Reviewerly/exHarmony huggingface dataset:
Scheduling mechanisms in the cloud environment: a methodological analysis

Purpose Today, with the rapid growth of cloud computing (CC), there exist several users that require to execute their tasks by the available resources to obtain the best performance, reduce response time and use resources. However, despite the significance of the scheduling issue in CC, as far as the authors know, there is not any systematic and inclusive paper about studying and analyzing the recent methods. This paper aims to review the current mechanisms and techniques, which can be addressed in this area. Design/methodology/approach The central purpose of this paper refers to offering a complete study of the state-of-the-art planning algorithms in the cloud and also instructions for future research. Besides, this paper offers a methodological analysis of the scheduling mechanisms in the cloud environment. Findings The central role of this paper is to present a summary of the present issues related to scheduling in the cloud environment, providing a structure of some popular techniques in cloud scheduling scope and defining key areas for the development of cloud scheduling techniques in the future research. Research limitations/implications In this paper, scheduling mechanisms are classified into two main categories include deterministic and non-deterministic algorithms; however, it can also be classified into different categories. In addition, the selection of all related papers could not be ensured. It is possible that some appropriate and related papers were removed in the search process. Practical implications According to the results of this paper, the requirement for more suitable algorithms exists to allocate tasks for resources in cloud environments. In addition, some principal rules in cloud scheduling should be re-evaluated to achieve maximum productivity and minimize wasted expense and effort. In this direction, to stay away from overloading and under loading of components and resources, the proposed method should execute workloads in an adaptable and scalable way. As the number of users increased in cloud environments, the number of tasks in the cloud that needed to be scheduled proportionally increased. Thus, an efficient mechanism is needed for scheduling tasks in these environments. Originality/value The general information gathered in this study makes the researchers acquainted with the state-of-the-art scheduling area of the cloud. Entirely, the answers to the research questions summarized the main objective of scheduling, current challenges, mechanisms and methods in the cloud systems. The authors hope that the results of this paper lead researchers to present more efficient scheduling techniques in cloud systems.
Evaluating the Progress of Deep Learning for Visual Relational Concepts

Convolutional Neural Networks (CNNs) have become the state of the art method for image classification in the last ten years. Despite the fact that they achieve superhuman classification accuracy on many popular datasets, they often perform much worse on more abstract image classification tasks. We will show that these difficult tasks are linked to relational concepts from cognitive psychology and that despite progress over the last few years, such relational reasoning tasks still remain difficult for current neural network architectures. We will review deep learning research that is linked to relational concept learning, even if it was not originally presented from this angle. Reviewing the current literature, we will argue that some form of attention will be an important component of future systems to solve relational tasks. In addition, we will point out the shortcomings of currently used datasets, and we will recommend steps to make future datasets more relevant for testing systems on relational reasoning.
Design of Near Optimal Convolutional Neural Network Based Crowd Density Classifier

Crowd density estimation and crowd counting has acquired importance towards Machine learning and Deep learning industry due to the improvement in performance, when compared to traditional computer vision techniques. This paper presents deep learning based optimal dimension convolutional neural network (CNN) for estimating crowd density, which is used to classify images of crowd into various density levels such as low crowd, very low crowd, moderate crowd, high crowd, very high crowd. This approach is experimented on existing datasets and gives the better accuracy with optimum network dimensions.
Classification of Sentiment Analysis Using Machine Learning

An application of the computational linguistics is identified as Natural Language Processing or NLP. With the help of NLP, the text can be analyzed. Any person’s opinion through which the emotions, attitude and thoughts can be communicated is known as sentiment. The surveys of human beings towards specific occasions, brands, items or organization can be known through sentiment analysis. Every one of the assumptions can be ordered into three unique categories, they are positive, negative and neutral. Twitter, being the utmost mainstream microblogging webpage, is utilized to gather the information for perform analysis. Tweepy is utilized to extract the source of information from Twitter. Python language is utilized in this exploration to execute the classification algorithm on the gathered information. In sentiment analysis, two steps namely feature extraction and classifications are implemented. The features are extracted using N-gram modeling technique. The opinion is classified among positive, negative and neutral by utilizing a supervised machine learning algorithm. In this research work, SVM (Support Vector Machine) and KNN (K-Nearest Neighbor) classification models are utilized. Also, we have shown both comparison for sentiment analysis.
Harnessing Code Switching to Transcend the Linguistic Barrier

