nils-herrmann/clean_scirepeval huggingface dataset:
R-CNNs for Pose Estimation and Action Detection

We present convolutional neural networks for the tasks of keypoint (pose) prediction and action classification of people in unconstrained images. Our approach involves training an R-CNN detector with loss functions depending on the task being tackled. We evaluate our method on the challenging PASCAL VOC dataset and compare it to previous leading approaches. Our method gives state-of-theart results for keypoint and action prediction. Additionally, we introduce a new dataset for action detection, the task of simultaneously localizing people and classifying their actions, and present results using our approach.
The DeLone and McLean Model of Information Systems Success: A Ten-Year Update

Ten years ago, we presented the DeLone and McLean Information Systems (IS) Success Model as a framework and model for measuring the complexdependent variable in IS research. In this paper, we discuss many of the important IS success research contributions of the last decade, focusing especially on research efforts that apply, validate, challenge, and propose enhancements to our original model. Based on our evaluation of those contributions, we propose minor refinements to the model and propose an updated DeLone and McLean IS Success Model. We discuss the utility of the updated model for measuring e-commerce system success. Finally, we make a series of recommendations regarding current and future measurement of IS success. 10 DELONE AND MCLEAN
A PSO and Pattern Search based Memetic Algorithm for SVMs Parameters Optimization

7 Addressing the issue of SVMs parameters optimization, this study proposes an efficient 8 memetic algorithm based on Particle Swarm Optimization algorithm (PSO) and Pattern Search 9 (PS). In the proposed memetic algorithm, PSO is responsible for exploration of the search space 10 and the detection of the potential regions with optimum solutions, while pattern search (PS) is 11 used to produce an effective exploitation on the potential regions obtained by PSO. Moreover, a 12 novel probabilistic selection strategy is proposed to select the appropriate individuals among the 13 current population to undergo local refinement, keeping a well balance between exploration and 14 exploitation. Experimental results confirm that the local refinement with PS and our proposed 15 selection strategy are effective, and finally demonstrate effectiveness and robustness of the 16 proposed PSO-PS based MA for SVMs parameters optimization.
Design and implementation of a door lock control based on a near field communication of a smartphone

In this paper we propose an integration design of both a near field communication (NFC) and a smartphone to achieve a door lock control system. This design consists of a built-in NFC capabilities of a smartphone combined with a dedicated application deemed to be a key to open the door by means of the logical link control protocol (LLCP) exchange together with a time stamp to match the user's own set of password information to verify who is a permissions user or not. When verified the specific door which is secured by this door lock control system immediately opens.
Adaptive neuro fuzzy controller for adaptive compliant robotic gripper

The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult no control using conventional techniques. Here, a novel design of an adaptive neuro fuzzy inference strategy (ANFIS) for controlling input displacement of a new adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Since the conventional control strategy is a very challenging task, fuzzy logic based controllers are considered as potential candidates for such an application. Fuzzy based controllers develop a control signal which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS controller, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.
A generalizable NLP framework for fast development of pattern-based biomedical relation extraction systems

Text mining is increasingly used in the biomedical domain because of its ability to automatically gather information from large amount of scientific articles. One important task in biomedical text mining is relation extraction, which aims to identify designated relations among biological entities reported in literature. A relation extraction system achieving high performance is expensive to develop because of the substantial time and effort required for its design and implementation. Here, we report a novel framework to facilitate the development of a pattern-based biomedical relation extraction system. It has several unique design features: (1) leveraging syntactic variations possible in a language and automatically generating extraction patterns in a systematic manner, (2) applying sentence simplification to improve the coverage of extraction patterns, and (3) identifying referential relations between a syntactic argument of a predicate and the actual target expected in the relation extraction task. A relation extraction system derived using the proposed framework achieved overall F-scores of 72.66% for the Simple events and 55.57% for the Binding events on the BioNLP-ST 2011 GE test set, comparing favorably with the top performing systems that participated in the BioNLP-ST 2011 GE task. We obtained similar results on the BioNLP-ST 2013 GE test set (80.07% and 60.58%, respectively). We conducted additional experiments on the training and development sets to provide a more detailed analysis of the system and its individual modules. This analysis indicates that without increasing the number of patterns, simplification and referential relation linking play a key role in the effective extraction of biomedical relations. In this paper, we present a novel framework for fast development of relation extraction systems. The framework requires only a list of triggers as input, and does not need information from an annotated corpus. Thus, we reduce the involvement of domain experts, who would otherwise have to provide manual annotations and help with the design of hand crafted patterns. We demonstrate how our framework is used to develop a system which achieves state-of-the-art performance on a public benchmark corpus.
V3: a vehicle-to-vehicle live video streaming architecture

