nils-herrmann/clean_scirepeval huggingface dataset:
Smart drip irrigation system using raspberry pi and arduino

This paper proposes a design for home automation system using ready-to-use, cost effective and energy efficient devices including raspberry pi, arduino microcontrollers, xbee modules and relay boards. Use of these components results in overall cost effective, scalable and robust implementation of system. The commands from the user are processed at raspberry pi using python programming language. Arduino microcontrollers are used to receive the on/off commands from the rasperry pi using zigbee protocol. Star zigbee topology serves as backbone for the communication between raspberry pi and end devices. Raspberry pi acts a central coordinator and end devices act as various routers. Low-cost and energy efficient drip irrigation system serves as a proof of concept. The design can be used in big agriculture fields as well as in small gardens via just sending an email to the system to water plants. The use of ultrasound sensors and solenoid valves make a smart drip irrigation system. The paper explains the complete installation of the system including hardware and software aspects. Experimental set-up is also tested and explained for an automatic drip irrigation system to water 50 pots.
SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports

We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in realtime for mobile platforms. In this paper, we describe how the project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors.
Data-driven biped control

We present a dynamic controller to physically simulate underactuated three-dimensional full-body biped locomotion. Our datadriven controller takes motion capture reference data to reproduce realistic human locomotion through realtime physically based simulation. The key idea is modulating the reference trajectory continuously and seamlessly such that even a simple dynamic tracking controller can follow the reference trajectory while maintaining its balance. In our framework, biped control can be facilitated by a large array of existing data-driven animation techniques because our controller can take a stream of reference data generated on-thefly at runtime. We demonstrate the effectiveness of our approach through examples that allow bipeds to turn, spin, and walk while steering its direction interactively. CR Categories: I.3.7 [Three-Dimensional Graphics and Realism]:
Empirically Evaluating the Quality of Automatically Generated and Manually Written Test Suites

The creation, execution, and maintenance of tests are some of the most expensive tasks in software development. To help reduce the cost, automated test generation tools can be used to assist and guide developers in creating test cases. Yet, the tests that automated tools produce range from simple skeletons to fully executable test suites, hence their complexity and quality vary. This paper compares the complexity and quality of test suites created by sophisticated automated test generation tools to that of developer-written test suites. The empirical study in this paper examines ten real-world programs with existing test suites and applies two state-of-the-art automated test generation tools. The study measures the resulting test suite quality in terms of code coverage and fault-finding capability. On average, manual tests covered 31.5% of the branches while the automated tools covered 31.8% of the branches. In terms of mutation score, the tests generated by automated tools had an average mutation score of 39.8% compared to the average mutation score of 42.1% for manually written tests. Even though automatically created tests often contain more lines of source code than those written by developers, this paper's empirical results reveal that test generation tools can provide value by creating high quality test suites while reducing the cost and effort needed for testing.
Errors in laboratory medicine.

The problem of medical errors has recently received a great deal of attention, which will probably increase. In this minireview, we focus on this issue in the fields of laboratory medicine and blood transfusion.



We conducted several MEDLINE queries and searched the literature by hand. Searches were limited to the last 8 years to identify results that were not biased by obsolete technology. In addition, data on the frequency and type of preanalytical errors in our institution were collected.



Our search revealed large heterogeneity in study designs and quality on this topic as well as relatively few available data and the lack of a shared definition of "laboratory error" (also referred to as "blunder", "mistake", "problem", or "defect"). Despite these limitations, there was considerable concordance on the distribution of errors throughout the laboratory working process: most occurred in the pre- or postanalytical phases, whereas a minority (13-32% according to the studies) occurred in the analytical portion. The reported frequency of errors was related to how they were identified: when a careful process analysis was performed, substantially more errors were discovered than when studies relied on complaints or report of near accidents.



