Reviewerly/exHarmony huggingface dataset:
Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for optimizing PID parameters on Autonomous Underwater Vehicle (AUV) control system

Indonesia consists of seventy percents of sea such that Indonesia has much marine resource. For exploring marine resource, it is required Autonomous Underwater Vehicle (AUV) with its control. In AUV, there are surge, sway, heave position and roll, pitch, yaw angle which have to be controlled. PID (Proportional-Integral-Derivative) control has been developed in many control system problems. In previous research, the tuning of PID parameters such as Kp, Ki, and Kd has been applied by Ziegler-Nichols technique. In this research, the optimization of PID parameters will be approached by heuristic methods such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). PSO is inspired by the flock of birds or fishes in food search while ACO is inspired by the cooperative behavior of ant colonies, to find the shortest path from their nest to the food source. Either particle in PSO or path consisting pheromone in ACO represents PID parameters and the fitness function is integral of absolute error (IAE). Based on simulations, heuristic methods can result responses with small overshoot and fast rise time and settling time.
Automated Sentiment Analysis of Text Data with NLTK

Abstract At present, most of researches in natural language processing primarily are focused on deep learning way to solve accuracy problems. This paper discusses a branch of natural language processing, sentiment analysis. In order to implement the function of sentiment analysis efficiently, we built an algorithm by calling the NLTK library firstly. As a result, this method can roughly obtain the emotional scores of different sentences and compare them with the scores given by human being who create these sentences. The results are presented by correlation calculation and images such as boxplot. The analysis of the images demonstrates the deficiency of the method. In addition, for the results, we found the direction of learning to solve the accuracy problems and made reasonable arrangements and planning.
Joint Cloud Collaboration Mechanism between Vehicle Clouds Based on Blockchain

With the development of the Internet of Vehicles, more and more applications require collaboration between vehicles. Vehicles from different car manufacturers have their own private clouds, and the collaboration between them is poor, resulting in inefficient collaboration between heterogeneous vehicles. A single cloud lacks collaboration between vehicles and it's an inevitable trend for developing collaboration between clouds. JointCloud is a new computing model in which collaboration services provided between clouds are used to server users in a software-defined manner based on blockchain. This paper proposes the collaboration framework of multiple vehicle clouds, introduces the blockchain to establish the coordination mechanism, and describes standardization method and service combination method of the vehicular cloud service in detail. At the end of this paper, we designed a distributed cloud service evaluation method based on blockchain to provide users with an effective cloud service evaluation solution.
Security Verification via Automatic Hardware-Aware Exploit Synthesis: The CheckMate Approach

Many hardware security exploits result from the combination of well-known attack classes with newly exploited hardware features. CheckMate is an approach and automated tool for evaluating microarchitectural susceptibility to specified attack classes, and for synthesizing proof-of-concept exploit code for susceptible designs.
Cost-Effective Path Planning for Submarine Cable Network Extension

We describe a cost-effective approach to path planning for submarine cables connecting a given site to an existing cable network on the Earth's surface. The objective is to minimize the overall life-cycle cost of submarine cables by considering multiple design considerations. With the surface terrain represented by a triangulated manifold, we formulate the problem as one of the variational optimization framed in terms of an Eikonal equation and solved by leveraging the fast marching method. The results based on real-world three-dimensional data are presented to illustrate the performance of this approach in the submarine cable network extension.
The Product Owner in Large-Scale Agile: An Empirical Study Through the Lens of Relational Coordination Theory

Abstract In agile software development, a core responsibility of the product owner (PO) is to communicate business needs to the development team. In large-scale agile software development projects, many teams work toward an overall outcome, but they also need to manage interdependencies and coordinate efficiently. In such settings, POs need to coordinate knowledge about project status and goal attainment both within and across the development teams. Previous research has shown that the PO assumes a wide set of roles. Still, our knowledge about how POs coordinate amongst themselves and with their teams in large-scale agile is limited. In this case study, we explore PO coordination in a large-scale development program through the theoretical lens of Relational Coordination Theory. Our findings suggest that (1) coordination varies depending on the context of each PO, (2) a focus on achieving high-quality communication changes coordination over time, and (3) unscheduled coordination enables of high-quality communication.
Depth Embedded Recurrent Predictive Parsing Network for Video Scenes

