nils-herrmann/clean_scirepeval huggingface dataset:
Agricultural Productivity Enhancement System & Livestock Management using Internet of Things

The more man has advanced towards technology, more and more problems affecting Mother Nature have aroused. ‘Not now, then when? Not earth, then where? Not we, then who’ are all the rhetoric questions that in themselves stress the need for us to become more aware about Global Warming. Global Warming has vastly affected different human sectors but the worst of the effects are seen in the Agricultural sector where the unpredictable climatic changes are causing to be of great trouble due to which many losses have been incurred. There were days back when our ancestors could predict the climate through ancient methods and to our surprise; most of their predictions did turn out to be true. Now that there are sudden climate changes, the previously used prediction methods have proven futile. There is an acute need to develop more sophisticated prediction methods using technology where more accurate decisions can be made. Using Embedded Systems and IOT, we aim at bringing sophistication to the crop prediction techniques to enhance the agricultural productivity even while the climatic trends keep on changing continuously. Adding on to the utility of this project is the Livestock Management module which further adds value to the project.
Finding and Fixing Vehicle NVH Problems with Transfer Path Analysis

This article discusses the use of experimental transfer path analysis (TPA) to find optimized solutions to NVH problems remaining late in vehicle development stages. After a short review of established TPA methods, four practical case histories are discussed to illustrate how TPA, FE models and practical experiments can supplement each other efficiently for finding optimum and attribute-balanced solutions to complex NVH issues late in the development process. Experimental transfer path analysis (TPA) is a fairly well established technique, 1,2 for estimating and ranking individual low-frequency noise or vibration contributions via the different structural transmission paths from point-coupled power-train or wheel suspensions to the vehicle body. TPA is also used to analyze the transmission paths into vibration-isolated truck or tractor cabs etc. TPA can also be used at higher frequencies (above 150-200 Hz) in road vehicles, although it may be reasonable to introduce a somewhat different formulation based on the response statistics of multimodal vibro-acoustic systems with strong modal overlap. 3 When NVH problems still remain close to start of production (SOP), experimental TPA is often a favored technique to investigate further possibilities to fine-tune the rubber components of the engine or wheel suspension with respect to NVH. The aim is to further improve NVH with minimal negative impact on other vehicle attributes, such as ride comfort, handling , drivability, durability, etc. The only design parameters that can directly be changed in a " what if? " study based purely on experimental TPA, are the dynamic properties of rubber elements connecting the source and the receiving structure. Also, any reduction of transfer path contributions to noise or vibration in that case will be a result of reducing some of the dynamic stiffness' for the connecting elements. To take any other design changes into account, additional measurements are normally necessary. Each degree of freedom (DOF) acting at interface points between a vibration source system and a receiving, passive vibro-acoustic system is a transfer path in TPA. TPA can also be performed analytically, using FE models or specialized system analysis software. 4 The experimental TPA method involves: 1) An indirect measurement procedure for estimating operating force components acting at the coupled DOFs. 2) The direct or reciprocal measurement of all transfer frequency response functions (FRFs) between response in points of interest (e.g. at the drivers ear) and points where these forces act. The FRFs are measured with the receiving subsystem disconnected at all …
An analysis of the positioning accuracy of iBeacon technology in indoor environments

Positioning and location tracking are two major needs of the modern era; in which logistics, industry automation, smart buildings/cities and the Internet of Things concepts are trending topics and are constantly evolving. Introduction of the Global Positioning System (GPS) solved the issue well for outdoor scenarios, however, it is not a reliable solution for indoors, where satellite communication (line-of-sight in general) cannot be maintained. Additionally, there are usually many obstructions (i.e. walls), which block or distort the signals or many signal sources causing interference and noise. Thus, indoor positioning requires different and smarter approaches. iBeacon, is new device-to-device communication technology that relies on Bluetooth Low Energy. In this work, we tested accuracy of a known method used to estimate the distance between two devices communicating through the iBeacon technology. Moreover; effects of the output power, placement of the devices, existence of walls and the possible radio interference, on the accuracy of that distance estimation are investigated.
Domain Modelling in Computational Persuasion for Behaviour Change in Healthcare