The large heterogeneity of literature on laboratory errors together with the prevalence of evidence that most errors occur in the preanalytical phase suggest the implementation of a more rigorous methodology for error detection and classification and the adoption of proper technologies for error reduction. Clinical audits should be used as a tool to detect errors caused by organizational problems outside the laboratory.
Recommendation Systems in Software Engineering

Software engineering is a knowledge-intensive activity that presents many information navigation challenges. Information spaces in software engineering include the source code and change history of the software, discussion lists and forums, issue databases, component technologies and their learning resources, and the development environment. The technical nature, size, and dynamicity of these information spaces motivate the development of a special class of applications to support developers: recommendation systems in software engineering (RSSEs), which are software applications that provide information items estimated to be valuable for a software engineering task in a given context. In this introduction, we review the characteristics of information spaces in software engineering, describe the unique aspects of RSSEs, present an overview of the issues and considerations involved in creating, evaluating, and using RSSEs, and present a general outlook on the current state of research and development in the field of recommendation systems for highly technical domains.
A simple regression method for mapping quantitative trait loci in line crosses using flanking markers

The use of flanking marker methods has proved to be a powerful tool for the mapping of quantitative trait loci (QTL) in the segregating generations derived from crosses between inbred lines. Methods to analyse these data, based on maximum-likelihood, have been developed and provide good estimates of QTL effects in some situations. Maximum-likelihood methods are, however, relatively complex and can be computationally slow. In this paper we develop methods for mapping QTL based on multiple regression which can be applied using any general statistical package. We use the example of mapping in an F2 population and show that these regression methods produce very similar results to those obtained using maximum likelihood. The relative simplicity of the regression methods means that models with more than a single QTL can be explored and we give examples of two linked loci and of two interacting loci. Other models, for example with more than two QTL, with environmental fixed effects, with between family variance or for threshold traits, could be fitted in a similar way. The ease, speed of application and generality of regression methods for flanking marker analyses, and the good estimates they obtain, suggest that they should provide the method of choice for the analysis of QTL mapping data from inbred line crosses.
Comparison of surface normal estimation methods for range sensing applications

As mobile robotics is gradually moving towards a level of semantic environment understanding, robust 3D object recognition plays an increasingly important role. One of the most crucial prerequisites for object recognition is a set of fast algorithms for geometry segmentation and extraction, which in turn rely on surface normal vectors as a fundamental feature. Although there exists a plethora of different approaches for estimating normal vectors from 3D point clouds, it is largely unclear which methods are preferable for online processing on a mobile robot. This paper presents a detailed analysis and comparison of existing methods for surface normal estimation with a special emphasis on the trade-off between quality and speed. The study sheds light on the computational complexity as well as the qualitative differences between methods and provides guidelines on choosing the ‘right’ algorithm for the robotics practitioner. The robustness of the methods with respect to noise and neighborhood size is analyzed. All algorithms are benchmarked with simulated as well as real 3D laser data obtained from a mobile robot.
Antibacterial drug discovery in the resistance era

The looming antibiotic-resistance crisis has penetrated the consciousness of clinicians, researchers, policymakers, politicians and the public at large. The evolution and widespread distribution of antibiotic-resistance elements in bacterial pathogens has made diseases that were once easily treatable deadly again. Unfortunately, accompanying the rise in global resistance is a failure in antibacterial drug discovery. Lessons from the history of antibiotic discovery and fresh understanding of antibiotic action and the cell biology of microorganisms have the potential to deliver twenty-first century medicines that are able to control infection in the resistance era.
A new method for decontamination of de novo transcriptomes using a hierarchical clustering algorithm

The identification of contaminating sequences in a de novo assembly is challenging because of the absence of information on the target species. For sample types where the target organism is impossible to isolate from its matrix, such as endoparasites, endosymbionts and soil-harvested samples, contamination is unavoidable. A few post-assembly decontamination methods are currently available but are based only on alignments to databases, which can lead to poor decontamination.



We present a new decontamination method based on a hierarchical clustering algorithm called MCSC. This method uses frequent patterns found in sequences to create clusters. These clusters are then linked to the target species or tagged as contaminants using classic alignment tools. The main advantage of this decontamination method is that it allows sequences to be tagged correctly even if they are unknown or misaligned to a database.


Availability and Implementation
Scripts and documentation about the MCSC decontamination method are available at https://github.com/Lafond-LapalmeJ/MCSC_Decontamination .



: benjamin.mimee@agr.gc.ca.