Semantic segmentation-based scene parsing plays an important role in automatic driving and autonomous navigation. However, most of the previous models only consider static images, and fail to parse sequential images because they do not take the spatial-temporal continuity between consecutive frames in a video into account. In this paper, we propose a depth embedded recurrent predictive parsing network (RPPNet), which analyzes preceding consecutive stereo pairs for parsing result. In this way, RPPNet effectively learns the dynamic information from historical stereo pairs, so as to correctly predict the representations of the next frame. The other contribution of this paper is to systematically study the video scene parsing (VSP) task, in which we use the RPPNet to facilitate conventional image paring features by adding spatial-temporal information. The experimental results show that our proposed method RPPNet can achieve fine predictive parsing results on cityscapes and the predictive features of RPPNet can significantly improve conventional image parsing networks in VSP task.
A New Deep Segmentation Quality Assessment Network for Refining Bounding Box Based Segmentation

Capturing quality cues in blind segmentation quality assessment (SQA) is a challenging task. This paper proposes a new blind SQA method that captures a variety of quality cues using two considerations: one is to consider different types of data, such as segmentation mask and original image patch and the other is to consider different types of data operations, such as the max pooling operation, the average pooling operation, and the convolution operation. An end-to-end segmentation quality assessment network is proposed to capture these different types of quality cues by the above-mentioned two considerations. Then, since the segmentation result by the traditional bounding box-based segmentation method using fixed parameter setting is usually inaccurate, we use the proposed blind SQA network to refine the traditional bounding box-based segmentation results, with the idea of selecting parameters that are adaptive to each image. A two-step refinement method is proposed. The first step generates multiple segmentation results by various parameter settings. The second step, then, uses the segmentation quality assessment to select the best quality segmentation result as the new result. To train and verify the proposed method, a new dataset constructed by using the PASCAL VOC dataset and the bounding box-based segmentation methods, such as Grabcut and improved FCN method, is constructed. The proposed method is finally verified on the constructed dataset and two tasks: the segmentation quality assessment and the bounding box-based segmentation. The experimental results demonstrate the effectiveness of the proposed method.
The Role of Negative Information in Distributional Semantic Learning

Distributional models of semantics learn word meanings from contextual co-occurrence patterns across a large sample of natural language. Early models, such as LSA and HAL (Landauer & Dumais, 1997; Lund & Burgess, 1996), counted co-occurrence events; later models, such as BEAGLE (Jones & Mewhort, 2007), replaced counting co-occurrences with vector accumulation. All of these models learned from positive information only: Words that occur together within a context become related to each other. A recent class of distributional models, referred to as neural embedding models, are based on a prediction process embedded in the functioning of a neural network: Such models predict words that should surround a target word in a given context (e.g., word2vec; Mikolov, Sutskever, Chen, Corrado, & Dean, 2013). An error signal derived from the prediction is used to update each word's representation via backpropagation. However, another key difference in predictive models is their use of negative information in addition to positive information to develop a semantic representation. The models use negative examples to predict words that should not surround a word in a given context. As before, an error signal derived from the prediction prompts an update of the word's representation, a procedure referred to as negative sampling. Standard uses of word2vec recommend a greater or equal ratio of negative to positive sampling. The use of negative information in developing a representation of semantic information is often thought to be intimately associated with word2vec's prediction process. We assess the role of negative information in developing a semantic representation and show that its power does not reflect the use of a prediction mechanism. Finally, we show how negative information can be efficiently integrated into classic count-based semantic models using parameter-free analytical transformations.
Deep long short-term memory networks-based automatic recognition of six different digital modulation types under varying noise conditions

In this paper, a new method based on deep learning has been proposed in order to recognize noise-digital modulation signals at varying noise levels automatically. The 8-bit data from six different modulations have been obtained by adding noise levels from 5 to 25 dB. The used digital modulation types are Amplitude Shift Keying, Frequency Shift Keying, Phase Shift Keying, Quadrature Amplitude Shift Keying, Quadrature Frequency Shift Keying, and Quadrature Phase Shift Keying. To recognize the noise-digital modulation signals automatically, a new deep long short-term memory networks (LSTMs) model has been proposed and then applied to these signals successfully. A significant advantage of the proposed system is that deep learning method has been trained and tested with raw digital modulation signals without applying any feature extraction from the signals. In this study, the noise modulation signals of 5–25 dB have been classified and compared with each other. The innovative aspect of the study is to classify the modulation with the LSTM method without dealing with the extraction of signal characteristics. Without noise, added digital modulation signals had been classified as the success rate of 97.22%, while with all noise-added signals have been classified as the success rate of 94.72% with deep LSTM model. The experimental results show that the proposed deep LSTM model has been achieved remarkable results in recognition of noised six different modulation signals with a fully end-to-end structure.
A Lightweight Fine-Grained Searchable Encryption Scheme in Fog-Based Healthcare IoT Networks

