nils-herrmann/clean_scirepeval huggingface dataset:
Energy optimisation of hybrid off-grid system for remote telecommunication base station deployment in Malaysia

Cellular network operators are always seeking to increase the area of coverage of their networks, open up new markets and provide services to potential customers in remote rural areas. However, increased energy consumption, operator energy cost and the potential environmental impact of increased greenhouse gas emissions and the exhaustion of non-renewable energy resources (fossil fuel) pose major challenges to cellular network operators. The specific power supply needs for rural base stations (BSs) such as cost-effectiveness, efficiency, sustainability and reliability can be satisfied by taking advantage of the technological advances in renewable energy. This study investigates the possibility of decreasing both operational expenditure (OPEX) and greenhouse gas emissions with guaranteed sustainability and reliability for rural BSs using a solar photovoltaic/diesel generator hybrid power system. Three key aspects have been investigated: (i) energy yield, (ii) economic factors and (iii) greenhouse gas emissions. The results showed major benefits for mobile operators in terms of both environmental conservation and OPEX reduction, with an average annual OPEX savings of 43% to 47% based on the characteristics of solar radiation exposure in Malaysia. Finally, the paper compares the feasibility of using the proposed approach in a four-season country and compares the results against results obtained in Malaysia, which is a country with a tropical climate.
Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization

One of the critical components of mapping an unknown environment is the robot’s ability to locate itself on a partially explored map. This becomes challenging when the robot experiences positioning error, does not have an external positioning device, nor the luxury of engineered landmarks placed in its free space. This paper presents a new method for simultaneous localization and mapping that exploits the topology of the robot’s free space to localize the robot on a partially constructed map. The topology of the environment is encoded in a topological map; the particular topological map used in this paper is the generalized Voronoi graph (GVG), which also encodes some metric information about the robot’s environment, as well. In this paper, we present the low-level control laws that generate the GVG edges and nodes, thereby allowing for exploration of an unknown space. With these prescribed control laws, the GVG (or other topological map) can be viewed as an arbitrator for a hybrid control system that determines when to invoke a particular low-level controller from a set of controllers all working toward the high-level capability of mobile robot exploration. The main contribution, however, is using the graph structure of the GVG, via a graph matching process, to localize the robot. Experimental results verify the described work.
Analysis of Fraud Detection Mechanism in Health Insurance Using Statistical Data Mining Techniques

Health insurance fraud increases the disorganization and unfairness in our society. Health care fraud leads to substantial losses of money and very costly to health care insurance system. It is horrible because the percentage of health insurance fraud keeps increasing every year in many countries. To address this widespread problem, effective techniques are in need to detect fraudulent claims in health insurance sector. The application of data mining is specifically relevant and it has been successfully applied in medical needs for its reliable precision accuracy and rapid beneficial results. This paper aims to provide a comprehensive survey of the statistical data mining methods applied to detect fraud in health insurance sector.
Cross-Platform Emoji Interpretation: Analysis, a Solution, and Applications

Most social media platforms are largely based on text, and users oen write posts to describe where they are, what they are seeing, and how they are feeling. Because wrien text lacks the emotional cues of spoken and face-to-face dialogue, ambiguities are common in wrien language. is problem is exacerbated in the short, informal nature of many social media posts. To bypass this issue, a suite of special characters called “emojis,” which are small pictograms, are embedded within the text. Many emojis are small depictions of facial expressions designed to help disambiguate the emotional meaning of the text. However, a new ambiguity arises in the way that emojis are rendered. Every platform (Windows, Mac, and Android, to name a few) renders emojis according to their own style. In fact, it has been shown that some emojis can be rendered so dierently that they look “happy” on some platforms, and “sad” on others. In this work, we use real-world data to verify the existence of this problem. We verify that the usage of the same emoji can be signicantly dierent across platforms, with some emojis exhibiting dierent sentiment polarities on dierent platforms. We propose a solution to identify the intended emoji based on the platform-specic nature of the emoji used by the author of a social media post. We apply our solution to sentiment analysis, a task that can benet from the emoji calibration technique we use in this work. We conduct experiments to evaluate the eectiveness of the mapping in this task.  Fred Morstaer, Kai Shu, Suhang Wang, and Huan Liu. 2016. Cross-Platform Emoji Interpretation: Analysis, a Solution, and Applications.   Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for prot or commercial advantage and that copies bear this notice and the full citation on the rst page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permied. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specic permission and/or a fee. Request permissions from permissions@acm.org. Conference’17, Washington, DC, USA  DOI: 10.1145/nnnnnnn.nnnnnnn
Mongodb, couchbase: Performance comparison for image dataset

