nils-herrmann/clean_scirepeval huggingface dataset:
Predicting the status of water pumps using data mining approach

Data mining approach can be used to discover knowledge by analyzing the patterns or correlations among of fields in large databases. Data mining approach was used to find the patterns of the data from Tanzania Ministry of Water. It is used to predict current and future status of water pumps in Tanzania. The data mining method proposed is XGBoost (eXtreme Gradient Boosting). XGBoost implement the concept of Gradient Tree Boosting which designed to be highly fast, accurate, efficient, flexible, and portable. In addition, Recursive Feature Elimination (RFE) is also proposed to select the important features of the data to obtain an accurate model. The best accuracy achieved with using 27 input factors selected by RFE and XGBoost as a learning model. The achieved result show 80.38% in accuracy. The information or knowledge which is discovered from data mining approach can be used by the government to improve the inspection planning, maintenance, and identify which factor that can cause damage to the water pumps to ensure the availability of potable water in Tanzania. Using data mining approach is cost-effective, less time consuming and faster than manual inspection.
Microcontroller Based Home Automation System With Security

With advancement of technology things are becoming simpler and easier for us. Automatic systems are being preferred over manual system. This unit talks about the basic definitions needed to understand the Project better and further defines the technical criteria to be implemented as a part of this project. Keywords-component; Automation, 8051 microcontroller, LDR, LED, ADC, Relays, LCD display, Sensors, Stepper motor
I Know the Feeling: Learning to Converse with Empathy

Beyond understanding what is being discussed, human communication requires an awareness of what someone is feeling. One challenge for dialogue agents is being able to recognize feelings in the conversation partner and reply accordingly, a key communicative skill that is trivial for humans. Research in this area is made difficult by the paucity of large-scale publicly available datasets both for emotion and relevant dialogues. This work proposes a new task for empathetic dialogue generation and EMPATHETICDIALOGUES, a dataset of 25k conversations grounded in emotional contexts to facilitate training and evaluating dialogue systems. Our experiments indicate that models explicitly leveraging emotion predictions from previous utterances are perceived to be more empathetic by human evaluators, while improving on other metrics as well (e.g. perceived relevance of responses, BLEU scores).
Duplicate Detection on GPUs

With the ever increasing volume of data and the ability to integrate different data sources, data quality problems abound. Duplicate detection, as an integral part of data cleansing, is essential in modern information systems. We present a complete duplicate detection workflow that utilizes the capabilities of modern graphics processing units (GPUs) to increase the efficiency of finding duplicates in very large datasets. Our solution covers several well-known algorithms for pair selection, attribute-wise similarity comparison, record-wise similarity aggregation, and clustering. We redesigned these algorithms to run memory-efficiently and in parallel on the GPU. Our experiments demonstrate that the GPU-based workflow is able to outperform a CPU-based implementation on large, real-world datasets. For instance, the GPU-based algorithm deduplicates a dataset with 1.8m entities 10 times faster than a common CPU-based algorithm using comparably priced hardware.
RAP: An End-to-End Rate-Based Congestion Control Mechanism for Realtime Streams in the Internet

End-to-end congestion control mechanisms have been critical to the robustness and stability of the Internet. Most of today’s Internet traffic is TCP, and we expect this to remain so in the future. Thus, having “TCP-friendly” behavior is crucial for new applications. However, the emergence of non-congestion-controlled realtime applications threatens unfairness to competing TCP traffic and possible congestion collapse. We present an end-to-end TCP-friendly Rate Adaptation Protocol (RAP), which employs an additive-increase, multiplicativedecrease (AIMD) algorithm. It is well suited for unicast playback of realtime streams and other semi-reliable rate-based applications. Its primary goal is to be fair and TCP-friendly while separating network congestion control from application-level reliability. We evaluate RAP through extensive simulation, and conclude that bandwidth is usually evenly shared between TCP and RAP traffic. Unfairness to TCP traffic is directly determined by how TCP diverges from the AIMD algorithm. Basic RAP behaves in a TCPfriendly fashion in a wide range of likely conditions, but we also devised a fine-grain rate adaptation mechanism to extend this range further. Finally, we show that deploying RED queue management can result in an ideal fairness between TCP and RAP traffic.
Bayesian LSTMs in medicine

The medical field stands to see significant benefits from the recent advances in deep learning. Knowing the uncertainty in the decision made by any machine learning algorithm is of utmost importance for medical practitioners. This study demonstrates the utility of using Bayesian LSTMs for classification of medical time series. Four medical time series datasets are used to show the accuracy improvement Bayesian LSTMs provide over standard LSTMs. Moreover, we show cherry-picked examples of confident and uncertain classifications of the medical time series. With simple modifications of the common practice for deep learning, significant improvements can be made for the medical practitioner and patient.
Trust Bit: Reward-based intelligent vehicle commination using blockchain paper

