nils-herrmann/clean_scirepeval huggingface dataset:
Tomato Leaf Disease Detection Using Convolutional Neural Networks

The tomato crop is an important staple in the Indian market with high commercial value and is produced in large quantities. Diseases are detrimental to the plant's health which in turn affects its growth. To ensure minimal losses to the cultivated crop, it is crucial to supervise its growth. There are numerous types of tomato diseases that target the crop's leaf at an alarming rate. This paper adopts a slight variation of the convolutional neural network model called LeNet to detect and identify diseases in tomato leaves. The main aim of the proposed work is to find a solution to the problem of tomato leaf disease detection using the simplest approach while making use of minimal computing resources to achieve results comparable to state of the art techniques. Neural network models employ automatic feature extraction to aid in the classification of the input image into respective disease classes. This proposed system has achieved an average accuracy of 94–95 % indicating the feasibility of the neural network approach even under unfavourable conditions.
The buying decision process and types of buying decision behaviour

The decision of buying an item does not instantly take place. Behind the visible act of purchasing an item, stands a buying decision process that smart companies should investigate. Motivation, perception, learning, memory, personality, and attitude play an important role in the unfolding of the decision process that presupposes the consumer’s covering of five stages: problem recognition, information search, evaluation of alternatives, purchase decision, and post purchase behaviour. Marketers’ task is to study consumer behaviour in order to achieve a thorough understanding of all five stages unfolding in this process, not only of the purchase decision as such. Buying behaviour differs greatly depending on the purchased item, therefore, the types of decision behaviour need to be known and studied. They are: complex buying behaviour, dissonance-reducing buying behaviour, habitual buying behaviour, variety-seeking buying behaviour.
IMPACT OF GREEN SUPPLY CHAIN MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE : A REVIEW

Environment management is the new development in the field of management which has emerged due to high production and consumption rate, globally. In addition, it has become need of present competitive business environment that, organizations should take some environmental initiatives and reduce environmental impact of their operations. This research paper attempts to review previous research studies in the field of Green Supply Chain Management (GSCM) Practices and its impact of Organizational Performance. This review shows that there are basically three GSCM practices, which studied, large number of times in the last, more than a decade, these are; Green Design; Green Manufacturing; and Green Reverse Logistics. Similarly, three performance measures are found common in majority of studies; Environmental Performance; Economic Performance; and Operational Performance, which are combindley called as Organizational Performance.
EXPERIMENTS OF ZERNIKE MOMENTS FOR LEAF IDENTIFICATION

So far, plant identification has challenges for sev eral researchers. Various methods and features have been proposed. However, there are still many approaches could be investigated to develop robust plant identification systems. This paper reports several xperiments in using Zernike moments to build folia ge plant identification systems. In this case, Zernike moments were combined with other features: geometr ic features, color moments and gray-level co-occurrenc e matrix (GLCM). To implement the identifications systems, two approaches has been investigated. Firs t approach used a distance measure and the second u sed Probabilistic Neural Networks (PNN). The results sh ow that Zernike Moments have a prospect as features in leaf identification systems when they are combin ed with other features.
Beyond Web/Native/Hybrid: A New Taxonomy for Mobile App Development

Currently, mobile operating systems are dominated by the duopoly of iOS and Android. App projects that intend to reach a high number of customers need to target these two platforms foremost. However, iOS and Android do not have an officially supported common development framework. Instead, different development approaches are available for multi-platform development.
 The standard taxonomy for different development approaches of mobile applications is: Web Apps, Native Apps, Hybrid Apps. While this made perfect sense for iPhone development, it is not accurate for Android or cross-platform development, for example.
 In this paper, a new taxonomy is proposed. Based on the fundamental difference in the tools and programming languages used for the task, six different categories are proposed for everyday use: Endemic Apps, Web Apps, Hybrid Web Apps, Hybrid Bridged Apps, System Language Apps, and Foreign Language Apps. In addition, when a more precise distinction is necessary, a total of three main categories and seven subcategories are defined.
 The paper also contains a short overview of the advantages and disadvantages of the approaches mentioned.
A Novel Approach for Effective Recognition of the Code-Switched Data on Monolingual Language Model

Code-switching refers to the phenomena of mixing of words or phrases from foreign languages while communicating in a native language by the multilingual speakers. Codeswitching is a global phenomenon and is widely accepted in multilingual communities. However, for training the language model (LM) for such tasks, a very limited code-switched textual resources are available as yet. In this work, we present an approach to reduce the perplexity (PPL) of Hindi-English code-switched data when tested over the LM trained on purely native Hindi data. For this purpose, we propose a novel textual feature which allows the LM to predict the code-switching instances. The proposed feature is referred to as code-switching factor (CS-factor). Also, we developed a tagger that facilitates the automatic tagging of the code-switching instances. This tagger is trained on a development data and assigns an equivalent class of foreign (English) words to each of the potential native (Hindi) words. For this study, the textual resource has been created by crawling the blogs from a couple of websites educating about the usage of the Internet. In the context of recognition of the code-switching data, the proposed technique is found to yield a substantial improvement in terms of PPL.
Robustness of Federated Averaging for Non-IID Data

