nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Detection of Video Shot Boundary in Social Media Using a Hybrid Approach of HLFPN and Keypoint Matching

Shot boundary detection (SBD) is an important and fundamental step in video content analysis such as content-based video indexing, browsing, and retrieval. In this paper, a hybrid SBD method is presented by integrating a high-level fuzzy Petri net (HLFPN) model with keypoint matching. The HLFPN model with histogram difference is executed as a predetection. Next, the speeded-up robust features (SURF) algorithm that is reliably robust to image affine transformation and illumination variation is used to figure out all possible false shots and the gradual transition based on the assumption from the HLFPN model. The top-down design can effectively lower down the computational complexity of SURF algorithm. The proposed approach has increased the precision of SBD and can be applied in different types of videos.
Pouch Motors : Printable Soft Actuators Integrated with Computational Design

We propose pouch motors, a new family of printable soft actuators integrated with computational design. The pouch motor consists of one or more inflatable gas-tight bladders made of sheet materials. This printable actuator is designed and fabricated in a planar fashion. It allows both easy prototyping and mass fabrication of affordable robotic systems. We provide theoretical models of the actuators compared with the experimental data. The measured maximum stroke and tension of the linear pouch motor are up to 28% and 100 N, respectively. The measured maximum range of motion and torque of the angular pouch motor are up to 80 and 0.2 N, respectively. We also develop an algorithm that automatically generates the patterns of the pouches and their fluidic channels. A custom-built fabrication machine streamlines the automated process from design to fabrication. We demonstrate a computer-generated life-sized hand that can hold a foam ball and perform gestures with 12 pouch motors, which can be fabricated in 15 min.
A Survey on Data Mining Techniques in Agriculture

Agriculture is the most significant application area particularly in the developing countries like India. Use of information technology in agriculture can change the situation of decision making and farmers can yield in a better way. Data mining plays a crucial role for decision making on several issues related to agriculture field. This paper discussed about the role of data mining in perspective of agriculture field and also confers about several data mining techniques and their related work by several authors in context to agriculture domain. It also discusses on different data mining applications in solving the different agricultural problems. It integrates the work of various authors in one place so it is useful for researchers to get information of current scenario of data mining techniques and applications in context to agriculture field. This paper provides a survey of various data mining techniques used in agriculture which includes Artificial Neural Networks, K nearest neighbor, Decision tree, Bayesion network, Fuzzy set, Support Vector Machine and K – means[1].
Performance enhancement of coupled-fed printed log-periodic antenna using complimentary split ring resonator

In this paper, a complimentary split ring resonator (CSRR) enhanced wideband log-periodic antenna with coupled microstrip line feeding is presented. Here in this work, coupled line feeding to the patches is proposed to avoid individual microstrip feed matching complexities. Three CSRR elements were etched in the ground plane. Individual patches were designed according to the conventional log-periodic design rules. FR4 dielectric substrate is used to design a five-element log-periodic patch with CSRR printed on the ground plane. The result shows a wide operating band ranging from 4.5 GHz to 9 GHz. Surface current distribution of the antenna shows a strong resonance of CSRR's placed in the ground plane. The design approach of the antenna is reported and performance of the proposed antenna has been evaluated through three dimensional electromagnetic simulation validating performance enhancement of the antenna due to presence of CSRRs. Antennas designed in this work may be used in satellite and indoor wireless communication.
Understanding kangaroo care and its benefits to preterm infants

The holding of an infant with ventral skin-to-skin contact typically in an upright position with the swaddled infant on the chest of the parent, is commonly referred to as kangaroo care (KC), due to its simulation of marsupial care. It is recommended that KC, as a feasible, natural, and cost-effective intervention, should be standard of care in the delivery of quality health care for all infants, regardless of geographic location or economic status. Numerous benefits of its use have been reported related to mortality, physiological (thermoregulation, cardiorespiratory stability), behavioral (sleep, breastfeeding duration, and degree of exclusivity) domains, as an effective therapy to relieve procedural pain, and improved neurodevelopment. Yet despite these recommendations and a lack of negative research findings, adoption of KC as a routine clinical practice remains variable and underutilized. Furthermore, uncertainty remains as to whether continuous KC should be recommended in all settings or if there is a critical period of initiation, dose, or duration that is optimal. This review synthesizes current knowledge about the benefits of KC for infants born preterm, highlighting differences and similarities across low and higher resource countries and in a non-pain and pain context. Additionally, implementation considerations and unanswered questions for future research are addressed.
Broadband Transition Design From Microstrip to CPW