The Intelligent vehicle is experiencing revolutionary growth in research and industry, but it still suffers from a lot of security vulnerabilities. Traditional security methods are incapable of providing secure IV, mainly in terms of communication. In IV communication, major issues are trust and data accuracy of received and broadcasted reliable data in the communication channel. Blockchain technology works for the cryptocurrency, Bitcoin which has been recently used to build trust and reliability in peer-to-peer networks with similar topologies to IV Communication world. IV to IV, communicate in a decentralized manner within communication networks. In this paper, we have proposed, Trust Bit (TB) for IV communication among IVs using Blockchain technology. Our proposed trust bit provides surety for each IVs broadcasted data, to be secure and reliable in every particular networks. Our Trust Bit is a symbol of trustworthiness of vehicles behavior, and vehicles legal and illegal action. Our proposal also includes a reward system, which can exchange some TB among IVs, during successful communication. For the data management of this trust bit, we have used blockchain technology in the vehicular cloud, which can store all Trust bit details and can be accessed by IV anywhere and anytime. Our proposal provides secure and reliable information. We evaluate our proposal with the help of IV communication on intersection use case which analyzes a variety of trustworthiness between IVs during communication.
Constrained Convolutional Neural Networks for Weakly Supervised Segmentation

We present an approach to learn a dense pixel-wise labeling from image-level tags. Each image-level tag imposes constraints on the output labeling of a Convolutional Neural Network (CNN) classifier. We propose Constrained CNN (CCNN), a method which uses a novel loss function to optimize for any set of linear constraints on the output space (i.e. predicted label distribution) of a CNN. Our loss formulation is easy to optimize and can be incorporated directly into standard stochastic gradient descent optimization. The key idea is to phrase the training objective as a biconvex optimization for linear models, which we then relax to nonlinear deep networks. Extensive experiments demonstrate the generality of our new learning framework. The constrained loss yields state-of-the-art results on weakly supervised semantic image segmentation. We further demonstrate that adding slightly more supervision can greatly improve the performance of the learning algorithm.
Learning to learn by gradient descent by gradient descent

The move from hand-designed features to learned features in machine learning has been wildly successful. In spite of this, optimization algorithms are still designed by hand. In this paper we show how the design of an optimization algorithm can be cast as a learning problem, allowing the algorithm to learn to exploit structure in the problems of interest in an automatic way. Our learned algorithms, implemented by LSTMs, outperform generic, hand-designed competitors on the tasks for which they are trained, and also generalize well to new tasks with similar structure. We demonstrate this on a number of tasks, including simple convex problems, training neural networks, and styling images with neural art.
Molecular and biotechnological aspects of microbial proteases.

Proteases represent the class of enzymes which occupy a pivotal position with respect to their physiological roles as well as their commercial applications. They perform both degradative and synthetic functions. Since they are physiologically necessary for living organisms, proteases occur ubiquitously in a wide diversity of sources such as plants, animals, and microorganisms. Microbes are an attractive source of proteases owing to the limited space required for their cultivation and their ready susceptibility to genetic manipulation. Proteases are divided into exo- and endopeptidases based on their action at or away from the termini, respectively. They are also classified as serine proteases, aspartic proteases, cysteine proteases, and metalloproteases depending on the nature of the functional group at the active site. Proteases play a critical role in many physiological and pathophysiological processes. Based on their classification, four different types of catalytic mechanisms are operative. Proteases find extensive applications in the food and dairy industries. Alkaline proteases hold a great potential for application in the detergent and leather industries due to the increasing trend to develop environmentally friendly technologies. There is a renaissance of interest in using proteolytic enzymes as targets for developing therapeutic agents. Protease genes from several bacteria, fungi, and viruses have been cloned and sequenced with the prime aims of (i) overproduction of the enzyme by gene amplification, (ii) delineation of the role of the enzyme in pathogenecity, and (iii) alteration in enzyme properties to suit its commercial application. Protein engineering techniques have been exploited to obtain proteases which show unique specificity and/or enhanced stability at high temperature or pH or in the presence of detergents and to understand the structure-function relationships of the enzyme. Protein sequences of acidic, alkaline, and neutral proteases from diverse origins have been analyzed with the aim of studying their evolutionary relationships. Despite the extensive research on several aspects of proteases, there is a paucity of knowledge about the roles that govern the diverse specificity of these enzymes. Deciphering these secrets would enable us to exploit proteases for their applications in biotechnology.
Ambidextrous Organizational and Individual Competencies in Open Innovation : The Dawn of a new Research Agenda