The aim of behaviour change is to help people to change aspects of their behaviour for the better (e.g., to decrease calorie intake, to drink in moderation, to take more exercise, to complete a course of antibiotics once started, etc.). In current persuasion technology for behaviour change, the emphasis is on helping people to explore their issues (e.g., through questionnaires or game playing) or to remember to follow a behaviour change plan (e.g., diaries and email reminders). However, recent developments in computational persuasion are leading to an argument-centric approach to persuasion that can potentially be harnessed in behaviour change applications. In this paper, we review developments in computational persuasion, and then focus on domain modelling as a key component. We present a multi-dimensional approach to domain modelling. At the core of this proposal is an ontology which provides a representation of key factors, in particular kinds of belief, which we have identified in the behaviour change literature as being important in diverse behaviour change initiatives. Our proposal for domain modelling is intended to facilitate the acquisition and representation of the arguments that can be used in persuasion dialogues, together with meta-level information about them which can be used by the persuader to make strategic choices of argument to present.
The weight consistency matrix framework for general non-binary LDPC code optimization: Applications in flash memories

Transmission channels underlying modern memory systems, e.g., Flash memories, possess a significant amount of asymmetry. While existing LDPC codes optimized for symmetric, AWGN-like channels are being actively considered for Flash applications, we demonstrate that, due to channel asymmetry, such approaches are fairly inadequate. We propose a new, general, combinatorial framework for the analysis and design of non-binary LDPC (NB-LDPC) codes for asymmetric channels. We introduce a refined definition of absorbing sets, which we call general absorbing sets (GASs), and an important subclass of GASs, which we refer to as general absorbing sets of type two (GASTs). Additionally, we study the combinatorial properties of GASTs. We then present the weight consistency matrix (WCM), which succinctly captures key properties in a GAST. Based on these new concepts, we then develop a general code optimization framework, and demonstrate its effectiveness on the realistic highly-asymmetric normal-Laplace mixture (NLM) Flash channel. Our optimized codes enjoy over one order (resp., half of an order) of magnitude performance gain in the uncorrectable BER (UBER) relative to the unoptimized codes (resp. the codes optimized for symmetric channels).
Savoring the Past: Positive Memories Evoke Value Representations in the Striatum

Reminders of happy memories can bring back pleasant feelings tied to the original experience, suggesting an intrinsic value in reminiscing about the positive past. However, the neural circuitry underlying the rewarding aspects of autobiographical memory is poorly understood. Using fMRI, we observed enhanced activity during the recall of positive relative to neutral autobiographical memories in corticostriatal circuits that also responded to monetary reward. Enhanced activity in the striatum and medial prefrontal cortex was associated with increases in positive emotion during recall, and striatal engagement further correlated with individual measures of resiliency. Striatal response to the recall of positive memories was greater in individuals whose mood improved after the task. Notably, participants were willing to sacrifice a more tangible reward, money, in order to reminisce about positive past experiences. Our findings suggest that recalling positive autobiographical memories is intrinsically valuable, which may be adaptive for regulating positive emotion and promoting better well-being.
Quasi-Newton Optimization in Deep Q-Learning for Playing ATARI Games

Reinforcement Learning (RL) algorithms allow artificial agents to improve their selection of actions so as to increase rewarding experiences in their environments. The learning can become intractably slow as the state space of the environment grows. This has motivated methods that learn internal representations of the agents state by a function approximator. Impressive results have been produced by using deep artificial neural networks to learn the state representations. However, deep reinforcement learning algorithms require solving a non-convex and nonlinear unconstrained optimization problem. Methods for solving the optimization problems in deep RL are restricted to the class of first-order algorithms, like stochastic gradient descent (SGD). The major drawback of the SGD methods is that they have the undesirable effect of not escaping saddle points. Furthermore, these methods require exhaustive trial and error to fine-tune many learning parameters. Using second derivative information can result in improved convergence properties, but computing the Hessian matrix for large-scale problems is not practical. QuasiNewton methods, like SGD, require only first-order gradient information, but they can result in superlinear convergence, which makes them attractive alternatives. The limitedmemory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) approach is one of the most popular quasi-Newton methods that construct positive definite Hessian approximations. In this paper, we introduce an efficient optimization method, based on the limited memory BFGS quasi-Newton method using line search strategy – as an alternative to SGD methods. Our method bridges the disparity between first order methods and second order methods by continuing to use gradient information to calculate a low-rank Hessian approximations. We provide empirical results on variety of the classic ATARI 2600 games. Our results show a robust convergence with preferred generalization characteristics, as well as fast training time and no need for the experience replaying mechanism.
Edge Drawing: A Heuristic Approach to Robust Real-Time Edge Detection