Machine learning requires access to all the data used for training. Recently, Google Research proposed Federated Learning as an alternative, where the training data is distributed over a federation of clients that each only access their own training data; the partially trained model is updated in a distributed fashion to maintain a situation where the data from all participating clients remains unknown. In this research we construct different distributions of the DMOZ dataset over the clients in the network and compare the resulting performance of Federated Averaging when learning a classifier. We find that the difference in spread of topics for each client has a strong correlation with the performance of the Federated Averaging algorithm.
Data mining techniques for medical data: A review

Data mining is an important area of research and is pragmatically used in different domains like finance, clinical research, education, healthcare etc. Further, the scope of data mining have thoroughly been reviewed and surveyed by many researchers pertaining to the domain of healthcare which is an active interdisciplinary area of research. In fact, the task of knowledge extraction from the medical data is a challenging endeavor and it is a complex task. The main motive of this review paper is to give a review of data mining in the purview of healthcare. Moreover, intertwining and interrelation of previous researches have been presented in a novel manner. Furthermore, merits and demerits of frequently used data mining techniques in the domain of health care and medical data have been compared. The use of different data mining tasks in health care is also discussed. An analytical approach regarding the uniqueness of medical data in health care is also presented.
A 0.3–2.6 TOPS/W precision-scalable processor for real-time large-scale ConvNets

A low-power precision-scalable processor for ConvNets or convolutional neural networks (CNN) is implemented in a 40nm technology. Its 256 parallel processing units achieve a peak 102GOPS running at 204MHz. To minimize energy consumption while maintaining throughput, this works is the first to both exploit the sparsity of convolutions and to implement dynamic precision-scalability enabling supply- and energy scaling. The processor is fully C-programmable, consumes 25-288mW at 204 MHz and scales efficiency from 0.3-2.6 real TOPS/W. This system hereby outperforms the state-of-the-art up to 3.9× in energy efficiency.
Vmgen - a generator of efficient virtual machine interpreters

Machine Interpreters M. Anton Ertl1; ;y, David Gregg2, Andreas Krall1, and Bernd Paysan3 1Institut f ur Computersprachen, Technische Universit at Wien, Argentinierstra e 8, A-1040 Wien, Austria 2Department of Computer Science, Trinity College, Dublin 2, Ireland 3Stockmannstr. 14, D-81477 M unchen, Germany SUMMARY In a virtual machine interpreter, the code for each virtual machine instruction has similarities to code for other instructions. We present an interpreter generator that takes simple virtual machine instruction descriptions as input and generates C code for processing the instructions in several ways: execution, virtual machine code generation, disassembly, tracing, and pro ling. The generator is designed to support e cient interpreters: it supports threaded code, caching the top-of-stack item in a register, combining simple instructions into superinstructions, and other optimizations. We have used the generator to create interpreters for Forth and Java. The resulting interpreters are faster than other interpreters for the same languages and they are typically 2{10 times slower than code produced by native-code compilers. We also present results for the e ects of the individual optimizations supported by the generator.
Generative Code Modeling with Graphs

Generative models for source code are an interesting structured prediction problem, requiring to reason about both hard syntactic and semantic constraints as well as about natural, likely programs. We present a novel model for this problem that uses a graph to represent the intermediate state of the generated output. Our model generates code by interleaving grammar-driven expansion steps with graph augmentation and neural message passing steps. An experimental evaluation shows that our new model can generate semantically meaningful expressions, outperforming a range of strong baselines.
proSQLite: Prolog File Based Databases via an SQLite Interface

We present a succinct yet powerful interface library to the SQLite database system. The single file, server-less approach of SQLite along with the natural integration of relational data within Prolog, render the library a useful addition to the existing database libraries in modern open-source engines. We detail the architecture and predicates of the library and provide example deployment scenarios. A simple bioinformatics example is presented throughout to illustrate proSQLite’s main functions. Finally, this paper discusses the strengths of the system and highlights possible extensions.
An approach based on improved Genetic Algorithm to selecting the threshold automatically in edge detection and its application in computer vision system