Code mixing (or code switching) is a common phenomenon observed in social-media content generated by a linguistically diverse user-base. Studies show that in the Indian sub-continent, a substantial fraction of social media posts exhibit code switching. While the difficulties posed by code mixed documents to further downstream analyses are well-understood, lending visibility to code mixed documents under certain scenarios may have utility that has been previously overlooked. For instance, a document written in a mixture of multiple languages can be partially accessible to a wider audience; this could be particularly useful if a considerable fraction of the audience lacks fluency in one of the component languages. In this paper, we provide a systematic approach to sample code mixed documents leveraging a polyglot embedding based method that requires minimal supervision. In the context of the 2019 India-Pakistan conflict triggered by the Pulwama terror attack, we demonstrate an untapped potential of harnessing code mixing for human well-being: starting from an existing hostility diffusing \emph{hope speech} classifier solely trained on English documents, code mixed documents are utilized as a bridge to retrieve \emph{hope speech} content written in a low-resource but widely used language - Romanized Hindi. Our proposed pipeline requires minimal supervision and holds promise in substantially reducing web moderation efforts.
Long Short-Term Attention

Attention is an important cognition process of humans, which helps humans concentrate on critical information during their perception and learning. However, although many machine learning models can remember information of data, they have no the attention mechanism. For example, the long short-term memory (LSTM) network is able to remember sequential information, but it cannot pay special attention to part of the sequences. In this paper, we present a novel model called long short-term attention (LSTA), which seamlessly integrates the attention mechanism into the inner cell of LSTM. More than processing long short term dependencies, LSTA can focus on important information of the sequences with the attention mechanism. Extensive experiments demonstrate that LSTA outperforms LSTM and related models on the sequence learning tasks.
Refresh When You Wake Up: Proactive Threshold Wallets with Offline Devices.

Proactive security is the notion of defending a distributed system against an attacker who compromises different devices through its lifetime, but no more than a threshold number of them at any given time. The emergence of threshold wallets for more secure cryptocurrency custody warrants an efficient proactivization protocol tailored to this setting. While many proactivization protocols have been devised and studied in the literature, none of them have communication patterns ideal for threshold wallets. In particular a (t, n) threshold wallet is designed to have t parties jointly sign a transaction (of which only one may be honest) whereas even the best current proactivization protocols require at least an additional t−1 honest parties to come online simultaneously to refresh the system.In this work we formulate the notion of refresh with offline devices, where any t ρ parties may proactivize the system at any time and the remaining n−t ρ offline parties can non-interactively catch up at their leisure. However, many subtle issues arise in realizing this pattern. We identify that this problem is divided into two settings: (2, n) and (t, n) where t > 2. We develop novel techniques to address both settings as follows:•We show that the (2, n) setting permits a tight t ρ for refresh. In particular we give a highly efficient t ρ = 2 protocol to upgrade a number of standard (2, n) threshold signature schemes to proactive security with offline refresh. This protocol can augment existing implementations of threshold wallets for immediate use– we show that proactivization does not have to interfere with their native mode of operation. This technique is compatible with Schnorr, EdDSA, and with some effort even sophisticated ECDSA protocols. By implementation we show that proactivizing two different recent (2, n) ECDSA protocols incurs only 14% and 24% computational overhead respectively, less than 200 bytes, and no extra round of communication.•For the general (t, n) setting we prove that it is impossible to construct an offline refresh protocol with t ρ < 2(t−1), i.e. tolerating a dishonest majority of online parties. Our techniques are novel in reasoning about the message complexity of proactive security, and may be of independent interest.Our results are positive for small-scale decentralization (such as 2FA with threshold wallets), and negative for large-scale distributed systems with higher thresholds. We thus initiate the study of proactive security with offline refresh, with a comprehensive treatment of the dishonest majority case.
Coral Reef Cultivation Through Online Donations

The amount of damage to coral reefs caused by human activities has an enormous impact on human life, then with that there must be conservation of reefs to preserve coral reefs in Indonesia, involve the Department of Fisheries and Maritime must play an important role in providing solutions to these problems. The role of the Institute can be done by conducting the cultivation and supervision of coral reefs. The cultivation can be done by making online donations to coral reefs. That way the community will participate in preserving coral reefs. However, facilities for serving online donations are not yet available, then this research is focused on making the system. The system will serve a donation system through the purchase of coral reefs and implanted by a diver agent at a specified location of coral reefs. This donation system was created using a structured approach in analyzing problems that occur in making the system to be built. The existence of this system will facilitate the community to participate and increase public awareness in cultivating coral reefs.
Efficient Abandoned Luggage Detection in Complex Surveillance Videos