For a smart healthcare system, a cloud based paradigm with numerous user terminals is to support and improve more reliable, convenient, and intelligent services. Considering the resource limitation of terminals and communication overhead in cloud paradigm, we propose a hybrid IoT-Fog-Cloud framework. In this framework, we deploy a geo-distributed fog layer at the edge of networks. The fogs can provide the local storage, sufficient processing power, and appropriate network functions. For the fog-based healthcare system, data confidentiality, access control, and secure searching over ciphertext are the key issues in sensitive data. Furthermore, how to adjust the storage and computing requirements to meet the limited resource is also a great challenge for data management. To address these, we design a lightweight keyword searchable encryption scheme with fine-grained access control for our proposed healthcare related IoT-Fog-Cloud framework. Through our design, the users can achieve a fast and efficient service by delegating a majority part of the workloads and storage requirements to fogs and the cloud without extra privacy leakage. We prove our scheme satisfies the security requirements and demonstrate the excellent efficiency through experimental evaluation.
Classification of texture based on Bag-of-Visual-Words through complex networks

Abstract Over the last years complex data (e.g. images) have been growing in a very fast pace. This demands the ability to describe and to categorize them. To solve this problem it is essential to develop efficient and effective vision-based expert techniques. Hence, the cornerstone of our work is to propose a new methodology, called BoVW-CN, that combines Bag-of-Visual-Words and complex networks for describing keypoints detected in a given image. Our insight is that describing just the relevant points of an image we can achieve a more cost-effective and better image description. The obtained results testify that BoVW-CN, applied to public image datasets, outperforms the widely used state-of-the-art methods. We not only obtained good accuracies (e.g. 78.18%), but also performed analyses to find the best trade-off between computational cost and accuracy. Besides, to the best of our knowledge, our work is the first one to propose such integration of Bag-of-Visual-Words and complex networks through a texture-based focus.
An outline of some future research issues for internet pragmatics

Abstract Internet communication has evolved a lot since it first became popular in the early nineties of the last century. Pragmatics has also evolved and has tried to come to terms with the non-stop changes that internet is constantly producing in our lives and especially in how we communicate and interact. We are probably now at a stage of internet development in which we can make some sound predictions regarding certain challenges that a pragmatics of internet communication will have to face in the next few years to deal with the radical changes that are taking place in today’s internet use. This article will be devoted to listing some of these research issues and to discussing what pragmatics can do to address them accurately, ranging from those issues centred upon the interpretation of online discourses to those involving interfaces and their options for contextualisation.
Solving Traveling Salesman Problem using Firefly algorithm and K-means Clustering

Traveling Salesman Problem (TSP) is a significant combinatorial optimization problem. TSP is NP-hard problem which involves finding the minimum tour length among a given set of nodes and return to the starting node knowing that each node must be visited once. This paper proposes a solution to TSP using Firefly Algorithm (FA) and k-means clustering. The proposed approach comprises three major steps: cluster the nodes, find the optimal path in each cluster, and reconnect the clusters. The first step uses k-means clustering to divide the nodes into sub-problems, the second step utilizes FA to find the optimal path in each cluster, finally reconnect all clusters and return the path between them. The experimental results show that the proposed approach provides better results when compared to other algorithms from the literature.
Filtering Instagram Hashtags Through Crowdtagging and the HITS Algorithm

Instagram is a rich source for mining descriptive tags for images and multimedia in general. The tags-image pairs can be used to train automatic image annotation (AIA) systems in accordance with the learning by example paradigm. In previous studies, we had concluded that, on average, 20% of the Instagram hashtags are related to the actual visual content of the image they accompany, i.e., they are descriptive hashtags, while there are many irrelevant hashtags, i.e., stop-hashtags, that are used across totally different images just for gathering clicks and for searchability enhancement. In this paper, we present a novel methodology, based on the principles of collective intelligence that helps in locating those hashtags. In particular, we show that the application of a modified version of the well-known hyperlink-induced topic search (HITS) algorithm, in a crowdtagging context, provides an effective and consistent way for finding pairs of Instagram images and hashtags, which lead to representative and noise-free training sets for content-based image retrieval. As a proof of concept, we used the crowdsourcing platform Figure-eight to allow collective intelligence to be gathered in the form of tag selection (crowdtagging) for Instagram hashtags. The crowdtagging data of Figure-eight are used to form bipartite graphs in which the first type of nodes corresponds to the annotators and the second type to the hashtags they selected. The HITS algorithm is first used to rank the annotators in terms of their effectiveness in the crowdtagging task and then to identify the right hashtags per image.
Implementation of Augmented Reality into the Training and Educational Process in Order to Support Spatial Perception in Technical Documentation