一 引言 图像的边缘是图像的重要的特征,它给出了图像场景中物体的轮廓特征信息。当要对图 像中的某一个物体进行识别时,边缘信息是重要的可以利用的信息,例如在很多系统中采用 的模板匹配的识别算法。基于此,我们设计了一套基于 PCI Bus和 Vision Bus的可重构的机 器人视觉系统[3]。此系统能够实时的对图像进行采集,并可以通过系统实时的对图像进行 边缘的提取。 对于图像的边缘提取,采用二阶的边缘检测算子处理后要进行过零点检测,计算量很大 而且用硬件实现资源占用大且速度慢,所以在我们的视觉系统中,卷积器中选择的是一阶的 边缘检测算子。采用一阶的边缘检测算子进行卷积运算之后,仅仅需要对卷积得到的图像进 行阈值处理就可以得到图像的边缘,而阈值处理的操作用硬件实现占用资源少且速度快。由 于本视觉系统要求与应用环境下的精密装配机器人配合使用,系统的实时性要求非常高。因 此,如何对实时采集图像进行快速实时的边缘提取阈值的自动选取,是我们必须要考虑的问 题。 遗传算法是一种仿生物系统的基因进化的迭代搜索算法,其基本思想是由美国Michigan 大学的 J.Holland 教授提出的。由于遗传算法的整体寻优策略以及优化计算时不依赖梯度信 息,所以它具有很强的全局搜索能力,即对于解空间中的全局最优解有着很强的逼近能力。 它适用于问题结构不是十分清楚,总体很大,环境复杂的场合,而对于实时采集的图像进行 边缘检测阈值的选取就是此类问题。本文在对传统的遗传算法进行改进的基础上,提出了一 种对于实时采集图像进行边缘检测的阈值的自动选取方法。
Accurate Light Field Depth Estimation With Superpixel Regularization Over Partially Occluded Regions

Depth estimation is a fundamental problem for light field photography applications. Numerous methods have been proposed in recent years, which either focus on crafting cost terms for more robust matching, or on analyzing the geometry of scene structures embedded in the epipolar-plane images. Significant improvements have been made in terms of overall depth estimation error; however, current state-of-the-art methods still show limitations in handling intricate occluding structures and complex scenes with multiple occlusions. To address these challenging issues, we propose a very effective depth estimation framework which focuses on regularizing the initial label confidence map and edge strength weights. Specifically, we first detect partially occluded boundary regions (POBR) via superpixel-based regularization. Series of shrinkage/reinforcement operations are then applied on the label confidence map and edge strength weights over the POBR. We show that after weight manipulations, even a low-complexity weighted least squares model can produce much better depth estimation than the state-of-the-art methods in terms of average disparity error rate, occlusion boundary precision-recall rate, and the preservation of intricate visual features.
Writing Qualitative IS Literature Reviews - Guidelines for Synthesis, Interpretation, and Guidance of Research

The literature review is an established research genre in many academic disciplines, including the IS discipline. Although many scholars agree that systematic literature reviews should be rigorous, few instructional texts for compiling a solid literature review, at least with regard to the IS discipline, exist. In response to this shortage, in this tutorial, I provide practical guidance for both students and researchers in the IS community who want to methodologically conduct qualitative literature reviews. The tutorial differs from other instructional texts in two regards. First, in contrast to most textbooks, I cover not only searching and synthesizing the literature but also the challenging tasks of framing the literature review, interpreting research findings, and proposing research paths. Second, I draw on other texts that provide guidelines for writing literature reviews in the IS discipline but use many examples of published literature reviews. I use an integrated example of a literature review, which guides the reader through the overall process of compiling a literature review.
Genetic Fuzzy based Artificial Intelligence for Unmanned Combat Aerial Vehicle Control in Simulated Air Combat Missions

Unmanned Combat Aerial Vehicle (UCAV) research has allowed the state of the art of the remote-operation of these technologies to advance significantly in modern times, though mostly focusing on ground strike scenarios. Within the context of air-to-air combat, millisecond long timeframes for critical decisions inhibit remoteoperation of UCAVs. Beyond this, given an average human visual reaction time of 0.15 to 0.30 seconds, and an even longer time to think of optimal plans and coordinate them with friendly forces, there is a huge window of improvement that an Artificial Intelligence (AI) can capitalize upon. While many proponents for an increase in autonomous capabilities herald the ability to design aircraft that can perform extremely high-g maneuvers as well as the benefit of reducing risk to our pilots, this white paper will primarily focus on the increase in capabilities of real-time decision making.
The Oxford Implementation Index: a new tool for incorporating implementation data into systematic reviews and meta-analyses.

This article presents a new tool that helps systematic reviewers to extract and compare implementation data across primary trials. Currently, systematic review guidance does not provide guidelines for the identification and extraction of data related to the implementation of the underlying interventions.



A team of systematic reviewers used a multistaged consensus development approach to develop this tool. First, a systematic literature search on the implementation and synthesis of clinical trial evidence was performed. The team then met in a series of subcommittees to develop an initial draft index. Drafts were presented at several research conferences and circulated to methodological experts in various health-related disciplines for feedback. The team systematically recorded, discussed, and incorporated all feedback into further revisions. A penultimate draft was discussed at the 2010 Cochrane-Campbell Collaboration Colloquium to finalize its content.