This letter presents a broadband transition between microstrip and CPW located at the opposite lawyer of the substrate. Basically, the transition is based on two couples of microstrip-to-slotline transitions. In order to widen bandwidth of the transition, a short-ended parallel microstrip stub is added. A demonstrator transition has been designed, fabricated and measured. Results show that a frequency range of 2.05 to 9.96 GHz (referred to return loss of 10 dB) is obtained.
Incremental Object Database: Building 3D Models from Multiple Partial Observations

Collecting 3D object data sets involves a large amount of manual work and is time consuming. Getting complete models of objects either requires a 3D scanner that covers all the surfaces of an object or one needs to rotate it to completely observe it. We present a system that incrementally builds a database of objects as a mobile agent traverses a scene. Our approach requires no prior knowledge of the shapes present in the scene. Object-like segments are extracted from a global segmentation map, which is built online using the input of segmented RGB-D images. These segments are stored in a database, matched among each other, and merged with other previously observed instances. This allows us to create and improve object models on the fly and to use these merged models to reconstruct also unobserved parts of the scene. The database contains each (potentially merged) object model only once, together with a set of poses where it was observed. We evaluate our pipeline with one public dataset, and on a newly created Google Tango dataset containing four indoor scenes with some of the objects appearing multiple times, both within and across scenes.
The quest for location-privacy in opportunistic mobile social networks

Location-tracking is a major privacy problem, especially with high penetration of smart-phones and GPS-enabled devices around the world. In this paper, we propose a Hybrid and Social-aware Location-Privacy in Opportunistic mobile social networks (HSLPO), a collaborative and distributed obfuscation protocol that offers location-privacy k-anonymity. HSLPO discovers the users' own social network and use it to obfuscate requests and hide the original sender's location from the Location based service (LBS) while minimizing the overhead needed to perform obfuscation. We performed extensive simulations over a map-based pseudo realistic environment to compare HSLPO to exiting protocols. Results show that HSLPO can reach higher location-privacy levels and quality of service, in term of success ratio, than other protocols while maintaining low overheads.
Parallel implementation of Apriori algorithms on the Hadoop-MapReduce platform - An evaluation of literature

Data mining is the extraction of useful, prognostic, interesting, and unknown information from massive transaction databases and other repositories. Data mining tools predict potential trends and actions, allowing various fields to make proactive, knowledge-driven decisions. Recently, with the rapid growth of information technology, the amount of data has exponentially increased in various fields. Big data mostly comes from people’s day-to-day activities and Internet-based companies. Mining frequent itemsets and association rule mining (ARM) are well-analysed techniques for revealing attractive correlations among variables in huge datasets. The Apriori algorithm is one of the most broadly used algorithms in ARM, and it collects the itemsets that frequently occur in order to discover association rules in massive datasets. The original Apriori algorithm is for sequential (single node or computer) environments. This Apriori algorithm has many drawbacks for processing huge datasets, such as that a single machine’s memory, CPU and storage capacity are insufficient. Parallel and distributed computing is the better solution to overcome the above problems. Many researchers have parallelized the Apriori algorithm. This study performs a survey on several well-enhanced and revised techniques for the parallel Apriori algorithm in the HadoopMapReduce environment. The Hadoop-MapReduce framework is a programming model that efficiently and effectively processes enormous databases in parallel. It can handle large clusters of commodity hardware in a reliable and fault-tolerant manner. This survey will provide an overall view of the parallel Apriori algorithm implementation in the Hadoop-MapReduce environment and briefly discuss the challenges and open issues of big data in the cloud and Hadoop-MapReduce. Moreover, this survey will not only give overall existing improved Apriori algorithm methods on Hadoop-MapReduce but also provide future research direction for upcoming researchers.
Summarizing Source Code with Transferred API Knowledge

Code summarization, aiming to generate succinct natural language description of source code, is extremely useful for code search and code comprehension. It has played an important role in software maintenance and evolution. Previous approaches generate summaries by retrieving summaries from similar code snippets. However, these approaches heavily rely on whether similar code snippets can be retrieved, how similar the snippets are, and fail to capture the API knowledge in the source code, which carries vital information about the functionality of the source code. In this paper, we propose a novel approach, named TL-CodeSum, which successfully uses API knowledge learned in a different but related task to code summarization. Experiments on large-scale real-world industry Java projects indicate that our approach is effective and outperforms the state-of-the-art in code summarization.
The hamstring syndrome. A new diagnosis of gluteal sciatic pain.