Abandoned luggage is seen as a major threat as they may carry explosives and smuggled goods and it affects the lives of normal people. It is necessary to have an automated surveillance system that provides alerts about potential threats in the environment. Abandoned luggage detection suggested in this paper automatically detects abandoned luggage items using camera installed in a particular scene and produces a real time alarm. Image processing techniques are applied on multiple video frames to detect objects, which are recognized as luggage using convolutional neural networks (CNN). If a luggage is detected as unattended for a huge amount of time an alarm sound will be raised.
Improvement of the ungrounded force display using grooved cam

This paper proposes the ungrounded force display using the grooved cam. Using the human illusion, the user can feel the force in the one direction when they use proposed device. It is mainly configured the cam and the slider. The slider follows the cam and moves translational movement. In this paper, we create the new prototype which solving the problems that emerged from the previous prototype. We evaluate the device using the two experiments. One is the subjective evaluation. Second one is the force presentation from the new prototype as the quantitative evaluation. We could solve problems of previous prototype by changing the cam type from plate to groove and blushless motor, and the new one produce the force more clearly than the previous prototype.
Lexicon-Based Text Analysis for Twitter and Quora

Nowadays Social Media is a trending platform for freedom of speech. So, this became a cakewalk to know the opinion of people. It has demonstrated, apart from social media uses, that it plays a crucial role in analyzing the trends in elections on the contrary to the biased predictions belong to the same region, community, class, and religion with the help of sentimental Analysis. Sentimental Analysis refers to identifying and categorizing opinions, especially in terms of positive, negative, neutral. In this paper, we study the trends of Andhra Pradesh Election 2019 using websites like Quora and Twitter by using Lexicon based approach and calculating the polarity score. The experimental results infer that Quora can also be used to obtain the behavior of different political parties.
Formalizing functional analysis structures in dependent type theory

This paper discusses the design of a hierarchy of structures which combine linear algebra with concepts related to limits, like topol-ogy and norms, in dependent type theory. This hierarchy is the backbone of a new library of formalized classical analysis, for the Coq proof assistant. It extends the Mathematical Components library, geared towards algebra, with topics in analysis. This marriage arouses issues of a more general nature, related to the inheritance of poorer structures from richer ones. We present and discuss a solution, coined forgetful inheritance, based on packed classes and unification hints.
Forensic Accounting and Fraud Audit in Turkey (2008–2018): An Academic Literature Review and Classification

Along with accounting scandals in the past, academics, researchers, and legislators have focused on fraud. The purpose of this study is to examine postgraduate and doctoral studies, articles, and books about forensic accounting and fraud audit published between the years 2008 and 2018 in Turkey. For this purpose, a total of 96 studies have been examined and 35 of these are master’s theses, 10 of them are PhD theses, 45 of them are articles, and six of them are books. These studies were presented in tables as classified. The studies examined in our research are summarized as year they were published, the author, and the scope of the topic and in terms of results. The conclusions of this study can be summarized as follows: (a) the majority of thesis published about forensic accounting and fraud audit are in 2011 and following years. In addition, most of the theses are focused on forensic accounting review rather than fraud audit. (b) Results in the articles reviewed are in the same direction with theses. (c) There are very few books about fraud audit and forensic accounting. One of them is related to fraud audit, while the rest of them are related to forensic accounting and forensic accounting profession. We suggest extending the scope of the study and making to other countries.
Toward a General Complexity Theory of Motion Planning: Characterizing Which Gadgets Make Games Hard

We begin a general theory for characterizing the computational complexity of motion planning of robot(s) through a graph of where each gadget has its own state defining a set of allowed traversals which in turn modify the gadget’s state. We study two general families of such gadgets within this theory, one which naturally leads to motion planning problems with polynomially bounded solutions, and another which leads to polynomially unbounded (potentially exponential) solutions. We also study a range of competitive game-theoretic scenarios, from one player controlling one robot to teams of players each controlling their own robot and racing to achieve their team’s goal. Under certain restrictions on these gadgets, we fully characterize the complexity of bounded 1-player motion planning (NL vs. NP-complete), unbounded 1-player motion planning (NL vs. PSPACE-complete), and bounded 2-player motion planning (P vs. PSPACE-complete), and we partially characterize the complexity of unbounded 2-player motion planning (P vs. EXPTIME-complete), bounded 2-team motion planning (P vs. NEXPTIME-complete), and unbounded 2-team motion planning (P vs. undecidable). These results can be seen as an alternative to Constraint Logic (which has already proved useful as a basis for hardness reductions), providing a wide variety of agent-based gadgets, any one of which suffices to prove a problem hard.
LIBOR Prediction Using Genetic Algorithm and Genetic Algorithm Integrated with Recurrent Neural Network