The Oxford Implementation Index provides a checklist of implementation data to extract from primary trials. Checklist items are organized into four domains: intervention design, actual delivery by trial practitioners, uptake of the intervention by participants, and contextual factors. Systematic reviewers piloting the index at the Cochrane-Campbell Colloquium reported that the index was helpful for the identification of implementation data.



The Oxford Implementation Index provides a framework to help reviewers assess implementation data across trials. Reviewers can use this tool to identify implementation data, extract relevant information, and compare features of implementation across primary trials in a systematic review. The index is a work-in-progress, and future efforts will focus on refining the index, improving usability, and integrating the index with other guidance on systematic reviewing.
Getting Things Done : The Science behind Stress-Free Productivity

Allen (2001) proposed the “Getting Things Done” (GTD) method for personal productivity enhancement, and reduction of the stress caused by information overload. This paper argues that recent insights in psychology and cognitive science support and extend GTD’s recommendations. We first summarize GTD with the help of a flowchart. We then review the theories of situated, embodied and distributed cognition that purport to explain how the brain processes information and plans actions in the real world. The conclusion is that the brain heavily relies on the environment, to function as an external memory, a trigger for actions, and a source of affordances, disturbances and feedback. We then show how these principles are practically implemented in GTD, with its focus on organizing tasks into “actionable” external memories, and on opportunistic, situation-dependent execution. Finally, we propose an extension of GTD to support collaborative work, inspired by the concept of stigmergy.
The Effects of Online Brand Communities on Brand Equity in the Luxury Fashion Industry Regular Paper

Quoting  the  fashion  genius  Coco  Chanel: “Fashion  is  not  something  that  exists  in dresses  only. Fashion  is  in  the  sky,  in  the  street,  fashion  has  to  do with ideas, the way we live, what is happening.” This is even more  true  today  than  it was  back  then.  Indeed, Web  2.0  technologies have made  luxury  fashion more accessible  to  everyone.  Nowadays,  consumers  can instantly access a wide variety of  fashion goods on  the Internet  and  share  brand  experiences  with  other consumers. In this scenario, Online Brand Communities (OBCs)  allow  fashion  customers  to  communicate with each other without any  restrictions of  time and place. For this reason, OBCs are viewed by fashion marketers as  powerful  instruments  to  influence  customers’ purchasing behaviour. The aim of  the current study  is to analyse the effects of OBC dynamics on brand equity. The  proposed  research  model  is  applied  to  eight spontaneous OBCs of the luxury fashion industry.
Distributed Computing: Fundamentals, Simulations and Advanced Topics

Why should wait for some days to get or receive the distributed computing fundamentals simulations and advanced topics book that you order? Why should you take it if you can get the faster one? You can find the same book that you order right here. This is it the book that you can receive directly after purchasing. This distributed computing fundamentals simulations and advanced topics is well known book in the world, of course many people will try to own it. Why don't you become the first? Still confused with the way?
Applying Support Vector Machines to Imbalanced Datasets

Support Vector Machines (SVM) have been extensively studied and have shown remarkable success in many applications. However the success of SVM is very limited when it is applied to the problem of learning from imbalanced datasets in which negative instances heavily outnumber the positive instances (e.g. in gene profiling and detecting credit card fraud). This paper discusses the factors behind this failure and explains why the common strategy of undersampling the training data may not be the best choice for SVM. We then propose an algorithm for overcoming these problems which is based on a variant of the SMOTE algorithm by Chawla et al, combined with Veropoulos et al’s different error costs algorithm. We compare the performance of our algorithm against these two algorithms, along with undersampling and regular SVM and show that our algorithm outperforms all of them.
Statistical Methods for Linguistic Research: Foundational Ideas - Part I

We present the fundamental ideas underlying statistical hypothesis testing using the frequentist framework. We start with a simple example that builds up the one-sample t-test from the beginning, explaining important concepts such as the sampling distribution of the sample mean, and the iid assumption. Then we examine the meaning of the p-value in detail, and discuss several important misconceptions about what a p-value does and does not tell us. This leads to a discussion of Type I, II error and power, and Type S and M error. An important conclusion from this discussion is that one should aim to carry out appropriately powered studies. Next, we discuss two common issues we have encountered in psycholinguistics and linguistics: running experiments until significance is reached, and the “garden-of-forking-paths” problem discussed by Gelman and others. The best way to use frequentist methods is to run appropriately powered studies, check model assumptions, clearly separate exploratory data analysis from planned comparisons decided upon before the study was run, and always attempt to replicate results.