A series of 59 patients was treated and operated on for pain felt over the area of the ischial tuberosity and radiating down the back of the thigh. This condition was labeled as the "hamstring syndrome." Pain was typically incurred by assuming a sitting position, stretching the affected posterior thigh, and running fast. The patients usually had a history of recurrent hamstring "tears." Their symptoms were caused by the tight, tendinous structures of the lateral insertion area of the hamstring muscles to the ischial tuberosity. Upon division of these structures, complete relief was obtained in 52 of the 59 patients.
Web Usage Analysis and User Profiling

Today it is quite common for web page content to include an advertisement. Since advertisers often want to target their message to people with certain demographic attributes, the anonymity of Internet users poses a special problem for them. The purpose of the present research is to find an effective way to infer demographic information (e.g. gender, age or income) about people who use the Internet but for whom demographic information is not otherwise available. Our hope is to build a high quality database of demographic profiles covering a large segment of the Internet population without having to survey each individual Internet user. Though Internet users are largely anonymous, they nonetheless provide a certain amount of usage information. Usage information includes, but is not limited to, (a) search terms entered by the Internet user and (b) web pages accessed by the Internet user. In this paper, we describe an application of the Latent Semantic Analysis (LSA)  information retrieval technique to construct a vector space in which we can represent the usage data associated with each Internet user of interest. Subsequently, we show how the LSA vector space enables us to produce demographic inferences by supplying the input to a three layer neural model trained using the scaled conjugate gradient (SCG) method.
Louis Working Paper Series Beyond the Numbers : An Analysis of Optimistic and Pessimistic Language in Earnings Press Releases

In this paper, we examine whether managers use optimistic and pessimistic language in earnings press releases to provide information about expected future firm performance to the market, and whether the market responds to optimistic and pessimistic language usage in earnings press releases after controlling for the earnings surprise and other factors likely to influence the market’s response to the earnings announcement. We use textual-analysis software to measure levels of optimistic and pessimistic language for a sample of approximately 24,000 earnings press releases issued between 1998 and 2003. We find a positive (negative) association between optimistic (pessimistic) language usage and future firm performance and a significant incremental market response to optimistic and pessimistic language usage in earnings press releases. Results suggest managers use optimistic and pessimistic language to provide credible information about expected future firm performance to the market, and that the market responds to managers’ language usage.
Simulation of actuator dynamics based on geometric model of pneumatic artificial muscle

Dynamic simulation model of the actuator with two pneumatic artificial muscles in antagonistic connection was designed and built in Matlab Simulink environment. The basis for this simulation model was dynamic model of the pneumatic actuator based on advanced geometric muscle model. The main dynamics characteristics of such actuator were obtained by model simulation, as for example muscle force change, pressure change in muscle, arm position of the actuator. Simulation results will be used in design of control system of such actuator using model reference adaptive controller.
Query by Image and Video Content: The QBIC System

Semantic versus nonsemantic information icture yourself as a fashion designer needing images of fabrics with a particular mixture of colors, a museum cataloger looking P for artifacts of a particular shape and textured pattern, or a movie producer needing a video clip of a red car-like object moving from right to left with the camera zooming. How do you find these images? Even though today’s technology enables us to acquire, manipulate, transmit, and store vast on-line image and video collections, the search methodologies used to find pictorial information are still limited due to difficult research problems (see “Semantic versus nonsemantic” sidebar). Typically, these methodologies depend on file IDS, keywords, or text associated with the images. And, although powerful,
3D-Aided Face Recognition Robust to Expression and Pose Variations

Expression and pose variations are major challenges for reliable face recognition (FR) in 2D. In this paper, we aim to endow state of the art face recognition SDKs with robustness to facial expression variations and pose changes by using an extended 3D Morphable Model (3DMM) which isolates identity variations from those due to facial expressions. Specifically, given a probe with expression, a novel view of the face is generated where the pose is rectified and the expression neutralized. We present two methods of expression neutralization. The first one uses prior knowledge to infer the neutral expression image from an input image. The second method, specifically designed for verification, is based on the transfer of the gallery face expression to the probe. Experiments using rectified and neutralized view with a standard commercial FR SDK on two 2D face databases, namely Multi-PIE and AR, show significant performance improvement of the commercial SDK to deal with expression and pose variations and demonstrates the effectiveness of the proposed approach.
Controllable Electrochemical Synthesis of Copper Sulfides as Sodium-Ion Battery Anodes with Superior Rate Capability and Ultralong Cycle Life.

