nils-herrmann/clean_scirepeval huggingface dataset:
Smart Cities : Strategic Sustainable Development for an Urban World

Global urbanisation trends and pressing issues around sustainability pose great challenges for cities. The smart city concept has been developed as a strategy for working with cities as they become systematically more complex through interconnected frameworks, and increasingly rely on the use of Information and Communication Technology to meet the needs of their citizens. This thesis explores the concept of smart cities as a potential urban construct that can address the social and ecological sustainability challenges which society faces. Smart cities are defined as cities where investments in human and social capital, and traditional and modern communication infrastructure fuel sustainable economic growth and a high quality of life, with a wise management of natural resources, through participatory governance. Through structured interviews with smart city practitioners and sustainability experts, the strengths and limitations of the smart city concept are identified and organised through the Framework for Strategic Sustainable Development (FSSD). Then, a Strategic Sustainable Development (SSD) approach is applied as a method to maximise the benefits of the concept, and to mitigate any identified limitations. This thesis recommends a planning guide, informed by an SSD approach, to help smart cities move strategically towards their smart city vision and also move society towards sustainability.
Applying Fuzzy Logic in Assessing the Readiness of the Company for Implementing ERP

ERP (Enterprise Resource Planning) plays an important role to integrate organization's information and functions in all areas and finally results in successful operation in global markets. In underdeveloped countries, most companies have been forced to join global markets. But ERP implementation is costly and time consuming project and unreadiness of the company for this implementation can lead to loss of many valuable resources of the company. Therefore, assessing the readiness of the company for implementing ERP before its implementation is essential. Many factors are essential in determining a company's readiness for ERP implementation. Since most of these factors are qualitative and relations between them are very complicated, determining their exact quantitative values is quiet difficult. Using fuzzy logic can be helpful to simplify the calculations and finally leads to a more precise result to determine qualitative problems like readiness of a company for ERP implementation In this study, we intended to gather critical success factors for ERP implementation and design a Fuzzy System to assess the readiness of a company for ERP. This research is done in a famous auto company in Iran. According to this study the readiness for ERP implementation of this company was low.
Soft Material-Enabled, Flexible Hybrid Electronics for Medicine, Healthcare, and Human-Machine Interfaces

Flexible hybrid electronics (FHE), designed in wearable and implantable configurations, have enormous applications in advanced healthcare, rapid disease diagnostics, and persistent human-machine interfaces. Soft, contoured geometries and time-dynamic deformation of the targeted tissues require high flexibility and stretchability of the integrated bioelectronics. Recent progress in developing and engineering soft materials has provided a unique opportunity to design various types of mechanically compliant and deformable systems. Here, we summarize the required properties of soft materials and their characteristics for configuring sensing and substrate components in wearable and implantable devices and systems. Details of functionality and sensitivity of the recently developed FHE are discussed with the application areas in medicine, healthcare, and machine interactions. This review concludes with a discussion on limitations of current materials, key requirements for next generation materials, and new application areas.
Effective Interaction-aware Trajectory Prediction using Temporal Convolutional Neural Networks

Socially compliant navigation is among the vital precursors to enable deployment of autonomous robots in populated environments. In order for mobile robots to plan trajectories that are safe and socially acceptable, they need to accurately and efficiently predict future trajectories of the surrounding pedestrians. Although current approaches for motion prediction utilize data-driven methods to estimate the behavior of pedestrians, they only model a local neighborhood around each pedestrian. This often results in suboptimal behavior in densely populated environments, where the interactions are more complex in nature. In this work, we propose the IA-TCNN architecture to address the problem of efficient trajectory prediction for multiple interacting pedestrians in a scene. Our network leverages the previous motion information of all pedestrians in order to aggregate trajectories of the most relevant pedestrians within the scene, thus predicting an accurate future trajectory. Extensive experimental evaluations on indoor and outdoor benchmarks demonstrate that our approach achieves state-of-the art performance, while simultaneously achieving fast inference time thereby facilitating online deployment.
Motion deblurring for optical character recognition

This paper investigates the problem of blurring caused by motion during image capture of text documents. Motion blurring prevents proper optical character recognition of the document text contents. One area of such applications is to deblur name card images obtained from handheld cameras. In this paper, a complete motion deblurring procedure for document images has been proposed. The method handles both uniform linear motion blur and uniform acceleration motion blur. Experiments on synthetic and real-life blurred images prove the feasibility and reliability of this algorithm provided that the motion is not too irregular. The restoration procedure consumes only small amount of computation time.
Beam Switching Reflectarray Monolithically Integrated With RF MEMS Switches