Supplementary data are available at Bioinformatics online.
An Efficient Tree Classifier Ensemble-Based Approach for Pedestrian Detection

Classification-based pedestrian detection systems (PDSs) are currently a hot research topic in the field of intelligent transportation. A PDS detects pedestrians in real time on moving vehicles. A practical PDS demands not only high detection accuracy but also high detection speed. However, most of the existing classification-based approaches mainly seek for high detection accuracy, while the detection speed is not purposely optimized for practical application. At the same time, the performance, particularly the speed, is primarily tuned based on experiments without theoretical foundations, leading to a long training procedure. This paper starts with measuring and optimizing detection speed, and then a practical classification-based pedestrian detection solution with high detection speed and training speed is described. First, an extended classification/detection speed metric, named feature-per-object (fpo), is proposed to measure the detection speed independently from execution. Then, an fpo minimization model with accuracy constraints is formulated based on a tree classifier ensemble, where the minimum fpo can guarantee the highest detection speed. Finally, the minimization problem is solved efficiently by using nonlinear fitting based on radial basis function neural networks. In addition, the optimal solution is directly used to instruct classifier training; thus, the training speed could be accelerated greatly. Therefore, a rapid and accurate classification-based detection technique is proposed for the PDS. Experimental results on urban traffic videos show that the proposed method has a high detection speed with an acceptable detection rate and a false-alarm rate for onboard detection; moreover, the training procedure is also very fast.
Ischemic oculomotor nerve palsy and skin necrosis caused by vascular embolization after hyaluronic acid filler injection: a case report.

Hyaluronic acid filler injection is widely used for soft tissue augmentation. However, there can be disastrous complications by direct vascular embolization. We present a case of ischemic oculomotor nerve palsy and skin necrosis after hyaluronic acid filler injection on glabellar.blepharoptosis, exotropia and diplopia developed suddenly after the injection, and skin necrosis gradually occurred. Symptoms and signs of oculomotor nerve palsy continuously improved with steroid therapy. Skin defects healed with minimal scars through intensive wound care.Percutaneous filler injection of periorbital areas should be performed carefully by experienced surgeons, and the possibility of embolization should be considered promptly if symptoms develop.
Evaluating State-of-the-Art Object Detector on Challenging Traffic Light Data

Traffic light detection (TLD) is a vital part of both intelligent vehicles and driving assistance systems (DAS). General for most TLDs is that they are evaluated on small and private datasets making it hard to determine the exact performance of a given method. In this paper we apply the state-of-the-art, real-time object detection system You Only Look Once, (YOLO) on the public LISA Traffic Light dataset available through the VIVA-challenge, which contain a high number of annotated traffic lights, captured in varying light and weather conditions.,,,,,,The YOLO object detector achieves an AUC of impressively 90.49% for daysequence1, which is an improvement of 50.32% compared to the latest ACF entry in the VIVAchallenge. Using the exact same training configuration as the ACF detector, the YOLO detector reaches an AUC of 58.3%, which is in an increase of 18.13%.
Algorithm 799: revolve: an implementation of checkpointing for the reverse or adjoint mode of computational differentiation

In its basic form, the reverse mode of computational differentiation yields the gradient of a scalar-valued function at a cost that is a small multiple of the computational work needed to evaluate the function itself. However, the corresponding memory requirement is proportional to the run-time of the evaluation program. Therefore, the practical applicability of the reverse mode in its original formulation is limited despite the availability of ever larger memory systems. This observation leads to the development of checkpointing schedules to reduce the storage requirements. This article presents the function revolve, which generates checkpointing schedules that are provably optimal with regard to a primary and a secondary criterion. This routine is intended to be used as an explicit “controller” for running a time-dependent applications program.
DNA Sequence Compression Using the Burrows-Wheeler Transform

We investigate off-line dictionary oriented approaches to DNA sequence compression, based on the Burrows-Wheeler Transform (BWT). The preponderance of short repeating patterns is an important phenomenon in biological sequences. Here, we propose off-line methods to compress DNA sequences that exploit the different repetition structures inherent in such sequences. Repetition analysis is performed based on the relationship between the BWT and important pattern matching data structures, such as the suffix tree and suffix array. We discuss how the proposed approach can be incorporated in the BWT compression pipeline.
Designing for collaborative creative problem solving

Collaborative creativity is traditionally supported by formal techniques, such as brainstorming. These techniques improve the idea-generation process by creating group synergies, but also suffer from a number of negative effects. Current electronic tools to support collaborative creativity overcome some of these problems, but introduce new ones, by either losing the benefits of face-to-face communication or the immediacy of simultaneous contribution. Using an interactive environment as a test bed, we are investigating how collaborative creativity can be supported electronically while maintaining face-to-face communication. What are the design-factors influencing such a system? We have designed a brainstorming application that uses an interactive table and a large wall display, and compared the results of using it to traditional paper-based brainstorming in a user study with 30 participants. From the considerations that went into the design and the observations during the study we derive a number of design guidelines for collaborative systems in interactive environments.
Species and variety detection of fruits and vegetables from images