We propose a new edge detection algorithm that works by computing a set of anchor edge points in an image and then linking these anchor points by drawing edges between them. The resulting edge map consists of perfect contiguous, one pixel wide edges. The performance tests show that our algorithm is up to 16% faster than the fastest known edge detection algorithm, i.e., OpenCV implementation of the Canny edge detector. We believe that our edge detector is a novel step in edge detection and would be very suitable for the next generation real-time image processing and computer vision applications.
Biped gait controller for large speed variations, combining reflexes and a central pattern generator in a neuromuscular model

Controllers based on neuromuscular models hold the promise of energy-efficient and human-like walkers. However, most of them rely on optimizations or cumbersome hand-tuning to find controller parameters which, in turn, are usually working for a specific gait or forward speed only. Consequently, designing neuromuscular controllers for a large variety of gaits is usually challenging and highly sensitive. In this contribution, we propose a neuromuscular controller combining reflexes and a central pattern generator able to generate gaits across a large range of speeds, within a single optimization. Applying this controller to the model of COMAN, a 95 cm tall humanoid robot, we were able to get energy-efficient gaits ranging from 0.4 m/s to 0.9 m/s. This covers normal human walking speeds once scaled to the robot height. In the proposed controller, the robot speed could be continuously commanded within this range by changing three high-level parameters as linear functions of the target speed. This allowed large speed transitions with no additional tuning. By combining reflexes and a central pattern generator, this approach can also predict when the next strike will occur and modulate the step length to step over a hole.
Specific Treatment to Neural Networks for Repeated Measures Analysis

Analysis of repeated measures data for the purpose of prediction is not an easy task particularly when the problem under consideration is highly nonlinear, number of subjects is large and the sample available to learn the model is small. The efficacy of the ANN for subject level treatment has been studied here empirically. Data were generated through a random coefficient model and a few nonlinear mixed effect models. For ANN feedforward backprop has been tried. Simulations have been conducted with varying number of covariates and parameters (both common and subject dependent), number of subjects and different sizes of repeated measures. ANN has demonstrated considerable promise.
On Plant Responses to D-Amino Acids Features of Growth , Root Behaviour and Selection for Plant Transformation

Amino acids have been regarded as potential plant nitrogen sources for more than a century. Some amino acids have been shown to support growth while others have growth-retarding effects. The D-isomers with notably adverse effects on plants’ growth and development include D-serine and D-alanine. Recently, D-serine has been recognised as an endogenous ligand of receptor channels mediating calcium fluxes in plants, but otherwise little is known about endogenous roles of D-amino acids in plants. In the studies underlying this thesis, the negative responses to D-serine and D-alanine were converted to positive effects in Arabidopsis thaliana (Arabidopsis) plants by introducing either of two D-amino acid-metabolising enzymes. Transgenic Arabidopsis lines expressing either the D-serine dehydratase (dsdA) gene from Escherichia coli or the D-amino acid oxidase (DAO1) gene from Rhodotorula gracilis grew with otherwise toxic D-amino acids as the sole nitrogen source. I also expressed a transporter specific for D-amino acids, which further increased the transgenic plants’ growth with D-serine as sole nitrogen source. Hence, both assimilation and uptake restrictions can limit plant growth on D-amino acids. The growth of transgenic lines on D-serine or D-alanine provides an unambiguous and highly visible phenotype, which is essential for a selectable marker. Thus, expressing of either the dsdA or DAO1 genes generated transformants that are easy to screen. Furthermore, the DAO1 gene can be readily used for either positive or negative selection, depending on the substrate, thus it provides a unique conditional, substrate-dependent positive/negative selectable marker for plant transformation. In summary, the presented work demonstrates that introducing the ability to catalyse a single metabolic step can allow plants to exploit an otherwise inaccessible or toxic form of organic nitrogen, and provides a versatile marker based on nitrogen nutrition for selecting transgenic plants. A possible role for D-serine in plants’ touch response is also reviewed in the thesis.
High Accuracy Optical Character Recognition Using Neural Networks with Centroid Dithering

-Optical character recognition (OCR) refers to a process whereby printed documents are transformed into ASCII files for the purpose of compact storage, editing, fast retrieval, and other file manipulations through the use of a computer. The recognition stage of an OCR process is made difficult by added noise, image distortion, and the various character typefaces, sizes, and fonts that a document may have. In this study a neural network approach is introduced to perform high accuracy recognition on multi-size and multi-font characters; a novel centroid-dithering training process with a low noise-sensitivity normalization procedure is used to achieve high accuracy results. The study consists of two parts. The first part focuses on single size and single font characters, and a two-layered neural network is trained to recognize the full set of 94 ASCII character images in 12-pt Courier font. The second part trades accuracy for additional font and size capability, and a larger two-layered neural network is trained to recognize the full set of 94 ASCII character images for all point sizes from S to 32 and for 12 commonly used fonts. The performance of these two networks is evaluated based on a database of more than one million character images from the testing data set.
Distributed Energy Resources (DER) object modeling with IEC 61850–7–420