We consider the problem of how to enable a live video streaming service to vehicles in motion. In such applications, the video source can be a typical video server or vehicles with appropriate capability, while the video receivers are vehicles that are driving on the road. An infrastructure-based approach relies on strategically deployed base stations and video servers to forward video data to nearby vehicles. While this approach can provide a streaming video service to certain vehicles, it suffers from high base station deployment and maintenance cost. In this paper, we propose V3, an architecture to provide a live video streaming service to driving vehicles through vehicle-to-vehicle (V2V) networks. We argue that this solution is practical with the advance of wireless ad-hoc network techniques. With ample engine power, powerful computing capability and considerable data storage that a vehicle can provide, it is reasonable to support data-intensive video streaming service. On the other hand, V2V video streaming can be challenging because: 1) the V2V network may be persistently partitioned, and 2) the video sources are mobile and transient. V3 addresses these challenges by incorporating a novel signaling mechanism to continuously trigger video sources to send video data back to receivers. It also adopts a store-carry-and-forward approach to transmit video data in a partitioned network environment. Several algorithms are proposed to balance the video transmission delay and bandwidth overheads Simulation experiments demonstrate the feasibility of supporting vehicle-to-vehicle live video streaming with acceptable performance.
IAM-OnDB - an on-line English sentence database acquired from handwritten text on a whiteboard

In this paper we present IAM-OnDB - a new large online handwritten sentences database. It is publicly available and consists of text acquired via an electronic interface from a whiteboard. The database contains about 86 K word instances from an 11 K dictionary written by more than 200 writers. We also describe a recognizer for unconstrained English text that was trained and tested using this database. This recognizer is based on hidden Markov models (HMMs). In our experiments we show that by using larger training sets we can significantly increase the word recognition rate. This recognizer may serve as a benchmark reference for future research.
The Importance of Integrating Cost Management with Building Information Modeling ( BIM )

The UK construction industry has been branded as an inefficient, fragmented and non-value delivering industry by prominent critics such as Michael Latham and Sir. John Egan; both have insisted on the need to change the way the industry delivers and manages assets through integrated project processes. Sir. John Egan specifically highlighted the need for significant reduction in project time and cost. As a result, for the last few years the main emphasis within the construction industry is on integrating various project processes by using the integrated approach and enabling technologies that bring all the stakeholders in a close relationship for achieving greater success. One of the most critical project processes is the cost management that involves cost estimating, control of expenditure and cost advice on cash flow and payments. Furthermore, Building Information Modeling (BIM) has emerged as a very powerful approach and set of information technologies that allows the project stakeholders to work collaboratively on highly technical and comprehensive models using parametric design components and visualize design in 3D. The UK government has made the delivery of public procured projects through BIM mandatory from 2016. Thus, it has become critical to investigate the prospects of cost management practice in this context and determination of how BIM can help in its improvement. The aim of this paper is to examine the importance of BIM in the UK construction sector with a specific focus on cost management through a state-of-the-art review of literature in order to highlight the significance of BIM for potential improvements in area of cost management.
Satellite handover techniques for LEO networks

Low earth orbit (LEO) satellite constellations could play an important role in future mobile communication networks due to their characteristics, such as global coverage and low propagation delays. However, because of the non-stationarity of the satellites, a call may be subjected to handovers, which can be cell or satellite handovers. Quite many techniques have been proposed in the literature dealing with the cell handover issue. In this paper, a satellite handover procedure is proposed, that investigates and exploits the partial satellite diversity (namely, the existing common coverage area between contiguous satellites) in order to provide an efficient handover strategy, based always on a tradeoff of the blocking and forced termination probabilities for a fair treatment of new and handover calls. Three different criteria were examined for the selection of a satellite. Each one of them could be applied either to new or handover calls, therefore we investigated nine different service schemes. A simulation tool was implemented in order to compare the different service schemes and simulation results are presented at the end of the paper.
Texture overlay onto deformable surface for virtual clothing

