Reviewerly/exHarmony huggingface dataset:
Elastic neural network method for load prediction in cloud computing grid

&lt;span&gt;Cloud computing still has no standard definition, yet it is concerned with Internet or network on-demand delivery of resources and services. It has gained much popularity in last few years due to rapid growth in technology and the Internet. Many issues yet to be tackled within cloud computing technical challenges, such as Virtual Machine migration, server association, fault tolerance, scalability, and availability. The most we are concerned with in this research is balancing servers load; the way of spreading the load between various nodes exists in any distributed systems that help to utilize resource and job response time, enhance scalability, and user satisfaction. Load rebalancing algorithm with dynamic resource allocation is presented to adapt with changing needs of a cloud environment. This research presents a modified elastic adaptive neural network (EANN) with modified adaptive smoothing errors, to build an evolving system to predict Virtual Machine load. To evaluate the proposed balancing method, we conducted a series of simulation studies using cloud simulator and made comparisons with previously suggested approaches in the previous work. The experimental results show that suggested method betters present approaches significantly and all these approaches.&lt;/span&gt;
Geochemical investigations of the Woodford–Chattanooga and Fayetteville Shales: Implications for genesis of the Mississippi Valley–type zinc–lead ores in the southern Ozark Region and hydrocarbon exploration

The relationship between base metal deposits, especially Mississippi Valley–type (MVT) Pb–Zn deposits, and hydrocarbons is not well constrained. This is despite the fact that hydrocarbons generally occur in MVT deposits; the ores are emplaced in the same temperature range as hydrocarbon maturation and migration, and the deposits commonly occur in proximity to metal-rich black shales. Better understanding should lead to better exploration models for both hydrocarbons and MVT deposits. This connection is better understood with the help of Pb isotope patterns. Sphalerite Pb isotope compositions from the northern Arkansas and Tri-State mining districts and Woodford–Chattanooga and Fayetteville Shales were determined to assess the potential of shales as source rocks for the ore metals. The ores in both districts have a broad range of Pb isotope ratios and define linear trends, suggesting mixing of Pb from two distinct end members. Current results and previous depositional environment studies indicate the following: (1) shales deposited mainly under nonsulfidic anoxic conditions represent the less radiogenic end member, or (2) shales are the only source of ore metals. Given the array of organic molecules, each with their own thermochemical range, and the ways metals can be associated with them, the release of metals may cover varying ranges. Thus, the compositions of the released fluids would change through time and not have a single static composition, closely approximating the isotopic composition of the released metals at various times. Mineralization derived from a dynamically evolving fluid may show apparent end members, without the need to call on mixing of fluids from separate sources.
Soft Conditional Computation.

Convolutional layers are one of the basic building blocks of modern deep neural networks. One fundamental assumption is that convolutional kernels should be shared for all examples in a dataset. We propose conditionally parameterized convolutions (CondConv), which learn specialized convolutional kernels for each example. Replacing normal convolutions with CondConv enables us to increase the size and capacity of a network, while maintaining efficient inference. We demonstrate that scaling networks with CondConv improves the performance and inference cost trade-off of several existing convolutional neural network architectures on both classification and detection tasks. On ImageNet classification, our CondConv approach applied to EfficientNet-B0 achieves state-of-the-art performance of 78.3% accuracy with only 413M multiply-adds. Code and checkpoints for the CondConv Tensorflow layer and CondConv-EfficientNet models are available at: this https URL.
Data Augmentation and Feature Fusion for Melanoma Detection with Content Based Image Classification

Computer aided diagnosis has leveraged a new horizon for accurate diagnosis of numerous fatal diseases. Melanoma is considered as one of the most lethal form of skin cancer which is increasingly affecting the population in recent times. The disease can be completely healed if diagnosed and addressed at an early stage. However, in most of the cases patients receive delayed care which results in fatal consequences. The authors have attempted to design an automated melanoma detection system in this work by means of content based image classification. Extraction of content based descriptors can nullify the requirement for manual annotation of the dermoscopic images which consumes considerable time and effort. The work has also undertaken a fusion based approach for feature combination for evaluating classification performances of hybrid architecture. The results have outclassed the state-of-the-art outcomes and have established significant performance improvement.
Normalization and parsing algorithms for uncertain input

