information sources. Generally, each event has a distinct activeness development so that its status changes continuously during its lifespan. When an event is active, there are a lot of related documents from various information sources. In contrast when it is inactive, there are very few documents, but they are focused. Previous works on event detection did not consider the characteristics of the event's activeness, and used rigid thresholds for event detection. We propose a concept called life profile, modeled by a hidden Markov model, to model the activeness trends of events. In addition, a general event detection framework, LIPED, which utilizes the learned life profiles and the burst-and-diverse characteristic to adjust the event detection thresholds adaptively, can be incorporated into existing event detection methods. Based on the official TDT corpus and contest rules, the evaluation results show that existing detection methods that incorporate LIPED achieve better performance in the cost and F1 metrics, than without.
Closed-Form Inverse Kinematics for Interventional C-Arm X-Ray Imaging With Six Degrees of Freedom: Modeling and Application

For trauma and orthopedic surgery, maneuvering a mobile C-arm fluoroscope into a desired position to acquire an X-ray is a routine surgical task. The precision and ease of use of the C-arm becomes even more important for advanced interventional imaging techniques such as parallax-free X-ray image stitching. Today's standard mobile C-arms have been modeled with only five degrees of freedom (DOF), which definitely restricts their motions in 3-D Cartesian space. In this paper, we present a method to model both the mobile C-arm and patient's table as an integrated kinematic chain having six DOF without constraining table position. The closed-form solutions for the inverse kinematics problem are derived in order to obtain the required values for all C-arm joint and table movements to position the fluoroscope at a desired pose. The modeling method and the closed-form solutions can be applied to general isocentric or nonisocentric mobile C-arms. By achieving this we develop an efficient and intuitive inverse kinematics-based method for parallax-free panoramic X-ray imaging. In addition, we implement a 6-DOF C-arm system from a low-cost mobile fluoroscope to optimally acquire X-ray images based solely on the computation of the required movement for each joint by solving the inverse kinematics on a continuous basis. Through simulation experimentation, we demonstrate that the 6-DOF C-arm model has a larger working space than the 5-DOF model. C-arm repositioning experiments show the practicality and accuracy of our 6-DOF C-arm system. We also evaluate the novel parallax-free X-ray stitching method on phantom and dry bones. Using five trials, results show that parallax-free panoramas generated by our method are of high visual quality and within clinical tolerances for accurate evaluation of long bone geometry (i.e., image and metric measurement errors are less than 1% compared to ground-truth).
The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey

The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as Radio frequency identifications, sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future Internet. Over the last decade, we have seen a large number of the IoT solutions developed by start-ups, small and medium enterprises, large corporations, academic research institutes (such as universities), and private and public research organizations making their way into the market. In this paper, we survey over one hundred IoT smart solutions in the marketplace and examine them closely in order to identify the technologies used, functionalities, and applications. Based on the application domain, we classify and discuss these solutions under five different categories: 1) smart wearable; 2) smart home; 3) smart city; 4) smart environment; and 5) smart enterprise. This survey is intended to serve as a guideline and a conceptual framework for future research in the IoT and to motivate and inspire further developments. It also provides a systematic exploration of existing research and suggests a number of potentially significant research directions.
Adaptable, metadata rich IO methods for portable high performance IO

Since IO performance on HPC machines strongly depends on machine characteristics and configuration, it is important to carefully tune IO libraries and make good use of appropriate library APIs. For instance, on current petascale machines, independent IO tends to outperform collective IO, in part due to bottlenecks at the metadata server. The problem is exacerbated by scaling issues, since each IO library scales differently on each machine, and typically, operates efficiently to different levels of scaling on different machines. With scientific codes being run on a variety of HPC resources, efficient code execution requires us to address three important issues: (1) end users should be able to select the most efficient IO methods for their codes, with minimal effort in terms of code updates or alterations; (2) such performance-driven choices should not prevent data from being stored in the desired file formats, since those are crucial for later data analysis; and (3) it is important to have efficient ways of identifying and selecting certain data for analysis, to help end users cope with the flood of data produced by high end codes.  In this paper, we also use this facility of ADIOS to experimentally evaluate on petascale machines alternative methods for high performance IO. Specific examples studied include methods that use strong file consistency vs. delayed parallel data consistency, as that provided by MPI-IO or POSIX IO. Concerning (2), to avoid linking IO methods to specific file formats and attain high IO performance, ADIOS introduces an efficient intermediate file format, termed BP, which can be converted, at small cost, to the standard file formats used by analysis tools, such as NetCDF and HDF-5. Concerning (3), associated with BP are efficient methods for data characterization, which compute attributes that can be used to identify data sets without having to inspect or analyze the entire data contents of large files.
Tracking Mobile Web Users Through Motion Sensors: Attacks and Defenses