In this article, we describe a method for overlaying arbitrary texture image onto surface of T-shirt worn by a user. In this method, the texture image is previously divided into a number of patches. On the T-shirt, markers are printed at the positions corresponding to the vertices of the patches. The markers on the surface of the T-shirt are tracked in the motion image taken by a camera. The texture image is warped according to the tracked positions of the markers, which is overlaid onto the captured image. This article presents experimental results with the pilot system of virtual clothing implemented based on the proposed method.
Multilevel Spin-Orbit Torque MRAMs

In this paper, we present two multilevel spin-orbit torque magnetic random access memories (SOT-MRAMs). A single-level SOT-MRAM employs a three-terminal SOT device as a storage element with enhanced endurance, close-to-zero read disturbance, and low write energy. However, the three-terminal device requires the use of two access transistors per cell. To improve the integration density, we propose two multilevel cells (MLCs): 1) series SOT MLC and 2) parallel SOT MLC, both of which store two bits per memory cell. A detailed analysis of the bit-cell suggests that the S-MLC is promising for applications requiring both high density and low write-error rate, and P-MLC is particularly suitable for high-density and low-write-energy applications. We also performed iso-bit-cell area comparison of our MLC designs with previously proposed MLCs that are based on spin-transfer torque MRAM and show 3-16× improvement in write energy.
Structural Representations for Learning Relations between Pairs of Texts

This paper studies the use of structural representations for learning relations between pairs of short texts (e.g., sentences or paragraphs) of the kind: the second text answers to, or conveys exactly the same information of, or is implied by, the first text. Engineering effective features that can capture syntactic and semantic relations between the constituents composing the target text pairs is rather complex. Thus, we define syntactic and semantic structures representing the text pairs and then apply graph and tree kernels to them for automatically engineering features in Support Vector Machines. We carry out an extensive comparative analysis of stateof-the-art models for this type of relational learning. Our findings allow for achieving the highest accuracy in two different and important related tasks, i.e., Paraphrasing Identification and Textual Entailment Recognition.
MIR In Matlab: The MIDI Toolbox

150-200 words) The MIDI Toolbox is a compilation of functions for analyzing and visualizing MIDI files in the Matlab computing environment. In this article, the basic issues of the Toolbox are summarized and demonstrated with examples ranging from melodic contour, similarity, keyfinding, meter-finding to segmentation. The Toolbox is based on symbolic musical data but signal processing methods are applied to cover such aspects of musical behaviour as geometric representations and short-term memory. Besides simple manipulation and filtering functions, the toolbox contains cognitively inspired analytic techniques that are suitable for contextdependent musical analysis, a prerequisite for many music information retrieval applications.
Coordinating Text and Graphics in Explanation Generation

To generate multimedia explanations, a system must be able to coordinate the use of different media in a single explanation. In this paper, we present an architecture that we have developed for COMET (COordinated Multimedia Explanation Testbed), a system that generates directions for equipment maintenance and repair, and we show how it addresses the coordination problem. In particular, we focus on the use of a single content planner that produces a common content description used by multiple media-specific generators, a media coordinator that makes a f'me-grained division of information between media, and bidirectional interaction between media-specific generators to allow influence across media.
Plant Disease Diagnosis Based on Image Processing, Appropriate for Mobile Phone Implementation

A steady plant monitoring is necessary to control the spread of a disease but its cost may be high and as a result, the producers often skip critical preventive procedures to keep the production cost low. Although, official disease recognition is a responsibility of professional agriculturists, low cost observation and computational assisted diagnosis can effectively help in the recognition of a plant disease in its early stages. The most important symptoms of a disease such as lesions in the leaves, fruits, stems, etc, are visible. The features (color, area, number of spots) of these lesions can form significant decision criteria supplemented by other more expensive molecular analyses and tests that can follow. An image processing technique capable of recognizing the plant lesion features is described in this paper. The low complexity of this technique can allow its implementation on mobile phones. The achieved accuracy is higher than 90% according to the experimental results.
Sub-nanosecond pulse generator for through-the-wall radar application