The electrical networks are receiving more deployments of Distributed Energy Resources (DERs). In order to control the impact of DERs on existing networks, it is proposed to divide them into more manageable smaller sets which are called ‘microgrids’. Unlike conventional utility grids, microgrids include generators, storage devices and loads at all levels of the system. Power generation, distribution and consumption levels are not discrete and power flow may occur at any direction. At any point in time, the microgrid may become isolated from the utility grid and continue its operation under islanding conditions. Furthermore, some microgrids may have changing structures with alternative paths and the coupling point for a device or a part of the microgrid may change due to the altering conditions. In order to sustain a safe operation in such a versatile and dynamic structure which has numerous parameters and variables, a new management strategy that has communication lines is required. The need of an extensive communication between the microgrid components is widely accepted in the Power Engineering field. In an effort to standardize the communication methods and modeling IEC 61850 is issued by International Electrotechnical Commission. Currently, there are studies underway to extend this standard with IEC 61850–7–420 so that DERs are also modeled in a standard way. This paper focuses on the communication challenges in a microgrid system, a review of IEC 61850 and the implementation of IEC 61850–7–420 for DER modeling.
Inverted files for text search engines

The technology underlying text search engines has advanced dramatically in the past decade. The development of a family of new index representations has led to a wide range of innovations in index storage, index construction, and query evaluation. While some of these developments have been consolidated in textbooks, many specific techniques are not widely known or the textbook descriptions are out of date. In this tutorial, we introduce the key techniques in the area, describing both a core implementation and how the core can be enhanced through a range of extensions. We conclude with a comprehensive bibliography of text indexing literature.
Digital Watermarking and Steganography

Sharing, disseminating, and presenting data in digital format is not just a fad, but it is becoming part of our life. Without careful planning, digitized resources could easily be misused, especially those that are shared across the Internet. Examples of such misuse include use without the owner’s permission, and modification of a digitized resource to fake ownership. One way to prevent such behaviors is to employ some form of copyright protection technique, such as digital watermarks. Digital watermarks refer to the data embedded into a digital source (e.g., images, text, audio, or video recording). They are similar to watermarks in printed materials as a message inserted into the host media typically becomes an integral part of the media. Apart from traditional watermarks in printed forms, digital watermarks may also be invisible, may be in the forms other than graphics, and may be digitally removed.
Image Stylization using Depth Information for Hatching and Engraving effects by

In this thesis inspired by artists such as Gustave Doré and copperplate engravers, we present a new approach for producing a sketch-type hatching effect as well as engraving art seen in the US dollar bills using depth maps as input. The approach uses the acquired depth to generate pen-and-ink type of strokes that exaggerate surface characteristics of faces and objects. The background on this area is presented with an emphasis on line drawing, pen-and-ink illustrations, engraving art and other artistic styles. The patterns generated are composed of parallel lines that change their directionality smoothly while avoiding intersections between them. The style communicates tone and surface characteristics using curve deformation, thickness, density, and spacing while at the same time adding crosshatching effects. This is achieved with our algorithmic approach which uses the data to perform operations that deforms patterns. The components of our methodology and algorithms are detailed, as well as the equations that govern these effects. This work also presents an array of different results by varying parameters or by rendering variations.
Sex-work harm reduction

Sex work is an extremely dangerous profession. The use of harm-reduction principles can help to safeguard sex workers' lives in the same way that drug users have benefited from drug-use harm reduction. Sex workers are exposed to serious harms: drug use, disease, violence, discrimination, debt, criminalisation, and exploitation (child prostitution, trafficking for sex work, and exploitation of migrants). Successful and promising harm-reduction strategies are available: education, empowerment, prevention, care, occupational health and safety, decriminalisation of sex workers, and human-rights-based approaches. Successful interventions include peer education, training in condom-negotiating skills, safety tips for street-based sex workers, male and female condoms, the prevention-care synergy, occupational health and safety guidelines for brothels, self-help organisations, and community-based child protection networks. Straightforward and achievable steps are available to improve the day-to-day lives of sex workers while they continue to work. Conceptualising and debating sex-work harm reduction as a new paradigm can hasten this process.