nils-herrmann/clean_scirepeval huggingface dataset:
Integron Involvement in Environmental Spread of Antibiotic Resistance

The spread of antibiotic-resistant bacteria is a growing problem and a public health issue. In recent decades, various genetic mechanisms involved in the spread of resistance genes among bacteria have been identified. Integrons - genetic elements that acquire, exchange, and express genes embedded within gene cassettes (GC) - are one of these mechanisms. Integrons are widely distributed, especially in Gram-negative bacteria; they are carried by mobile genetic elements, plasmids, and transposons, which promote their spread within bacterial communities. Initially studied mainly in the clinical setting for their involvement in antibiotic resistance, their role in the environment is now an increasing focus of attention. The aim of this review is to provide an in-depth analysis of recent studies of antibiotic-resistance integrons in the environment, highlighting their potential involvement in antibiotic-resistance outside the clinical context. We will focus particularly on the impact of human activities (agriculture, industries, wastewater treatment, etc.).
Structural insights into energy regulation of light-harvesting complex CP29 from spinach

CP29, one of the minor light-harvesting complexes of higher-plant photosystem II, absorbs and transfers solar energy for photosynthesis and also has important roles in photoprotection. We have solved the crystal structure of spinach CP29 at 2.80-Å resolution. Each CP29 monomer contains 13 chlorophyll and 3 carotenoid molecules, which differs considerably from the major light-harvesting complex LHCII and the previously proposed CP29 model. The 13 chlorophyll-binding sites are assigned as eight chlorophyll a sites, four chlorophyll b and one putative mixed site occupied by both chlorophylls a and b. Based on the present X-ray structure, an integrated pigment network in CP29 is constructed. Two special clusters of pigment molecules, namely a615–a611–a612–Lut and Vio(Zea)–a603–a609, have been identified and might function as potential energy-quenching centers and as the exit or entrance in energy-transfer pathways.
Cumulative trauma: the impact of child sexual abuse, adult sexual assault, and spouse abuse.

The present study investigated the relationship between trauma symptoms and a history of child sexual abuse, adult sexual assault, and physical abuse by a partner as an adult. While there has been some research examining the correlation between individual victimization experiences and traumatic stress, the cumulative impact of multiple victimization experiences has not been addressed. Subjects were recruited from psychological clinics and community advocacy agencies. Additionally, a nonclinical undergraduate student sample was evaluated. The results of this study indicate not only that victimization and revictimization experiences are frequent, but also that the level of trauma specific symptoms are significantly related to the number of different types of reported victimization experiences. The research and clinical implications of these findings are discussed.
Modeling Local Coherence: An Entity-Based Approach

This article proposes a novel framework for representing and measuring local coherence. Central to this approach is the entity-grid representation of discourse, which captures patterns of entity distribution in a text. The algorithm introduced in the article automatically abstracts a text into a set of entity transition sequences and records distributional, syntactic, and referential information about discourse entities. We re-conceptualize coherence assessment as a learning task and show that our entity-based representation is well-suited for ranking-based generation and text classification tasks. Using the proposed representation, we achieve good performance on text ordering, summary coherence evaluation, and readability assessment.
3D object class detection in the wild

Object class detection has been a synonym for 2D bounding box localization for the longest time, fueled by the success of powerful statistical learning techniques, combined with robust image representations. Only recently, there has been a growing interest in revisiting the promise of computer vision from the early days: to precisely delineate the contents of a visual scene, object by object, in 3D. In this paper, we draw from recent advances in object detection and 2D-3D object lifting in order to design an object class detector that is particularly tailored towards 3D object class detection. Our 3D object class detection method consists of several stages gradually enriching the object detection output with object viewpoint, keypoints and 3D shape estimates. Following careful design, in each stage it constantly improves the performance and achieves state-of-the-art performance in simultaneous 2D bounding box and viewpoint estimation on the challenging Pascal3D+  dataset.
High Performance Implementation of 3D Convolutional Neural Networks on a GPU

Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory. This strategy was shown to be successful for 2D neural networks. We implement the algorithm for 3D convolutional neural networks and apply it to a popular 3D convolutional neural network which is used to classify videos and compare it to cuDNN. For our highly optimized implementation of the algorithm, we observe a twofold speedup for most of the 3D convolution layers of our test network compared to the cuDNN version.
Speech Recognition using Artificial Neural Network