A reflectarray antenna monolithically integrated with 90 RF MEMS switches has been designed and fabricated to achieve switching of the main beam. Aperture coupled microstrip patch antenna (ACMPA) elements are used to form a 10 × 10 element reconfigurable reflectarray antenna operating at 26.5 GHz. The change in the progressive phase shift between the elements is obtained by adjusting the length of the open ended transmission lines in the elements with the RF MEMS switches. The reconfigurable reflectarray is monolithically fabricated with the RF MEMS switches in an area of 42.46 cm2 using an in-house surface micromachining and wafer bonding process. The measurement results show that the main beam can be switched between broadside and 40° in the H-plane at 26.5 GHz.
Process to enhance the quality of software requirement specification document

The failure and success of any software mainly depends on a technical document known as Software Requirement Specification (SRS) document, as it contains all requirements and features of the product. In the past, many developments had been done to improve the quality of the SRS, with respect to different attributes of the product, but the product success rate is not satisfactory and the room for improvement is still there. We have developed a different approach to resolve those issues. Our methodology consist of four processes i.e. Parsing Requirement (PR), Requirement Mapping using Matrix (RMM), Addition of Requirements in SRS template and Third Party Inspection. Requirement Engineering Process will provide the required inputs to PR after the implementation of its ontology rules completion of requirements will be achieved. RMM will be generated to minimize ambiguities and incorrectness with concerns of all stakeholders. Outputs of the previous processes will be added to IEEE standard format. A third party inspection will be conducted to check the requirements of the client and SRS. After inspecting SRS using inspection models and assigning Total Quality Score (TQS) third party will submit a detailed report to team of Requirement Engineers (RE). This practice will not only identify the problem but will solve the issue on its way.
Exploiting generalization in the subspaces for faster model-based learning

Due to the lack of enough generalization in the statespace, common methods in Reinforcement Learning (RL) suffer from slow learning speed especially in the early learning trials. This paper introduces a model-based method in discrete statespaces for increasing the learning speed in terms of required experience (but not required computational time) by exploiting generalization in the experiences of the subspaces. A subspace is formed by choosing a subset of features in the original state representation (full-space). Generalization and faster learning in a subspace are due to many-to-one mapping of experiences from the full-space to each state in the subspace. Nevertheless, due to inherent perceptual aliasing in the subspaces, the policy suggested by each subspace does not generally converge to the optimal policy. Our approach, called Model Based Learning with Subspaces (MoBLeS), calculates confidence intervals of the estimated Q-values in the full-space and in the subspaces. These confidence intervals are used in the decision making, such that the agent benefits the most from the possible generalization while avoiding from detriment of the perceptual aliasing in the subspaces. Convergence of MoBLeS to the optimal policy is theoretically investigated. Additionally, we show through several experiments that MoBLeS improves the learning speed in the early trials.
Object localization in weakly labeled data using regularized attention networks

We consider the problem of weakly supervised object localization. For an object of interest (e.g. “car”), an image is weakly labeled when its label only indicates the presence/absence of this object, but not the exact location of the object in the image. Given a collection of weakly labeled images for an object, our goal is to localize the object of interest in each image. We propose a novel architecture called the regularized attention network for this problem. Our work builds upon the attention network proposed in . We extend the standard attention network by incorporating a regularization term that encourages the attention scores of object proposals to mimic the scoring distribution of a strong fully supervised object detector. Despite of the simplicity of our approach, our proposed architecture achieves the state-of-the-art results on several benchmark datasets.
Botnet protocol inference in the presence of encrypted traffic

Network protocol reverse engineering of botnet command and control (C&C) is a challenging task, which requires various manual steps and a significant amount of domain knowledge. Furthermore, most of today's C&C protocols are encrypted, which prevents any analysis on the traffic without first discovering the encryption algorithm and key. To address these challenges, we present an end-to-end system for automatically discovering the encryption algorithm and keys, generating a protocol specification for the C&C traffic, and crafting effective network signatures. In order to infer the encryption algorithm and key, we enhance state-of-the-art techniques to extract this information using lightweight binary analysis. In order to generate protocol specifications we infer field types purely by analyzing network traffic. We evaluate our approach on three prominent malware families: Sality, ZeroAccess and Ramnit. Our results are encouraging: the approach decrypts all three protocols, detects 97% of fields whose semantics are supported, and infers specifications that correctly align with real protocol specifications.
Biosynthesis and application of silver and gold nanoparticles