In today's world data is growing very rapidly, which we call as big data. To deal with these large data sets, currently we are using NoSQL databases, as relational database is not capable for handling such data. These schema less NoSQL database allow us to handle unstructured data. Through this paper we are comparing two NoSQL databases MongoDB and CouchBase server, in terms of image storage and retrieval. Aim behind selecting these two databases as both comes under Document store category. Major applications like social media, traffic analysis, criminal database etc. require image database. The motivation behind this paper is to compare database performance in terms of time required to store and retrieve images from database. In this paper, firstly we are going describe advantages of NoSQL databases over SQL, then brief idea about MongoDB and CouchBase and finally comparison of time required to insert various size images in databases and to retrieve various size images using front end tool Java.
Skill Specific Unemployment with Imperfect Substitution of Skills

A large body of literature explains the inferior position of unskilled workers by imposing a structural shift in the labor force skill composition. This paper takes a different approach by emphasizing the connection between cyclical variations in skilled and unskilled labor markets. Using a stylized business cycle model with search frictions in the respective sub-markets, I find that imperfect substitution between skilled and unskilled labor creates a channel for the variations in the sub-markets. Together with a general labor augmenting technology shock, it can generate downward sloping Beveridge curves. Calibrating the model to US data yields higher volatilities in the unskilled labor markets and reproduces stylized business cycle facts.
Deep Graph Infomax

We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs—both derived using established graph convolutional network architectures. The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks. In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups. We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning.
NVIDIA Tensor Core Programmability, Performance & Precision

The NVIDIA Volta GPU microarchitecture introduces a specialized unit, called Tensor Core that performs one matrix-multiply-and-accumulate on 4x4 matrices per clock cycle. The NVIDIA Tesla V100 accelerator, featuring the Volta microarchitecture, provides 640 Tensor Cores with a theoretical peak performance of 125 Tflops/s in mixed precision. In this paper, we investigate current approaches to program NVIDIA Tensor Cores, their performances and the precision loss due to computation in mixed precision. Currently, NVIDIA provides three different ways of programming matrix-multiply-and-accumulate on Tensor Cores: the CUDA Warp Matrix Multiply Accumulate (WMMA) API, CUTLASS, a templated library based on WMMA, and cuBLAS GEMM. After experimenting with different approaches, we found that NVIDIA Tensor Cores can deliver up to 83 Tflops/s in mixed precision on a Tesla V100 GPU, seven and three times the performance in single and half precision respectively. A WMMA implementation of batched GEMM reaches a performance of 4 Tflops/s. While precision loss due to matrix multiplication with half precision input might be critical in many HPC applications, it can be considerably reduced at the cost of increased computation. Our results indicate that HPC applications using matrix multiplications can strongly benefit from using of NVIDIA Tensor Cores.
Framework for surveillance of instant messages in instant messengers and social neworking sites using data mining and ontology

Innumerable terror and suspicious messages are sent through Instant Messengers (IM) and Social Networking Sites (SNS) which are untraced, leading to hindrance for network communications and cyber security. We propose a Framework that discover and predict such messages that are sent using IM or SNS like Facebook, Twitter, LinkedIn, and others. Further, these instant messages are put under surveillance that identifies the type of suspected cyber threat activity by culprit along with their personnel details. Framework is developed using Ontology based Information Extraction technique (OBIE), Association rule mining (ARM) a data mining technique with set of pre-defined Knowledge-based rules (logical), for decision making process that are learned from domain experts and past learning experiences of suspicious dataset like GTD (Global Terrorist Database). The experimental results obtained will aid to take prompt decision for eradicating cyber crimes.
Low rank subspace clustering (LRSC)