LIBOR (London Inter Banking Offered Rate) market, as one of the most important interest rate market to display the liquidity of the currency flow globally, is a huge market with global stake holders to be involved. The rate is a significant indicator for the liquidity risk of global markets in different currencies and for the stability of the finance markets in main countries. This thesis is to predict the LIBOR in USD with two algorithms: the pure Genetic Algorithm (GA) and the Genetic Algorithm integrated with Recurrent Neural Network (GA + RNN). The pro and cons of these two algorithms will be compared, with fitness values and Mean Squared Error (MSE). 50 test cases will be created and tested randomly to verify the performance of the predictions. In the algorithm of RNN+GA, the target variance between predication and real value is no more than 0.015.
Stochastic L-system inference from multiple string sequence inputs

Lindenmayer systems (L-systems) are a grammar system that consists of string rewriting rules. The rules replace every symbol in a string in parallel with a successor to produce the next string, and this procedure iterates. In a stochastic context-free L-system (S0L-system), every symbol may have one or more rewriting rule, each with an associated probability of selection. Properly constructed rewriting rules have been found to be useful for modeling and simulating some natural and human engineered processes where each derived string describes a step in the simulation. Typically, processes are modeled by experts who meticulously construct the rules based on measurements or domain knowledge of the process. This paper presents an automated approach to finding stochastic L-systems, given a set of string sequences as input. The implemented tool is called the Plant Model Inference Tool for S0L-systems or PMIT-S0L. PMIT-S0L is evaluated using 960 procedurally generated S0L-systems in a test suite, which are each used to generate input strings, and PMIT-S0L is then used to infer the system from only the sequences. The evaluation shows that PMIT-S0L infers S0L-systems with up to 9 rewriting rules each in under 12 hours. Additionally, it is found that 3 sequences of strings are sufficient to find the correct original rewriting rules in $$100\%$$ of the cases in the test suite, and 6 sequences of strings reduce the difference in the associated probabilities to approximately $$1\%$$ or less.
Tutorial Machine Learning for AI-Driven Wireless Networks: Challenges and Opportunities

The goal of this tutorial is to provide one of the first holistic tutorials on the topic of machine learning for wireless network design. In particular, we will first provide a comprehensive treatment of the fundamentals of machine learning and artificial neural networks, which are one of the most important pillars of machine learning. After providing a substantial introduction to the basics of machine learning, we introduce a classification of the various types of neural networks that include feed-forward neural networks, recurrent neural networks, spiking neural networks, and deep neural networks. For each type, we provide an introduction on their basic components, their training processes, and their use cases with specific example neural networks. Then, we overview a broad range of wireless applications that can make use of neural network designs. This range of applications includes spectrum management, multiple radio access technology cellular networks, wireless virtual reality, mobile edge computing and caching, drone-based communications, the Internet of Things, and vehicular networks. For each application, we first outline the main rationale for applying machine learning while pinpointing illustrative scenarios. Then, we overview the challenges and opportunities brought forward by the use of neural networks in the specific wireless application. We complement this overview with a detailed example drawn from the state-of-the-art. Finally, we conclude by shedding light on the potential future works within each specific area and within the overall area of AI for wireless networks.
Complexity-aware loss function for fast neural networks with early exits

Most modern convolutional neural networks (CNNs) are compute-intensive, making them infeasible to use in mobile or embedded devices. One of the approaches to this problem is to modify a usual deep CNN with shallow early-exit branches, appended to some convolutional layers [1]. This modification, named BranchyNet, allows to process simple input samples without performing full volume of calculations, providing a speed-up on average. In this work we consider the problem of training a BranchyNet. We exploit a cascade loss function [2], which explicitly regularizes CNN’s average computation time, and modify it to use the entropy of branches’ prediction as confidence measure. We show, that on CIFAR10 dataset the proposed loss function provides a actual speed-up increase from 43% to 47% without quality degradation, comparing with the original loss function.
Incorporating VR, AR, and Related Technologies in the Tourism Industry

The chapter exposes the importance of tourism in the world economy, gives an overview of what academic and practitioners are doing regarding the use of engagement-facilitating technologies in tourism, and suggests avenues for further research. Authors give insights about the evolution and important of tourism. The chapter presents an overview of the state of the art on the use of engagement-facilitating technologies (mainly virtual and augmented reality) in research. Examples of applications of engagement-facilitating technologies are given. Authors suggest future research directions and present conclusions.