Nanotechnology has dynamically developed as an important field of modern research with potential effects in electronic and medicine (Glomm 2005, Chan 2006, Boisselier and Astruc 2009). Nanotechnology can be defined as a research for the design, synthesis, and manipulation of structure of particles with dimension smaller than 100nm. A new branch of nanotechnology is nanobiotechnology. Nanobiotechnology combines biological principles with physical and chemical procedures to generate nano-sized particles with specific functions. Nanobiotechnology represents an economic alternative for chemical and physical methods of nanopaticles formation. These methods of synthesis can be divided on intra cellular and extracellular (Ahmad et al. 2005). This integration of nanoparticles with biological molecules has lead to the development of diagnostic devices, contrast agents, and important tools in cancel therapy. Nanobiotechnology describes an application of biological systems for the production of new functional material such as nanoparticles. Biosynthetic methods can employed either microorganism cells or plant extract for nanoparticles production. Biosynthesis of nanoparticles is an exciting recent addition to the large repertoire of nanoparticles synthesis methods and now, nanoparticle have entered a commercial exploration period. Gold and silver nanoparticles are presently under intensive study for applications in optoelectronic devices, ultrasensitive chemical and biological sensors and as catalysts. This chapter is devoted to biosynthesis and application of gold and silver nanoparticles
Stratification, Imaging, and Management of Acute Massive and Submassive Pulmonary Embolism.

While pulmonary embolism (PE) causes approximately 100 000-180 000 deaths per year in the United States, mortality is restricted to patients who have massive or submassive PEs. This state of the art review familiarizes the reader with these categories of PE. The review discusses the following topics: pathophysiology, clinical presentation, rationale for stratification, imaging, massive PE management and outcomes, submassive PE management and outcomes, and future directions. It summarizes the most up-to-date literature on imaging, systemic thrombolysis, surgical embolectomy, and catheter-directed therapy for submassive and massive PE and gives representative examples that reflect modern practice.
A review of deep tissue injury development, detection, and prevention: shear savvy.

Pressure ulcer prevention strategies include the prevention, and early recognition, of deep tissue injury (DTI), which can evolve into a Stage III or Stage IV pressure ulcer. In addition to their role in pressure-induced ischemia, shearing forces are believed to contribute substantially to the risk of DTI. Because the visual manifestation of a DTI may not occur until many hours after tissues were damaged, research to explore methods for early detection is on-going. For example, rhabdomyolysis is a common complication of deep tissue damage; its detection via blood chemistry and urinalysis is explored as a possible diagnostic tool of early DTI in anatomical areas where muscle is present. Substances released from injured muscle cells have a predictable time frame for detection in blood and urine, possibly enabling the clinician to estimate the time of the tissue death. Several small case studies suggest the potential validity and reliability of ultrasoun for visualizing soft tissue damage also deserve further research. While recommendations to reduce mechanical pressure and shearing damage in high-risk patients remain unchanged, their implementation is not always practical, feasible, or congruent with the overall plan of patient care. Early detection of existing tissue damage will help clinicians implement appropriate care plans that also may prevent further damage. Research to evaluate the validity, reliability, sensitivity, and specificity of diagnostic studies to detect pressure-related tissue death is warranted.
No-Reference JPEG Image Quality Assessment Based on Blockiness and Luminance Change

When scoring the quality of JPEG images, the two main considerations for viewers are blocking artifacts and improper luminance changes, such as blur. In this letter, we first propose two measures to estimate the blockiness and the luminance change within individual blocks. Then, a no-reference image quality assessment (NR-IQA) method for JPEG images is proposed. Our method obtains the quality score by considering the blocking artifacts and the luminance changes from all nonoverlapping 8 × 8 blocks in one JPEG image. The proposed method has been tested on five public IQA databases and compared with five state-of-the-art NR-IQA methods for JPEG images. The experimental results show that our method is more consistent with subjective evaluations than the state-of-the-art NR-IQA methods. The MATLAB source code of our method is available at http://image.ustc.edu.cn/IQA.html.
An origami-inspired multicell biobattery stack

We created a 9-cell biobattery stack on paper, which was capable of generating bio-power from microbial metabolism, delivering on-board energy to the next generation of paper-based systems. The developed biobattery stack has competitive advantages over other paper-based batteries in resource-limited and remote regions because water in every environment generally hosts various microorganisms that can operate such a battery. Bacterial cells were contained in patterned hydrophilic paper reservoirs and produced a targeted performance through three-dimensionally stacked batteries. Using origami, a compact, stackable 3-D battery stack was created from 2-D sheets through high degrees of folding along pre-defined creases. The battery stack generated the maximum output voltage of 1.8V and current of 2.5μΛ under 100kΩ.
Railway Network Modelling Using Petri Nets

This paper deals with the use of Petri nets in modelling railway network and designing appropriate control logic for it to avoid collision. Here, the whole railway network is presented as a combination of the elementary models – tracks, stations and points (switch) within the station including sensors and semaphores. We use generalized mutual exclusion constraints and constraints containing the firing vector to ensure safeness of the railway network. In this research work, we have actually introduced constraints at the points within the station. These constraints ensure that when a track is occupied, we control the switch so that another train will not enter into the same track and thus avoid collision.
Impact of increasing fruit and vegetables and flavonoid intake on the human gut microbiota.