Humans prefer to interact with each other using speech. Since this is the most natural mode of communication, the humans also want to interact with machines using speech only. So, automatic speech recognition has gained a lot of popularity. Different approaches for speech recognition exists like Hidden Markov Model (HMM), Dynamic Time Warping (DTW), Vector Quantization (VQ), etc. This paper uses Neural Network (NN) along with Mel Frequency Cepstrum Coefficients (MFCC) for speech recognition. Mel Frequency Cepstrum Coefiicients (MFCC) has been used for the feature extraction of speech. This gives the feature of the waveform. For pattern matching FeedForward Neural Network with Back propagation algorithm has been applied. The paper analyzes the various training algorithms present for training the Neural Network and uses train scg for the experiment. The work has been done on MATLAB and experimental results show that system is able to recognize words at sufficiently high accuracy.
Texture based feature extraction methods for content based medical image retrieval systems.

The developments of content based image retrieval (CBIR) systems used for image archiving are continued and one of the important research topics. Although some studies have been presented general image achieving, proposed CBIR systems for archiving of medical images are not very efficient. In presented study, it is examined the retrieval efficiency rate of spatial methods used for feature extraction for medical image retrieval systems. The investigated algorithms in this study depend on gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), and Gabor wavelet accepted as spatial methods. In the experiments, the database is built including hundreds of medical images such as brain, lung, sinus, and bone. The results obtained in this study shows that queries based on statistics obtained from GLCM are satisfied. However, it is observed that Gabor Wavelet has been the most effective and accurate method.
Next-generation sequencing technologies and their impact on microbial genomics.

Next-generation sequencing technologies have had a dramatic impact in the field of genomic research through the provision of a low cost, high-throughput alternative to traditional capillary sequencers. These new sequencing methods have surpassed their original scope and now provide a range of utility-based applications, which allow for a more comprehensive analysis of the structure and content of microbial genomes than was previously possible. With the commercialization of a third generation of sequencing technologies imminent, we discuss the applications of current next-generation sequencing methods and explore their impact on and contribution to microbial genome research.
Mapping emotion to color

The sensitive artificial listener (SAL) project is interested in ways to elicit emotions from humans. This can be done in several ways; one of them is by presenting colors. The use of colors to stimulate a certain feeling, may it be calm, aggressive, energetic, happy etc. This paper tries to provide a method for generating colors to elicit a certain feelings based on an emotional state from the SAL agent. This is done by mapping the emotional state of the agent into a color. The emotional state is represented by two values Pleasure and Arousal these two form the two dimensional space in which the distinctive emotions can be placed. The emotional state of the agent is a point (coordinate) on the same 2d space, and by looking at the position of this point the current corresponding color can be calculated by interpolating between the emotions on the 2d space. The end use is to use colors elicit a certain feeling in the user, how the agent uses this to his advantage is up to the agent. Examples of use can be, the agents virtual body changes color (expressing his emotion), or the complete virtual world gets a change in color glow (a narrow emotional commitment), or a more physical example the lights in your house change colors (a broad emotional commitment).
Mindfulness and self-compassion as predictors of psychological wellbeing in long-term meditators and matched nonmeditators

(2012): Mindfulness and self-compassion as predictors of psychological wellbeing in long-term meditators and matched nonmeditators, The Journal of Positive Psychology: This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.
Implementation of a reticle seeker missile simulator for jamming effect analysis

In this paper, we implement a reticle seeker missile simulator on MATLAB-SIMULINK to analyze the jamming effect of the spin-scan and conscan reticle seeker. The DIRCM (Directed Infrared Countermeasures) system uses the pulsing flashes of infrared (IR) energy and its frequency and intensity have influence on the missile guidance system. Our simulation results show that jamming effect is indicated significantly when jammer frequency and reticle frequency are similar and present a 3D trajectory of missile motions by jamming.
Survey on various door lock access control mechanisms