The automatic analysis (parsing) of natural language is an important ingredient for many natural language processing applications (search-engines, automatic translation, speech-processing, etc.), as it is the first step towards interpretation. For standard texts, like well-edited news articles, current parsers perform very well. However, for user-generated content, such as tweets, parser performance drops dramatically. In this research, we attempt to improve the automatic analysis of spontaneous language by translating it to 'normal' language. For example, the sentence pix comming tomorroe is translated to pictures coming tomorrow. In this example sentence, a variety of phenomena occurs: 'pix' is a replacement based on the pronunciation, whereas 'comming' is probably a typo. This translation is also referred to as 'normalization'. Based on the observation that the normalization problem actually consists of multiple sub-problems, we developed a modular normalization model: MoNoise. This normalization model reaches a new state-of-art performance on a variety of languages. Normalizing social media texts leads to a performance increase for syntactic parsers. In the basic setup, we use only the single best normalization candidate for each word, which might lead to error propagation. Hence, we introduce two novel methods to let the parser to take multiple normalization candidates into account per position, leading to further improvements in parser performance.
Task Programming Methodology for Powered Wheelchairs

The work described in this paper is directed towards applying task oriented methodologies to the creation of new types of powered wheelchairs. The new work has required the creation of a new type of task machine. In addition, new techniques to analyse situations and implement the results within a new user interface have been investigated. This paper describes task machinery in general and the stages required to create a task machine.
Decentralized Video Streaming in Multi-Hop Wireless Networks: Incentive Mechanism and Energy Efficiency

This paper studies video streaming from a source to multiple receivers in wireless networks. The video is streamed with the help of intermediate users who forward the video to others. Two main challenges affect user satisfaction in this network. The users usually have 1) different willingness to contribute (forwarding the video), and; 2) different preferences regarding the video quality. To overcome the challenges, we propose a framework based on an incentive/taxation mechanism in which the forwarding users, depending on their spent energy, are paid by their corresponding receivers. The video is layered such that the more video layers are received, the higher the quality-of-experience (QoE) and the higher the price. Using a decentralized game-theoretic algorithm, we define a user-specific utility function whose maximization determines the number of video layers a user wishes to receive. The utility function captures the user's preferences including the importance of the video quality to her and her willingness to contribute. Our model supports the multicast transmission by which the receivers can use a common forwarder and share the cost. The simulation results show that the proposed model not only provides a higher QoE for the users compared to the preference-agnostic models but also improves the network social-welfare.
Differentially Private Collaborative Learning for the IoT Edge

Collaborative learning based on training data contributed by many edge devices is a promising paradigm for implementing crowd intelligence. The collaboratively trained model generally provides superior classification performance due to the increased volume and expanded coverage of the training data. However, the data contribution may incur the concern of privacy breach. This paper presents the design of a privacy-preserving collaborative learning approach, in which the edge devices and the cloud train different stages of a deep neural network, and the data transmitted from an edge device to the honest-but-curious cloud is perturbed by Laplacian random noises to achieve e-differential privacy. We apply the proposed approach to a case study of collaboratively training a convolutional neural network for handwritten digit recognition. The results show that our approach maintains 99% and 96% classification accuracy in implementing privacy loss levels of e = 5 and e = 2, respectively.
Design of Database Teaching Model Based on Computational Thinking Training

This paper proposes to combine knowledge acquisition and thinking training in database course teaching, and designs a database teaching model based on computational thinking training. This model takes computational thinking as the core, and through the close combination of multiple levels of thinking ability and database teaching content, it promotes the improvement of students' thinking ability, trains students to feel and experience the role of thinking in the process of knowledge learning, and guides students to exercise and train their thinking ability independently, spontaneously and consciously in learning. A method of pre-processing before training model implementation is designed, and similar items are merged to design a processing scheme.
CNN and Traditional Classifiers Performance for Sign Language Recognition

Many people around the world are suffering from vocal and hearing disabilities and they communicate with others by actions rather than speech. They prefer sign language (hand gestures) to convey what revolves in their mind. Every language has some set of rules of grammar to express information in meaningful way but not everyone can recognize what is being conveyed through sign language. Thus the automatic translation of a sign language serves as basic need for overcoming many difficulties and providing convenience for impaired people in the developing era of technology. For many years, a lot of researchers have been working on developing the better algorithm for sign language communication using machine learning and computer vision techniques that passes through many stages such as pre-processing, segmentation, extraction of features and classification. But the efficient features can produce more effective and accurate results. This paper aims at comparing performance of different classifiers to deep convolutional neural network (CNN) on sign language dataset providing with and without local feature descriptor and bag of visual words. This is definitely a classification task for which CNN, Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM) are being widely considered. CNN is capable of extracting and representing high-level abstractions in the dataset that results good accuracy but some traditional classifiers are also capable for that when providing with good features. We evaluate the performance of MLP and SVM without and with Speed up Robust Features (SURF) on the same data given to CNN. Results are also discussed in this paper that shows MLP and SVM employing descriptor gives high accuracy than CNN.
PREDIKSI PRODUKTIVITAS JAGUNG DI INDONESIA SEBAGAI UPAYA ANTISIPASI IMPOR MENGGUNAKAN JARINGAN SARAF TIRUAN BACKPROPAGATION