Efficient detection of ‘species and variety’ of fruits and vegetables from the images is one of the major challenges for the computers. In this paper, we introduce a framework for the fruit and vegetable classification problem which takes the images of fruits and vegetables as input and returns it is species and variety. The input image contains fruit or vegetable of single variety in arbitrary position and in any number. This paper also introduces a texture feature based on sum and difference of intensity values of the neighbouring pixels of the colour images. The experimental results show that the proposed texture feature supports accurate fruit and vegetable recognition and performs better than other state-of-the-art colour and texture features. The classification accuracy for the proposed ISADH texture feature is achieved up to 99%.
A Measure of Polarization on Social Media Networks Based on Community Boundaries

Polarization in social media networks is a fact in several scenarios such as political debates and other contexts such as same-sex marriage, abortion and gun control. Understanding and quantifying polarization is a longterm challenge to researchers from several areas, also being a key information for tasks such as opinion analysis. In this paper, we perform a systematic comparison between social networks that arise from both polarized and non-polarized contexts. This comparison shows that the traditional polarization metric – modularity – is not a direct measure of antagonism between groups, since non-polarized networks may be also divided into fairly modular communities. To bridge this conceptual gap, we propose a novel polarization metric based on the analysis of the boundary of a pair of (potentially polarized) communities, which better captures the notions of antagonism and polarization. We then characterize polarized and non-polarized social networks according to the concentration of high-degree nodes in the boundary of communities, and found that polarized networks tend to exhibit low concentration of popular nodes along the boundary. To demonstrate the usefulness of our polarization measures, we analyze opinions expressed on Twitter on the gun control issue in the United States, and conclude that our novel metrics help making sense of opinions expressed on online media.
Combining Reward Shaping and Curriculum Learning for Training Agents with High Dimensional Continuous Action Spaces

The needs for training agent with high dimensional continuous action spaces will increase as the robot hardware such as robotic arms and humanoid robots are becoming more and more sophisticated. However, it is difficult and time-consuming task. To tackle the problem, we combine reward shaping and curriculum learning. More specifically, the rewards are provided to the agent for every step it takes and the difficulty of the problem gradually increases depending on the agent learning. Both reward function and curriculum are designed to make the agent achieve its objective. The simulation results demonstrate that the proposed scheme outperforms the comparisons.
A “K Hypotheses + Other” Belief Updating Model

Spoken dialog systems typically rely on recognition confidence scores to guard against potential misundersta ndings. While confidence scores can provide an initial assessment for the reliability of the information obtained fro m the user, ideally systems should leverage information that is available in subsequent user responses to update and improve the accuracy of their beliefs. We present a machine-learning based solution for this problem. We use a compresse d representation of beliefs that tracks up to k hypotheses for each concept at any given time. We train a generalized l inear model to perform the updates. Experimental results show that the proposed approach significantly outperform s heuristic rules used for this task in current systems. Fu rthermore, a user study with a mixed-initiative spoken dialog sy stem shows that the approach leads to significant gains in task success and in the efficiency of the interaction, a cross a wide range of recognition error-rates.
A CNS-permeable Hsp90 inhibitor rescues synaptic dysfunction and memory loss in APP-overexpressing Alzheimer’s mouse model via an HSF1-mediated mechanism

Induction of neuroprotective heat-shock proteins via pharmacological Hsp90 inhibitors is currently being investigated as a potential treatment for neurodegenerative diseases. Two major hurdles for therapeutic use of Hsp90 inhibitors are systemic toxicity and limited central nervous system permeability. We demonstrate here that chronic treatment with a proprietary Hsp90 inhibitor compound (OS47720) not only elicits a heat-shock-like response but also offers synaptic protection in symptomatic Tg2576 mice, a model of Alzheimer’s disease, without noticeable systemic toxicity. Despite a short half-life of OS47720 in mouse brain, a single intraperitoneal injection induces rapid and long-lasting (>3 days) nuclear activation of the heat-shock factor, HSF1. Mechanistic study indicates that the remedial effects of OS47720 depend upon HSF1 activation and the subsequent HSF1-mediated transcriptional events on synaptic genes. Taken together, this work reveals a novel role of HSF1 in synaptic function and memory, which likely occurs through modulation of the synaptic transcriptome.