This paper presents a sub-nanosecond pulse generator intended for a transmitter of through-the-wall surveillance radar. The basis of the generator is a step recovery diode, which is used to sharpen the slow rise time edge of an input driving waveform. A unique pulse shaping technique is then applied to form an ultra-wideband Gaussian pulse. A simple transistor switching circuit was used to drive this Gaussian pulser, which transforms a TTL trigger signal to a driving pulse with the timing and amplitude parameters required by the step recovery diode. The maximum pulse repetition frequency of the generator is 20 MHz. High amplitude pulses are advantageous for obtaining a good radar range, especially when penetrating thick lossy walls. In order to increase the output power of the transmitter, the outputs of two identical generators were connected in parallel. The measurement results are presented, which show waveforms of the generated Gaussian pulses approximately 180 ps in width and over 32 V in amplitude.
Toward efficient and privacy-preserving computing in big data era

Big data, because it can mine new knowledge for economic growth and technical innovation, has recently received considerable attention, and many research efforts have been directed to big data processing due to its high volume, velocity, and variety (referred to as "3V") challenges. However, in addition to the 3V challenges, the flourishing of big data also hinges on fully understanding and managing newly arising security and privacy challenges. If data are not authentic, new mined knowledge will be unconvincing; while if privacy is not well addressed, people may be reluctant to share their data. Because security has been investigated as a new dimension, "veracity," in big data, in this article, we aim to exploit new challenges of big data in terms of privacy, and devote our attention toward efficient and privacy-preserving computing in the big data era. Specifically, we first formalize the general architecture of big data analytics, identify the corresponding privacy requirements, and introduce an efficient and privacy-preserving cosine similarity computing protocol as an example in response to data mining's efficiency and privacy requirements in the big data era.
Pair programming as a teaching tool: a student review of empirical studies

The aims of this research were to investigate the applicability of the Systematic Literature Review (SLR) process within the constraints of a 13-week master’s level project and to aggregate evidence about the effectiveness of pair programming for teaching introductory programming. It was found that, with certain modifications to the process, it was possible to undertake an SLR within a limited time period and to produce valid results. Based on pre-defined inclusion and exclusion criteria, the student found 28 publications reporting empirical studies of pair programming, of which nine publications were used for data extraction and analysis. Results of the review indicates that whilst pair programming has little effect on the marks obtained for examinations and assignments, it can significantly improve the pass and retention rates and the students’ confidence and enjoyment of programming. Following the student study, experienced reviewers re-applied the inclusion and exclusion criteria to the 28 publications and carried out data extraction and synthesis using the resulting papers. A comparison of the student’s results and those of the experienced reviewers is presented.
Fast image blending using watersheds and graph cuts

Please cite this article in press as: N. Gra doi:10.1016/j.imavis.2008.04.014 This paper presents a novel approach for combining a set of registered images into a composite mosaic with no visible seams and minimal texture distortion. To promote execution speed in building large area mosaics, the mosaic space is divided into disjoint regions of image intersection based on a geometric criterion. Pair-wise image blending is performed independently in each region by means of watershed segmentation and graph cut optimization. A contribution of this work – use of watershed segmentation on image differences to find possible cuts over areas of low photometric difference – allows for searching over a much smaller set of watershed segments, instead of over the entire set of pixels in the intersection zone. Watershed transform seeks areas of low difference when creating boundaries of each segment. Constraining the overall cutting lines to be a sequence of watershed segment boundaries results in significant reduction of search space. The solution is found efficiently via graph cut, using a photometric criterion. The proposed method presents several advantages. The use of graph cuts over image pairs guarantees the globally optimal solution for each intersection region. The independence of such regions makes the algorithm suitable for parallel implementation. The separated use of the geometric and photometric criteria leads to reduced memory requirements and a compact storage of the input data. Finally, it allows the efficient creation of large mosaics, without user intervention. We illustrate the performance of the approach on image sequences with prominent 3-D content and moving objects.
Management and Follow-Up of a Case of Gestational Gigantomastia in a Brazilian Hospital

Gigantomastia is a breast disorder that is associated with an exaggerated, rapid growth of the breasts, generally bilaterally. Since the pathology is rare and has seldom been described, its etiology has yet to be fully established, although there are speculations that a hormonal component may play an important role. Treatment is aimed at improving the clinical and psychological symptoms; however, the best therapeutic option varies from case to case. The present report describes a case of gestational gigantomastia seen at the Department of Obstetrics and Gynecology of the Hospital da Santa Casa de Misericórdia, Vitória, Espírito Santo, Brazil, in a primigravida in the second trimester of pregnancy. The report follows this patient from her diagnosis until the completion of treatment with a third and final surgical procedure.