We consider the problem of fitting a union of subspaces to a collection of data points drawn from one or more subspaces and corrupted by noise and/or gross errors. We pose this problem as a non-convex optimization problem, where the goal is to decompose the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise and/or gross errors. By self-expressive we mean a dictionary whose atoms can be expressed as linear combinations of themselves with low-rank coefficients. In the case of noisy data, our key contribution is to show that this non-convex matrix decomposition problem can be solved in closed form from the SVD of the noisy data matrix. The solution involves a novel polynomial thresholding operator on the singular values of the data matrix, which requires minimal shrinkage. For one subspace, a particular case of our framework leads to classical PCA, which requires no shrinkage. For multiple subspaces, the low-rank coefficients obtained by our framework can be used to construct a data affinity matrix from which the clustering of the data according to the subspaces can be obtained by spectral clustering. In the case of data corrupted by gross errors, we solve the problem using an alternating minimization approach, which combines our polynomial thresholding operator with the more traditional shrinkage-thresholding operator. Experiments on motion segmentation and face clustering show that our framework performs on par with state-of-the-art techniques at a reduced computational cost. ! 2013 Elsevier B.V. All rights reserved.
Therapeutic applications of pomegranate (Punica granatum L.): a review.

The pomegranate, Punica granatum L., is an ancient, mystical, unique fruit borne on a small, long-living tree cultivated throughout the Mediterranean region, as far north as the Himalayas, in Southeast Asia, and in California and Arizona in the United States. In addition to its ancient historical uses, pomegranate is used in several systems of medicine for a variety of ailments. The synergistic action of the pomegranate constituents appears to be superior to that of single constituents. In the past decade, numerous studies on the antioxidant, anticarcinogenic, and anti-inflammatory properties of pomegranate constituents have been published, focusing on treatment and prevention of cancer, cardiovascular disease, diabetes, dental conditions, erectile dysfunction, bacterial infections and antibiotic resistance, and ultraviolet radiation-induced skin damage. Other potential applications include infant brain ischemia, male infertility, Alzheimer's disease, arthritis, and obesity.
Cyberbullying and adolescent mental health: systematic review.

Cyberbullying is a new form of violence that is expressed through electronic media and has given rise to concern for parents, educators and researchers. In this paper, an association between cyberbullying and adolescent mental health will be assessed through a systematic review of two databases: PubMed and Virtual Health Library (BVS). The prevalence of cyberbullying ranged from 6.5% to 35.4%. Previous or current experiences of traditional bullying were associated with victims and perpetrators of cyberbullying. Daily use of three or more hours of Internet, web camera, text messages, posting personal information and harassing others online were associated with cyberbullying. Cybervictims and cyberbullies had more emotional and psychosomatic problems, social difficulties and did not feel safe and cared for in school. Cyberbullying was associated with moderate to severe depressive symptoms, substance use, ideation and suicide attempts. Health professionals should be aware of the violent nature of interactions occurring in the virtual environment and its harm to the mental health of adolescents.
GPS Digital Tracking Loops Design for High Dynamic Launching Vehicles

This paper describes the design of digital tracking loops for GPS receivers in a high dynamics environment, without external aiding. We adopted the loop structure of a frequency-locked loop (FLL)-assisted phase-locked loop (PLL) and design it to track accelerations steps, as those occurring in launching vehicles. We used a completely digital model of the loop where the FLL and PLL parts are jointly designed, as opposed to the classical discretized analog model with separately designed FLL and PLL. The new approach does not increase the computational burden. We performed simulations and real RF signal experiments of a fixed-point implementation of the loop, showing that reliable tracking of steps up to 40 g can be achieved
Agile Software Development

With the further development of computer technology, the software development process has some new goals and requirements. In order to adapt to these changes, people has optimized and improved the previous method. At the same time, some of the traditional software development methods have been unable to adapt to the requirements of people. Therefore, in recent years there have been some new lightweight software process development methods, That is agile software development, which is widely used and promoted. In this paper the author will firstly introduces the background and development about agile software development, as well as comparison to the traditional software development. Then the second chapter gives the definition of agile software development and characteristics, principles and values. In the third chapter the author will highlight several different agile software development methods, and characteristics of each method. In the fourth chapter the author will cite a specific example, how agile software development is applied in specific areas.Finally the author will conclude his opinion. This article aims to give readers a overview of agile software development and how people use it in practice.
An Overview of the EPIC Architecture for Cognition and Performance With Application to Human-Computer Interaction