In day to day life, security of an object or property plays a major role. Nowadays, security is the major threat faced by most of the organizations; hence security is gaining more importance in these days. This paper gives a survey on various automatic identification and access control mechanisms that have been used over the years to prevent unauthorized access. In olden days, for high security zones like locker rooms for banks, military sites etc, traditional lock systems or passwords were employed. But this solution was not secure. Due to the advancements in technology RFID cards were used, but this was not useful for the user due to the chance of getting lost, forgotten or stolen. Later various door lock security systems based on biometrics, GSM, OTP, cryptography etc were developed. A lot of research is going on various automatic door lock systems and can expect more secure systems in the upcoming years.
Calculation and three-dimensional analysis of electric and magnetic fields on transmission lines with MATLAB®

The concern with electric and magnetic fields generated by transmission lines grew after the publication of the report by Wertheimer and Leeper (1979), pointing out a possible association between childhood cancer and these fields. As a precaution, standards were created establishing limits for human exposure to such fields, making them to be calculated and analyzed even in the design phase. In order to facilitate this analysis, this work proposes a software, based on MATLAB®, that calculates the fields generated by lines with multiple circuits. 2D and 3D graphics are provided as results. The validation of the proposal was made through several case studies, in which transmission lines of the Companhia Hidro Eletrica do São Francisco were analyzed by the software and the results were compared with the reports of studies made by the company. One of these case studies is presented.
T-HOG: An effective gradient-based descriptor for single line text regions

We discuss the use of histogram of oriented gradients (HOG) d escriptors as an effective tool for text description and recognition. Specifically, we propose a HOG-based texture descriptor (THOG) that uses a partition of the image into overlapping hori zontal cells with gradual boundaries, to characterize single-line texts in outdoor scenes. The in put of our algorithm is a rectangular image presumed to contain a single line of text in Roman-like cha racters. The output is a relatively short descriptor, that provides an effective input to an SVM classifier. Extensive experiments show that the T-HOG is more accurate than Dalal and Triggs’s origi nal HOG-based classifier, for any descriptor size. In addition, we show that the T-HOG is an eff ective tool for text/non-text discrimination and can be used in various text detection application s. In particular, combining T-HOG with a permissive bottom-up text detector is shown to outperform state-of-the-art text detection systems in two major publicly available databases.
Design of a robotic endoscope holder for sinus surgery

During endoscopic sinus surgery, to perform complex surgical procedures in the delicate nasal cavity, endoscope holding devices are needed. This paper presents a novel robotic system with safe locking and easy to release system that assists surgeons in holding the endoscope. The presented robotic system has eleven degrees of freedom (DOF). The robot consists of an elevator mechanism, a positioning arm and a fine adjustment device. The positioning arm which adopts a negatively actuated air-locking system, consist of three modular rotational joints and two modular ball joints. Since the positioning arm has a structure such that it normally assumes a locked state, and an unlocked state only when necessary, the locked state of the arm can be maintained even if any trouble occurs, causing no falling of the whole robot. The mechanical models of the modular rotational joints and modular ball joints have been derived to guide the detailed design of the prototype of the robot. The workspace of the robot has also been analyzed. Preliminary experiments with manikin have been conducted to demonstrate the performance of the robot.
Mechanical design, modelling and control of a novel aerial manipulator

In this paper a novel aerial manipulation system is proposed. The mechanical structure of the system, the number of thrusters and their geometry will be derived from technical optimization problems. The aforementioned problems are defined by taking into consideration the desired actuation forces and torques applied to the end-effector of the system. The framework of the proposed system is designed in a CAD Package in order to evaluate the system parameter values. Following this, the kinematic and dynamic models are developed and an adaptive backstepping controller is designed aiming to control the exact position and orientation of the end-effector in the Cartesian space. Finally, the performance of the system is demonstrated through a simulation study, where a manipulation task scenario is investigated.
Tradeoffs in displaying peripheral information

Peripheral information is information that is not central to a person's current task, but provides the person the opportunity to learn more, to do a better job, or to keep track of less important tasks. Though peripheral information displays are ubiquitous, they have been rarely studied. For computer users, a common peripheral display is a scrolling text display that provides announcements, sports scores, stock prices, or other news. In this paper, we investigate how to design peripheral displays so that they provide the most information while having the least impact on the user's performance on the main task. We report a series of experiments on scrolling displays aimed at examining tradeoffs between distraction of scrolling motion and memorability of information displayed. Overall, we found that continuously scrolling displays are more distracting than displays that start and stop, but information in both is remembered equally well. These results are summarized in a set of design recommendations.
Key Trends, Skills, and Knowledge Required for the Supply Chain Manager of the Future