This paper describes a conceptual approach to individual and organizational competencies needed for Open Innovation (OI) using a new ambidexterity model. It starts from the assumption that the entire innovation process is rarely open by all means, as the OI concept may suggest. It rather takes into consideration that in practice especially for early phases of the innovation process the organization and their innovation actors are opening up for new ways of joint ideation, collaboration etc. to gain a maximum of explorative performance and effectiveness. Though, when it comes to committing considerable resources to development and implementation activities, the innovation process usually closes step by step as efficiency criteria gain ground for a maximum of knowledge exploitation. The ambidexterity model of competences for OI refers to these tensions and provides a new framework to understand the needs of industry and Higher Education Institutes (HEI) to develop appropriate exploration and exploitation competencies for OI.
KnowSim: A Document Similarity Measure on Structured Heterogeneous Information Networks

As a fundamental task, document similarity measure has broad impact to document-based classification, clustering and ranking. Traditional approaches represent documents as bag-of-words and compute document similarities using measures like cosine, Jaccard, and dice. However, entity phrases rather than single words in documents can be critical for evaluating document relatedness. Moreover, types of entities and links between entities/words are also informative. We propose a method to represent a document as a typed heterogeneous information network (HIN), where the entities and relations are annotated with types. Multiple documents can be linked by the words and entities in the HIN. Consequently, we convert the document similarity problem to a graph distance problem. Intuitively, there could be multiple paths between a pair of documents. We propose to use the meta-path defined in HIN to compute distance between documents. Instead of burdening user to define meaningful meta paths, an automatic method is proposed to rank the meta-paths. Given the meta-paths associated with ranking scores, an HIN-based similarity measure, KnowSim, is proposed to compute document similarities. Using Freebase, a well-known world knowledge base, to conduct semantic parsing and construct HIN for documents, our experiments on 20Newsgroups and RCV1 datasets show that KnowSim generates impressive high-quality document clustering.
The Effects of Extracorporeal Shock Wave Therapy in Patients with Coccydynia: A Randomized Controlled Trial

Coccydynia is pain in the coccygeal region, and usually treated conservatively. Extracorporeal shock wave therapy (ESWT) was incorporated as non-invasive treatment of many musculoskeletal conditions. However, the effects of ESWT on coccydynia are less discussed. The purpose of this study is to evaluate the effects of ESWT on the outcomes of coccydynia. Patients were allocated to ESWT (n = 20) or physical modality (SIT) group (n = 21) randomly, and received total treatment duration of 4 weeks. The visual analog scale (VAS), Oswestry disability index (ODI), and self-reported satisfaction score were used to assess treatment effects. The VAS and ODI scores were significantly decreased after treatment in both groups, and the decrease in the VAS score was significantly greater in the ESWT group. The mean proportional changes in the ODI scores were greater in the ESWT group than in the SIT group, but the between-group difference was not statistically significant. The patients in the ESWT group had significantly higher subjective satisfaction scores than SIT group. We concluded that ESWT is more effective and satisfactory in reducing discomfort and disability caused by coccydynia than the use of physical modalities. Thus, ESWT is recommended as an alternative treatment option for patients with coccydynia.
Transformer to CNN: Label-scarce distillation for efficient text classification

Significant advances have been made in Natural Language Processing (NLP) mod1 elling since the beginning of 2018. The new approaches allow for accurate results, 2 even when there is little labelled data, because these NLP models can benefit from 3 training on both task-agnostic and task-specific unlabelled data. However, these 4 advantages come with significant size and computational costs. 5 This workshop paper outlines how our proposed convolutional student architec6 ture, having been trained by a distillation process from a large-scale model, can 7 achieve 300× inference speedup and 39× reduction in parameter count. In some 8 cases, the student model performance surpasses its teacher on the studied tasks. 9
Weaving Multi-scale Context for Single Shot Detector

Aggregating context information from multiple scales has been proved to be effective for improving accuracy of Single Shot Detectors (SSDs) on object detection. However, existing multi-scale context fusion techniques are computationally expensive, which unfavorably diminishes the advantageous speed of SSD. In this work, we propose a novel network topology, called WeaveNet, that can efficiently fuse multi-scale information and boost the detection accuracy with negligible extra cost. The proposed WeaveNet iteratively weaves context information from adjacent scales together to enable more sophisticated context reasoning while maintaining fast speed. Built by stacking light-weight blocks, WeaveNet is easy to train without requiring batch normalization and can be further accelerated by our proposed architecture simplification. Experimental results on PASCAL VOC 2007, PASCAL VOC 2012 benchmarks show signification performance boost brought by WeaveNet. For 320×320 input of batch size = 8, WeaveNet reaches 79.5% mAP on PASCAL VOC 2007 test in 101 fps with only 4 fps extra cost, and further improves to 79.7% mAP with more iterations.
Minimal Recursion Semantics: An Introduction