EPIC (Executive Process-Interactive Control) is a cognitive architecture especially suited for modeling human multimodal and multiple-task performance. The EPIC architecture includes peripheral sensory-motor processors surrounding a production-rule cognitive processor, and is being used to construct precise computational models for a variety of HCI situations. Some of these models are briefly illustrated here to demonstrate how EPIC clarifies basic properties of human performance and provides usefully precise accounts of performance speed and accuracy.
Automated Pruning for Deep Neural Network Compression

In this work we present a method to improve the pruning step of the current state-of-the-art methodology to compress neural networks. The novelty of the proposed pruning technique is in its differentiability, which allows pruning to be performed during the backpropagation phase of the network training. This enables an end-to-end learning and strongly reduces the training time. The technique is based on a family of differentiable pruning functions and a new regularizer specifically designed to enforce pruning. The experimental results show that the joint optimization of both the thresholds and the network weights permits to reach a higher compression rate, reducing the number of weights of the pruned network by a further 14% to 33 % compared to the current state-of-the-art. Furthermore, we believe that this is the first study where the generalization capabilities in transfer learning tasks of the features extracted by a pruned network are analyzed. To achieve this goal, we show that the representations learned using the proposed pruning methodology maintain the same effectiveness and generality of those learned by the corresponding non-compressed network on a set of different recognition tasks.
Stochastic Training of Graph Convolutional Networks with Variance Reduction

Graph convolutional networks (GCNs) are powerful deep neural networks for graph-structured data. However, GCN computes the representation of a node recursively from its neighbors, making the receptive field size grow exponentially with the number of layers. Previous attempts on reducing the receptive field size by subsampling neighbors do not have convergence guarantee, and their receptive field size per node is still in the order of hundreds. In this paper, we develop control variate based algorithms with new theoretical guarantee to converge to a local optimum of GCN regardless of the neighbor sampling size. Empirical results show that our algorithms enjoy similar convergence rate and model quality with the exact algorithm using only two neighbors per node. The running time of our algorithms on a large Reddit dataset is only one seventh of previous neighbor sampling algorithms.
Molecular mechanisms of nociception

The sensation of pain alerts us to real or impending injury and triggers appropriate protective responses. Unfortunately, pain often outlives its usefulness as a warning system and instead becomes chronic and debilitating. This transition to a chronic phase involves changes within the spinal cord and brain, but there is also remarkable modulation where pain messages are initiated — at the level of the primary sensory neuron. Efforts to determine how these neurons detect pain-producing stimuli of a thermal, mechanical or chemical nature have revealed new signalling mechanisms and brought us closer to understanding the molecular events that facilitate transitions from acute to persistent pain.
Working with UML: A software Design Process Based on Inspections for the Unified Modeling Language

This text describes a simple and effective object oriented software design process template having UML as the modeling language and extensively using inspections to support the construction and maintenance of software products. This software design process uses a sequential organization, based on the waterfall approach, for two reasons: to simplify the explanation of design activities in the context of All rights of reproduction in any form reserved.  this text and to make available a standard process that can be continuously improved by developers. The component phases of this design process are described in a way that gives developers the freedom to reorganize the overall process based on their own needs and environment. In addition, a survey is provided of specific literature regarding important aspects of UML and the object oriented paradigm.
A Recurrent Neural Network Survival Model: Predicting Web User Return Time

The size of a website’s active user base directly affects its value. Thus, it is important to monitor and influence a user’s likelihood to return to a site. Essential to this is predicting when a user will return. Current state of the art approaches to solve this problem come in two flavors: (1) Recurrent Neural Network (RNN) based solutions and (2) survival analysis methods. We observe that both techniques are severely limited when applied to this problem. Survival models can only incorporate aggregate representations of users instead of automatically learning a representation directly from a raw time series of user actions. RNNs can automatically learn features, but can not be directly trained with examples of non-returning users who have no target value for their return time. We develop a novel RNN survival model that removes the limitations of the state of the art methods. We demonstrate that this model can successfully be applied to return time prediction on a large e-commerce dataset with a superior ability to discriminate between returning and non-returning users than either method applied in isolation.