Epidemiological studies have shown protective effects of fruits and vegetables (F&V) in lowering the risk of developing cardiovascular diseases (CVD) and cancers. Plant-derived dietary fibre (non-digestible polysaccharides) and/or flavonoids may mediate the observed protective effects particularly through their interaction with the gut microbiota. The aim of this study was to assess the impact of fruit and vegetable (F&V) intake on gut microbiota, with an emphasis on the role of flavonoids, and further to explore relationships between microbiota and factors associated with CVD risk. In the study, a parallel design with 3 study groups, participants in the two intervention groups representing high-flavonoid (HF) and low flavonoid (LF) intakes were asked to increase their daily F&V intake by 2, 4 and 6 portions for a duration of 6 weeks each, while a third (control) group continued with their habitual diet. Faecal samples were collected at baseline and after each dose from 122 subjects. Faecal bacteria enumeration was performed by fluorescence in situ hybridisation (FISH). Correlations of dietary components, flavonoid intake and markers of CVD with bacterial numbers were also performed. A significant dose X treatment interaction was only found for Clostidium leptum-Ruminococcus bromii/flavefaciens with a significant increase after intake of 6 additional portions in the LF group. Correlation analysis of the data from all 122 subjects independent from dietary intervention indicated an inhibitory role of F&V intake, flavonoid content and sugars against the growth of potentially pathogenic clostridia. Additionally, we observed associations between certain bacterial populations and CVD risk factors including plasma TNF-α, plasma lipids and BMI/waist circumference.
Cross-Cultural Adaptation and Validation of the MPAM-R to Brazilian Portuguese and Proposal of a New Method to Calculate Factor Scores

In order to understand the reasons that lead individuals to practice physical activity, researchers developed the Motives for Physical Activity Measure-Revised (MPAM-R) scale. In 2010, a translation of MPAM-R to Portuguese and its validation was performed. However, psychometric measures were not acceptable. In addition, factor scores in some sports psychology scales are calculated by the mean of scores by items of the factor. Nevertheless, it seems appropriate that items with higher factor loadings, extracted by Factor Analysis, have greater weight in the factor score, as items with lower factor loadings have less weight in the factor score. The aims of the present study are to translate, validate the MPAM-R for Portuguese versions, and investigate agreement between two methods used to calculate factor scores. Three hundred volunteers who were involved in physical activity programs for at least 6 months were collected. Confirmatory Factor Analysis of the 30 items indicated that the version did not fit the model. After excluding four items, the final model with 26 items showed acceptable model fit measures by Exploratory Factor Analysis, as well as it conceptually supports the five factors as the original proposal. When two methods are compared to calculate factors scores, our results showed that only "Enjoyment" and "Appearance" factors showed agreement between methods to calculate factor scores. So, the Portuguese version of the MPAM-R can be used in a Brazilian context, and a new proposal for the calculation of the factor score seems to be promising.
Statistical Learning and Data Sciences

This paper introduces an advanced setting of machine learning problem in which an Intelligent Teacher is involved. During training stage, Intelligent Teacher provides Student with information that contains, along with classification of each example, additional privileged information (explanation) of this example. The paper describes two mechanisms that can be used for significantly accelerating the speed of Student’s training: (1) correction of Student’s concepts of similarity between examples, and (2) direct Teacher-Student knowledge transfer.
Isolated bidirectional DC/AC and AC/DC three-phase power conversion using series resonant converter modules and a three-phase unfolder

This paper proposes a modular converter system to achieve high power density, high system bandwidth and scalability for isolated bidirectional DC/AC and AC/DC three-phase power conversion. The approach is based on high frequency isolated series resonant converter (SRC) modules with series output connection and a low frequency three-phase unfolder. The performance objectives are realized through elimination of traditional low frequency passive filters used in PWM inverters and instead require high control bandwidth in the SRC modules to achieve high quality AC waveforms. The system operation and performance are verified with simulation and experimental results for a 1 kW prototype.