Corn is a staple food that is still widely consumed by the population of Indonesia. Based on data from the Indonesian Statistics Agency, corn productivity in Indonesia from 2005 to 2015 calculated an unstable curve. Therefore this research was conducted to predict and see the large growth of maize in Indonesia for the following years so that the government has a reference to continuously strive to increase corn productivity in Indonesia in order to remain stable in order to meet the needs of Indonesian people to minimize corn imports. This study uses data on corn productivity in Indonesia in 2005-2015 sourced from the Indonesian Central Bureau of Statistics. The prediction algorithm used is the Backpropagation Neural Network. This algorithm is able to predict data well, especially data that is maintained for a certain period of time. To facilitate data analysis, the author uses the Matlab 2011b application. In this study, a training and testing process will be carried out using 5 network architecture models, namely 5-25-1, 5-43-1, 5-76-1, 5-78-1 and 7-128-1. Of the 5 architectural models, the best is 5-25-1 with the percentage of 88% and the MSE value of 0.00992433.
What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party Dialogue

Laura Aina, Carina Silberer, Ionut-Teodor Sorodoc, Matthijs Westera, Gemma Boleda. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Pre-processing Framework for Twitter Sentiment Classification

Twitter Sentiment Classification is undergoing great appeal from the research community; also, user posts and opinions are producing very interesting conclusions and information. In the context of this paper, a pre-processing tool was developed in Python language. This tool processes text and natural language data intending to remove wrong values and noise. The main reason for developing such a tool is to achieve sentiment analysis in an optimum and efficient way. The most remarkable characteristic is considered the use of emojis and emoticons in the sentiment analysis field. Moreover, supervised machine learning techniques were utilized for the analysis of users’ posts. Through our experiments, the performance of the involved classifiers, namely Naive Bayes and SVM, under specific parameters such as the size of the training data, the employed methods for feature selection (unigrams, bigrams and trigrams) are evaluated. Finally, the performance was assessed based on independent datasets through the application of k-fold cross validation.
A Privacy Preserving Multiagent System for Load Balancing in the Smart Grid

To improve system economics and reliability, microgrids (viz. power consumers equipped with local generators) can cooperatively utilize their local energy to facilitate load balancing on the power grid (balancing the regional supply and demand) via a multiagent system. However, due to the privacy concerns on continuously revealing each microgrid's local data (e.g., demand and supply at different times) for deriving real-time optimal balancing decisions, the application of such multiagent cooperation is still limited. In this paper, we design a novel privacy preserving multiagent system via an efficient cryptographic protocol for cooperatively balancing the regional supply and demand, as well as each microgrid's local supply and demand without disclosing their local data.
Creating quantum correlation from coherence via incoherent quantum operations

Quantum coherence and quantum correlation are two fundamental resources in quantum information and computation. Quantum correlated states in a bipartite system are the same as the states that are coherent with respect to any product basis, while a coherent state with respect to some given product basis may not be quantum correlated. In this work, we focus on the question of creating quantum correlation from a coherent state via incoherent quantum operations. We compare characterizations of incoherent operations (IOs), maximally incoherent operations, genuinely incoherent operations and coherence-breaking operations and then explicit interrelations among these operations. Moreover, we give the specific forms of states in two-qubit systems, called super-coherent ones, that can be mapped to quantum correlated ones via some genuinely incoherent operation. We find that there exist some coherent states that are not super-coherent. Furthermore, we prove that a state can be mapped to a quantum correlated state via an incoherent operation if and only if it is coherent. Lastly, we prove that any maximally entangled state can be mapped to an incoherent one via some one-side coherence-breaking operation.
Stabilizing GANs with Octave Convolutions.

In this preliminary report, we present a simple but very effective technique to stabilize the training of CNN based GANs. Motivated by recently published methods using frequency decomposition of convolutions (e.g. Octave Convolutions), we propose a novel convolution scheme to stabilize the training and reduce the likelihood of a mode collapse. The basic idea of our approach is to split convolutional filters into additive high and low frequency parts, while shifting weight updates from low to high during the training. Intuitively, this method forces GANs to learn low frequency coarse image structures before descending into fine (high frequency) details. Our approach is orthogonal and complementary to existing stabilization methods and can simply plugged into any CNN based GAN architecture. First experiments on the CelebA dataset show the effectiveness of the proposed method.
Fashion Editing with Multi-scale Attention Normalization.