Minimal recursion semantics (MRS) is a framework for computational semantics that is suitable for parsing and generation and that can be implemented in typed feature structure formalisms. We discuss why, in general, a semantic representation with minimal structure is desirable and illustrate how a descriptively adequate representation with a nonrecursive structure may be achieved. MRS enables a simple formulation of the grammatical constraints on lexical and phrasal semantics, including the principles of semantic composition. We have integrated MRS with a broad-coverage HPSG grammar.
The Hamburg Metaphor Database project: issues in resource creation

This paper concerns metaphor resource creation. It provides an account of methods used, problems discovered, and insights gained at the Hamburg Metaphor Database project, intended to inform similar resource creation initiatives, as well as future metaphor processing algorithms. After introducing the project, the theoretical underpinnings that motivate the subdivision of represented information into a conceptual and a lexical level are laid out. The acquisition of metaphor attestations from electronic corpora is explained, and annotation practices as well as database contents are evaluated. The paper concludes with an overview of related projects and an outline of possible future work.
A Load Balancing Model Based on Cloud Partitioning for the Public Cloud

Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
All You Need is Beyond a Good Init: Exploring Better Solution for Training Extremely Deep Convolutional Neural Networks with Orthonormality and Modulation

Deep neural network is difficult to train and this predicament becomes worse as the depth increases. The essence of this problem exists in the magnitude of backpropagated errors that will result in gradient vanishing or exploding phenomenon. We show that a variant of regularizer which utilizes orthonormality among different filter banks can alleviate this problem. Moreover, we design a backward error modulation mechanism based on the quasi-isometry assumption between two consecutive parametric layers. Equipped with these two ingredients, we propose several novel optimization solutions that can be utilized for training a specific-structured (repetitively triple modules of Conv-BNReLU) extremely deep convolutional neural network (CNN) WITHOUT any shortcuts/ identity mappings from scratch. Experiments show that our proposed solutions can achieve distinct improvements for a 44-layer and a 110-layer plain networks on both the CIFAR-10 and ImageNet datasets. Moreover, we can successfully train plain CNNs to match the performance of the residual counterparts. Besides, we propose new principles for designing network structure from the insights evoked by orthonormality. Combined with residual structure, we achieve comparative performance on the ImageNet dataset.
Zero-Shot Relation Extraction via Reading Comprehension

We show that relation extraction can be reduced to answering simple reading comprehension questions, by associating one or more natural-language questions with each relation slot. This reduction has several advantages: we can (1) learn relationextraction models by extending recent neural reading-comprehension techniques, (2) build very large training sets for those models by combining relation-specific crowd-sourced questions with distant supervision, and even (3) do zero-shot learning by extracting new relation types that are only specified at test-time, for which we have no labeled training examples. Experiments on a Wikipedia slot-filling task demonstrate that the approach can generalize to new questions for known relation types with high accuracy, and that zero-shot generalization to unseen relation types is possible, at lower accuracy levels, setting the bar for future work on this task.
Image Captioning with Deep Bidirectional LSTMs and Multi-Task Learning

Generating a novel and descriptive caption of an image is drawing increasing interests in computer vision, natural language processing, and multimedia communities. In this work, we propose an end-to-end trainable deep bidirectional LSTM (Bi-LSTM (Long Short-Term Memory)) model to address the problem. By combining a deep convolutional neural network (CNN) and two separate LSTM networks, our model is capable of learning long-term visual-language interactions by making use of history and future context information at high-level semantic space. We also explore deep multimodal bidirectional models, in which we increase the depth of nonlinearity transition in different ways to learn hierarchical visual-language embeddings. Data augmentation techniques such as multi-crop, multi-scale, and vertical mirror are proposed to prevent overfitting in training deep models. To understand how our models “translate” image to sentence, we visualize and qualitatively analyze the evolution of Bi-LSTM internal states over time. The effectiveness and generality of proposed models are evaluated on four benchmark datasets: Flickr8K, Flickr30K, MSCOCO, and Pascal1K datasets. We demonstrate that Bi-LSTM models achieve highly competitive performance on both caption generation and image-sentence retrieval even without integrating an additional mechanism (e.g., object detection, attention model). Our experiments also prove that multi-task learning is beneficial to increase model generality and gain performance. We also demonstrate the performance of transfer learning of the Bi-LSTM model significantly outperforms previous methods on the Pascal1K dataset.