Modern smartphones contain motion sensors, such as accelerometers and gyroscopes. These sensors have many useful applications; however, they can also be used to uniquely identify a phone by measuring anomalies in the signals, which are a result of manufacturing imperfections. Such measurements can be conducted surreptitiously by web page publishers or advertisers and can thus be used to track users across applications, websites, and visits. We analyze how well sensor fingerprinting works under realworld constraints. We first develop a highly accurate fingerprinting mechanism that combines multiple motion sensors and makes use of inaudible audio stimulation to improve detection. We evaluate this mechanism using measurements from a large collection of smartphones, in both lab and public conditions. We then analyze techniques to mitigate sensor fingerprinting either by calibrating the sensors to eliminate the signal anomalies, or by adding noise that obfuscates the anomalies. We evaluate the impact of calibration and obfuscation techniques on the classifier accuracy; we also look at how such mitigation techniques impact the utility of the motion sensors.
Decision fusion in a wireless sensor network with a large number of sensors

For a wireless sensor network (WSN) with a large number of sensors, a decision fusion rule using the total number of detections reported by local sensors for hypothesis testing, is proposed and studied. Based on a signal attenuation model where the received signal power decays as the distance from the target increases, the system level detection performance, namely probabilities of detection and false alarms, are derived and calculated. Without the knowledge of local sensors’ performances and at low signal to noise ratio (SNR), this fusion rule can still achieve very good system level detection performance if the number of sensors is sufficiently large. The problem of designing an optimum local sensor level threshold is investigated. For various system parameters, the optimal thresholds are found numerically. Guidelines on selecting the optimal local threshold have been presented.
The gist of maps - summarizing experience for lifelong localization

Robust, scalable place recognition is a core competency for many robotic applications. However, when revisiting places over and over, many state-of-the-art approaches exhibit reduced performance in terms of computation and memory complexity and in terms of accuracy. For successful deployment of robots over long time scales, we must develop algorithms that get better with repeated visits to the same environment, while still working within a fixed computational budget. This paper presents and evaluates an algorithm that alternates between online place recognition and offline map maintenance with the goal of producing the best performance with a fixed map size. At the core of the algorithm is the concept of a Summary Map, a reduced map representation that includes only the landmarks that are deemed most useful for place recognition. To assign landmarks to the map, we use a scoring function that ranks the utility of each landmark and a sampling policy that selects the landmarks for each place.  We evaluate a number of scoring functions and sampling policies and show that it is possible to build and maintain maps of a constant size and that place-recognition performance improves over multiple visits.
Making BFT Protocols Really Adaptive

Many state-machine Byzantine Fault Tolerant (BFT) protocols have been introduced so far. Each protocol addressed a different subset of conditions and use-cases. However, if the underlying conditions of a service span different subsets, choosing a single protocol will likely not be a best fit. This yields robustness and performance issues which may be even worse in services that exhibit fluctuating conditions and workloads. In this paper, we reconcile existing state-machine BFT protocols in a single adaptive BFT system, called ADAPT, aiming at covering a larger set of conditions and use-cases, probably the union of individual subsets of these protocols. At anytime, a launched protocol in ADAPT can be aborted and replaced by another protocol according to a potential change (an event) in the underlying system conditions. The launched protocol is chosen according to an "evaluation process" that takes into consideration both: protocol characteristics and its performance. This is achieved by applying some mathematical formulas that match the profiles of protocols to given user (e.g., service owner) preferences. ADAPT can assess the profiles of protocols (e.g., throughput) at run-time using Machine Learning prediction mechanisms to get accurate evaluations. We compare ADAPT with well known BFT protocols showing that it outperforms others as system conditions change and under dynamic workloads.
A 56.4-to-63.4 GHz Multi-Rate All-Digital Fractional-N PLL for FMCW Radar Applications in 65 nm CMOS

A mm-wave digital transmitter based on a 60 GHz all-digital phase-locked loop (ADPLL) with wideband frequency modulation (FM) for FMCW radar applications is proposed. The fractional-N ADPLL employs a high-resolution 60 GHz digitally-controlled oscillator (DCO) and is capable of multi-rate two-point FM. It achieves a measured rms jitter of 590.2 fs, while the loop settles within 3 μs. The measured reference spur is only -74 dBc, the fractional spurs are below -62 dBc, with no other significant spurs. A closed-loop DCO gain linearization scheme realizes a GHz-level triangular chirp across multiple DCO tuning banks with a measured frequency error (i.e., nonlinearity) in the FMCW ramp of only 117 kHz rms for a 62 GHz carrier with 1.22 GHz bandwidth. The synthesizer is transformer-coupled to a 3-stage neutralized power amplifier (PA) that delivers +5 dBm to a 50 Ω load. Implemented in 65 nm CMOS, the transmitter prototype (including PA) consumes 89 mW from a 1.2 V supply.
The Computational Impact of Partial Votes on Strategic Voting

In many real world elections, agents are not required to rank all candidates. We study three of the most common meth ods used to modify voting rules to deal with such partial votes. These methods modify scoring rules (like the Borda count), e limination style rules (like single transferable vote) and rule s based on the tournament graph (like Copeland) respectively. We argu e that with an elimination style voting rule like single transfera ble vote, partial voting does not change the situations where strateg ic voting is possible. However, with scoring rules and rules based on the tournament graph, partial voting can increase the situations wher e strategic voting is possible. As a consequence, the computational com plexity of computing a strategic vote can change. For example, with B orda count, the complexity of computing a strategic vote can decr ease or stay the same depending on how we score partial votes.