Sodium-ion batteries (SIBs) are prospective alternative to lithium-ion batteries for large-scale energy-storage applications, owing to the abundant resources of sodium. Metal sulfides are deemed to be promising anode materials for SIBs due to their low-cost and eco-friendliness. Herein, for the first time, series of copper sulfides (Cu2S, Cu7S4, and Cu7KS4) are controllably synthesized via a facile electrochemical route in KCl-NaCl-Na2S molten salts. The as-prepared Cu2S with micron-sized flakes structure is first investigated as anode of SIBs, which delivers a capacity of 430 mAh g-1 with a high initial Coulombic efficiency of 84.9% at a current density of 100 mA g-1. Moreover, the Cu2S anode demonstrates superior capability (337 mAh g-1 at 20 A g-1, corresponding to 50 C) and ultralong cycle performance (88.2% of capacity retention after 5000 cycles at 5 A g-1, corresponding to 0.0024% of fade rate per cycle). Meanwhile, the pseudocapacitance contribution and robust porous structure in situ formed during cycling endow the Cu2S anodes with outstanding rate capability and enhanced cyclic performance, which are revealed by kinetics analysis and ex situ characterization.
Strategies for Power Line Communications Smart Metering Network Deployment

Smart Grids are becoming a reality all over the world. Nowadays, the research efforts for the introduction and deployment of these grids are mainly focused on the development of the field of Smart Metering. This emerging application requires the use of technologies to access the significant number of points of supply (PoS) existing in the grid, covering the Low Voltage (LV) segment with the lowest possible costs. Power Line Communications (PLC) have been extensively used in electricity grids for a variety of purposes and, of late, have been the focus of renewed interest. PLC are really well suited for quick and inexpensive pervasive deployments. However, no LV grid is the same in any electricity company (utility), and the particularities of each grid evolution, architecture, circumstances and materials, makes it a challenge to deploy Smart Metering networks with PLC technologies, with the Smart Grid as an ultimate goal. This paper covers the evolution of Smart Metering networks, together with the evolution of PLC technologies until both worlds have converged to project PLC-enabled Smart Metering networks towards Smart Grid. This paper develops guidelines over a set of strategic aspects of PLC Smart Metering network deployment based on the knowledge gathered on real field; and introduces the future challenges of these networks in their evolution towards the Smart Grid.
DeepGauge: multi-granularity testing criteria for deep learning systems

Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. We have seen wide adoption of DL in many safety-critical scenarios. However, a plethora of studies have shown that the state-of-the-art DL systems suffer from various vulnerabilities which can lead to severe consequences when applied to real-world applications. Currently, the testing adequacy of a DL system is usually measured by the accuracy of test data. Considering the limitation of accessible high quality test data, good accuracy performance on test data can hardly provide confidence to the testing adequacy and generality of DL systems. Unlike traditional software systems that have clear and controllable logic and functionality, the lack of interpretability in a DL system makes system analysis and defect detection difficult, which could potentially hinder its real-world deployment. In this paper, we propose DeepGauge, a set of multi-granularity testing criteria for DL systems, which aims at rendering a multi-faceted portrayal of the testbed. The in-depth evaluation of our proposed testing criteria is demonstrated on two well-known datasets, five DL systems, and with four state-of-the-art adversarial attack techniques against DL. The potential usefulness of DeepGauge sheds light on the construction of more generic and robust DL systems.
Road edge and lane boundary detection using laser and vision

This paper presents a methodology for extracting road edge and lane information for smart and intelligent navigation of vehicles. The range information provided by a fast laser range-measuring device is processed by an extended Kalman filter to extract the road edge or curb information. The resultant road edge information is used to aid in the extraction of the lane boundary from a CCD camera image. Hough Transform (HT) is used to extract the candidate lane boundary edges, and the most probable lane boundary is determined using an Active Line Model based on minimizing an appropriate Energy function. Experimental results are presented to demonstrate the effectiveness of the combined Laser and Vision strategy for road-edge and lane boundary detection.