Based on the results of a series of roundtables and the surveys from a recent research project sponsored by the Center for Advanced Purchasing Studies, there appears to be several major themes that emerge from the research that provide some clear messages for supply chain management education and training requirements. This report is based on the general premise that as the supply chain management environment is changing, there is a changing skill set required for success. Some of the primary elements identified with respect to the new set of requirements for supply management include: q Great pressure for cost reduction due to globalization q A greater demand for performance from internal customers, q A greater need to integrate and exploit supply base technologies and capabilities, q An increased focus on outsourcing and strategic-value added relationships, q Increasing focus on the supply chain q Increasing need to capture total cost and establish the business case q Increasing need for technology integration and e-procurement deployment The top trends mentioned in the focus groups effectively triangulate with the results of the survey. As shown in Table 1, at least eight of the fourteen trends most often brought up as a key trend in the focus groups were also ranked in the top ten trends ranked by executives in the survey. This analysis ensures that the results triangulate
Adaptive demand-driven multicast routing in multi-hop wireless ad hoc networks

The use of on-demand techniques in routing protocols for multi-hop wireless ad hoc networks has been shown to have significant advantages in terms of reducing the routing protocol's overhead and improving its ability to react quickly to topology changes in the network. A number of on-demand multicast routing protocols have been proposed, but each also relies on significant periodic (non-on-demand) behavior within portions of the protocol. This paper presents the design and initial evluation of the Adaptive Demand-Driven Multicast Routing protocol (ADMR), a new on-demand ad hoc network multicast routing protocol that attemps to reduce as much as possible any non-on-demand components within the protocol. Multicast routing state is dynamically established and maintained only for active groups and only in nodes located between multicast senders and receivers. Each multicast data packet is forwarded along the shortest-delay path with multicast forwarding state, from the sender to the receivers, and receivers dynamically adapt to the sending pattern of senders in order to efficiently balance overhead and maintenance of the multicast routing state as nodes in the network move or as wireless transmission conditions in the network change. We describe the operation of the ADMR protocol and present an initial evaluation of its performance based on detailed simulation in ad hoc networks of 50 mobile nodes. We show that ADMR achieves packet delivery ratios within 1% of a flooding-based protocol, while incurring half to a quarter of the overhead.
The neurophysiology of memory.

How do the structures of the medial temporal lobe contribute to memory? To address this question, we examine the neurophysiological correlates of both recognition and associative memory in the medial temporal lobe of humans, monkeys, and rats. These cross-species comparisons show that the patterns of mnemonic activity observed throughout the medial temporal lobe are largely conserved across species. Moreover, these findings show that neurons in each of the medial temporal lobe areas can perform both similar as well as distinctive mnemonic functions. In some cases, similar patterns of mnemonic activity are observed across all structures of the medial temporal lobe. In the majority of cases, however, the hippocampal formation and surrounding cortex signal mnemonic information in distinct, but complementary ways.
Fuzzy rule based classification systems for big data with MapReduce: granularity analysis

Due to the vast amount of information available nowadays, and the advantages related to the processing of this data, the topics of big data and data science have acquired a great importance in the current research. Big data applications are mainly about scalability, which can be achieved via the MapReduce programming model.It is designed to divide the data into several chunks or groups that are processed in parallel, and whose result is “assembled” to provide a single solution. Among different classification paradigms adapted to this new framework, fuzzy rule based classification systems have shown interesting results with a MapReduce approach for big data. It is well known that the performance of these types of systems has a strong dependence on the selection of a good granularity level for the Data Base. However, in the context of MapReduce this parameter is even harder to determine as it can be also related with the number ofMaps chosen for the processing stage. In this paper, we aim at analyzing the interrelation between the number of labels of the fuzzy variables and the scarcity of the data due to the data sampling in MapReduce. Specifically, we consider that as the partitioning of the initial instance set grows, the level of granularity necessary to B Alberto Fernández alberto@decsai.ugr.es Sara del Río srio@decsai.ugr.es Abdullah Bawakid abawakid@kau.edu.sa Francisco Herrera herrera@decsai.ugr.es 1 Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain 2 Faculty of Computing and Information Technology, King Abdulaziz University (KAU), Jeddah, Saudi Arabia