At present, while the development of technology used to display, create, and manipulate 3D models is already very advanced, we are still confronted with situations where it is necessary to use technical documentation. Whatever the production or educational process, this part of the documentation will still be necessary. With this fact, we are increasingly forced to think about how it would facilitate the process of reading and learning with this kind of media. Due to the often inadequate technical level of the interested persons, we come in this article with a way of possible implementation of the augmented reality into the technical documentation, to facilitate its reading and to improve the spatial perception of the included objects.
Trust Beyond Computation Alone: Human Aspects of Trust in Blockchain Technologies

Blockchains - with their inherent properties of transaction transparency, distributed consensus, immutability and cryptographic verifiability - are increasingly seen as a means to underpin innovative products and services in a range of sectors from finance through to energy and healthcare. Discussions, too often, make assertions that the trustless nature of blockchain technologies enables and actively promotes their suitability - there being no need to trust third parties or centralised control. Yet humans need to be able to trust systems, and others with whom the system enables transactions. In this paper, we highlight that understanding this need for trust is critical for the development of blockchain-based systems. Through an online study with 125 users of the most well-known of blockchain based systems - the cryptocurrency Bitcoin - we uncover that human and institutional aspects of trust are pervasive. Our analysis highlights that, when designing future blockchain-based technologies, we ought to not only consider computational trust but also the wider eco-system, how trust plays a part in users engaging/disengaging with such eco-systems and where design choices impact upon trust. From this, we distill a set of guidelines for software engineers developing blockchain-based systems for societal applications.
Personalized Web Service Recommendation Based on QoS Prediction and Hierarchical Tensor Decomposition

Web service recommendation based on the quality of service (QoS) is important for users to find the exact Web service among many functionally similar Web services. Although service recommendations have been recently studied, the performance of the existing ones is unsatisfactory because: 1) the current QoS predicting algorithms still experience data sparsity and cannot predict the QoS values accurately and 2) the previous approaches fail to consider the QoS variance according to the users and services' locations carefully. A Web service recommendation method based on the QoS prediction and hierarchical tensor decomposition is proposed in this paper. The method is called QoSHTD that is based on location clustering and hierarchical tensor decomposition. First, the users and services of the QoSHTD cluster into several local groups based on their location and models local and global triadic tensors for the user-service-time relationship. The hierarchical tensor decomposition is then performed on the local and global triadic tensors. Finally, the predicted QoS value through local and global tensor decomposition is combined as the missing QoS values. The comprehensive experiment shows that the proposed method achieves a high prediction accuracy and recommending quality of Web service, and can partially address data sparsity.
EATSDCD: A green energy-aware scheduling algorithm for parallel task-based application using clustering, duplication and DVFS technique in cloud datacenters

Energy consumption and performance metrics have become critical issues for scheduling parallel task-based applications in high-performance computing systems such as cloud datacenters. The duplication and clustering strategy, as well as Dynamic Voltage Frequency Scaling (DVFS) technique, have separ ately been concentrated on reducing energy consumption and optimizing performance parameters such as throughput and makespan. In this paper, a dual-phase algorithm called EATSDCD which is an energy efficient time aware has been proposed. The algorithm uses the combination of duplication and clustering strategies to schedule the precedence-constrained task graph on datacenter processors through DVFS. The first phase focuses on a smart combination of duplication and clustering strategy to reduce makespan and energy consumed by processors in an effort to execute Directed Acyclic Graph (DAG) while satisfying the throughput constraint. The main idea behind EATSDCD intended to minimize energy consumption in the second phase. After determining the critical path and specifying a set of dependent tasks in non-critical paths, the slack time for each task in non-critical paths was distributed among all dependent tasks in that path. Then, the frequency of DVFS-enabled processors is scaled down to execute non-critical tasks as well as idle and communication phases, without extending the execution time of tasks. Finally, a testbed is developed and different parameters are tested on the randomly generated DAG to evaluate and illustrate the effectiveness of EATSDCD. It was also compared against duplication and clustering-based algorithms and DVFS-based algorithms. In terms of energy consumption and makespan, the results show that our proposed algorithm can save up to 8.3% and 20% energy compared against Power Aware List-based Scheduling (PALS) and Power Aware Task Clustering (PATC) algorithms, respectively. Furthermore, there is 16% improvement over Parallel Pipeline Latency Optimization (PaPilo) algorithm with Encur = 1.2Enmin (G). In comparison with Reliability Aware Scheduling with Duplication (RASD) algorithm, the execution time has been reduced in heterogeneous environments.