Interactive fashion image manipulation, which enables users to edit images with sketches and color strokes, is an interesting research problem with great application value. Existing works often treat it as a general inpainting task and do not fully leverage the semantic structural information in fashion images. Moreover, they directly utilize conventional convolution and normalization layers to restore the incomplete image, which tends to wash away the sketch and color information. In this paper, we propose a novel Fashion Editing Generative Adversarial Network (FE-GAN), which is capable of manipulating fashion images by free-form sketches and sparse color strokes. FE-GAN consists of two modules: 1) a free-form parsing network that learns to control the human parsing generation by manipulating sketch and color; 2) a parsing-aware inpainting network that renders detailed textures with semantic guidance from the human parsing map. A new attention normalization layer is further applied at multiple scales in the decoder of the inpainting network to enhance the quality of the synthesized image. Extensive experiments on high-resolution fashion image datasets demonstrate that the proposed method significantly outperforms the state-of-the-art methods on image manipulation.
Real-Time Logo Detection in Brand-Related Social Media Images

This paper presents a work consisting in using deep convolutional neural networks (CNNs) for real-time logo detection in brand-related social media images. The final goal is to facilitate searching and discovering user-generated content (UGC) with potential value for digital marketing tasks. The images are captured in real time and automatically annotated with two CNNs designed for object detection, SSD InceptionV2 and Faster Atrous InceptionV4 (that provides better performance on small objects). We report experiments with 2 real brands, Estrella Damm and Futbol Club Barcelona. We examine the impact of different configurations and derive conclusions aiming to pave the way towards systematic and optimized methodologies for automatic logo detection in UGC.
PORTOS: Proof of Data Reliability for Real-World Distributed Outsourced Storage

Proofs of data reliability are cryptographic protocols that provide assurance to a user that a cloud storage system correctly stores her data and has provisioned sufficient redundancy to be able to guarantee reliable storage service. In this paper, we consider distributed cloud storage systems that make use of erasure codes to guarantee data reliability. We propose a novel proof of data reliability scheme, named PORTOS, that on the one hand guarantees the retrieval of the outsourced data in their entirety through the use of proofs of data possession and on the other hand ensures the actual storage of redundancy. PORTOS makes sure that redundancy is stored at rest and not computed on-the-fly (whenever requested) thanks to the use of time-lock puzzles. Furthermore, PORTOS delegates the burden of generating the redundancy to the cloud. The repair operations are also taken care of by the cloud. Hence, PORTOS is compatible with the current cloud computing model where the cloud autonomously performs all maintenance operations without any interaction with the user. The security of the solution is proved in the face of a rational adversary whereby the cheating cloud provider tries to gain storage savings without increasing its total operational cost.
A bird's eye view of knowledge needs related to penetration testing

According to the National Institute of Standards and Technology (NIST), penetration testing is an assessment conducted on software systems to identify vulnerabilities that could be exploited by adversaries1. Despite the importance of penetration testing in software security, practitioners search for strategies and guidance on how to get started in the domain of penetration testing. We hypothesize that practitioners have knowledge needs related to penetration testing, which can be synthesized using penetration testing-related questions posted on questions and answer (Q&A) websites. A systematic investigation can identify the knowledge needs of practitioners related to penetration testing, helping the cyber-security community in advancing the field of cyber-security education. The goal of this paper is to help cyber-security researchers in advancing the field of cyber-security education by analyzing penetration testing-related questions posted by practitioners.
Vocabulary Pyramid Network: Multi-Pass Encoding and Decoding with Multi-Level Vocabularies for Response Generation

We study the task of response generation.Conventional methods employ a fixed vocabulary and one-pass decoding, which not only make them prone to safe and general responses but also lack further refining to the first generated raw sequence.To tackle the above two problems, we present a Vocabulary Pyramid Network (VPN) which is able to incorporate multi-pass encoding and decoding with multi-level vocabularies into response generation.Specifically, the dialogue input and output are represented by multi-level vocabularies which are obtained from hierarchical clustering of raw words.Then, multi-pass encoding and decoding are conducted on the multilevel vocabularies.Since VPN is able to leverage rich encoding and decoding information with multi-level vocabularies, it has the potential to generate better responses.Experiments on English Twitter and Chinese Weibo datasets demonstrate that VPN remarkably outperforms strong baselines. Seq2Seq