Reviewerly/exHarmony huggingface dataset:
Path planning based on improved multi-objective particle swarm algorithm

This paper proposes an improved multi-objective particle swarm algorithm (MOPSO) to solve the path planning problem of mobile robots. The path planning problem is reduced to the three-objective optimization problem of constraint of path length, path smoothness, and path safety. In this paper, a multiobjective particle swarm optimization algorithm is used, so that multiple targets for path planning can be optimized simultaneously and reasonably, and a single run can provide multiple optimized candidate paths. Aiming at the common problems of multi-objective optimization algorithms, multiple fusion strategies were proposed to improve the effectiveness of the algorithm. Finally, simulation results verify the ability of the improved algorithm to generate high-quality Pareto optimal paths.
Keypoint-based mapping analysis on transformed Side Scan Sonar images

In recent years, detecting wrecks and submerged sites is a challenging task and time-consuming. A fine technique is required to pick out features from the sea-floor. Side Scan Sonar (SSS) has been used for underwater imaging to detect submerged objects on the sea-floor. Due to the rotational and translational instabilities of SSS tow-fish, the acquired SSS images may be distorted. Hence, this paper concentrates on the geometric transformation analysis for different shadows of SSS images. Based on the intensity-based threshold method, the SSS images are classified as high-shadows, low-shadows, no-shadows, and semi-shadows. Different keypoint detector and mapping algorithms are considered for the extraction of keypoints (features) and matching of those detected points. The point mapping performance was evaluated using Repeatability rate and Mapping rate. It is observed that the Mapping rate of 98.7% is obtained using Scale Invariant Feature Transform (SIFT) with Fast Library for Approximate Nearest Neighbor Search (FLANN) and RANdom Sample Consensus (RANSAC). It is also inferred that the Repeatability rate is 0.8 using Oriented FAST & Rotated BRIEF (ORB) compared with other algorithms considered.
Exploiting Object Similarity for Robotic Visual Recognition

We are interested in robotic visual object classification using a deep convolutional neural network (DCNN) classifier. We show that the correlation coefficient of the automatically learned DCNN features of two object images carries robust information on their similarity, and can be utilized to significantly improve the robot's classification accuracy, without additional training. More specifically, we first probabilistically analyze how the feature correlation carries vital similarity information and build a correlation-based Markov random field (CoMRF) for joint object labeling. Given query and motion budgets, we then propose an optimization framework to plan the robot's query and path based on our CoMRF. This gives the robot a new way to optimally decide which object sites to move close to for better sensing and for which objects to ask a remote human for help with classification, which considerably improves the overall classification. We extensively evaluate our proposed approach on two large datasets (e.g., drone imagery and indoor scenes) and several real-world robotic experiments. The results show that our proposed approach significantly outperforms the benchmarks.
A User Training Error based Correction Approach combined with the Synthetic Coordinate Recommender System

We propose a Synthetic Coordinate Recommendation system using a user Training Error based Correction approach (SCoR-UTEC). Synthetic Euclidean coordinates are assigned by SCoR system to users and items, so that, when the system converges, the distance between a user and an item provides an accurate prediction of the user's preference for that item. In this paper, after the SCoR execution, we introduce a stage called UTEC to correct the SCoR recommendations taking into account the error on the training set between users and items and their proximity in the synthetic Euclidean space of SCoR. UTEC is also applicable on any model-based recommender system with positive training error like SCoR. The experimental results demonstrate the efficiency and high performance of the proposed second stage on real world datasets.
Research on English Teaching Transition between Rural Primary Schools and Secondary Schools in Light of Computer Multimedia Technology

Abstract With the development of science and technology, especially the development of computer network and multimedia equipment and technology and popularization, the application of multimedia in rural primary and middle school English class is becoming more and more be taken seriously, it makes full use of images, text, practicality, sound, animation, and other forms convey information to all students, auxiliary classroom teaching and the teaching of cohesion, changed the traditional pattern of rural primary and middle school English teaching, so this article through to the analysis of the rural primary and secondary school English traditional teaching mode, called for English teachers is clever use of multimedia means, optimize English classroom teaching, such as: reasonable use of courseware, stimulate students view hearing; Using multimedia to create language scenes; Make full use of multimedia to improve students’ listening and speaking ability; The method of audio-visual education is used to cultivate students’ ability of independent learning and to optimize their way of thinking. However, there are also the following deficiencies in the application of multimedia: some teachers attach too much importance to multimedia and neglect the conventional teaching methods; their teaching skills are lagging behind; they misuse or abuse multimedia; their courseware contents are too flashy; and students’ interests are transferred. We English teachers should learn to use multimedia correctly and combine with other teaching methods to improve teaching efficiency, which plays a positive role in the cohesion of English teaching in primary and middle schools in rural areas.
A New Semantic Relations-Based Hybrid Approach for Implicit Aspect Identification in Sentiment Analysis

Within the next few years, sentiment analysis or opinion mining is set to become an important component of real-world applications for product manufacturers, e-commerce companies, and potential customers. Sentiment analysis deals with the computational assessment of people’s opinions apparent or hidden within the text according to three levels: document, sentence and aspect levels. The aspect-level is increasingly becoming an active phase of sentiment analysis. At this level, the aim is to determine the hidden target of opinion represented in datasets, known as aspect term identification. This paper proposes an original hybrid model combining semantic relations and frequency-based approach with supervised classifiers for implicit aspect identification (IAI). The proposed approach is directed towards improving the F1-performances for traditional supervised classifiers commonly used in this field based on eager and lazy learning, and deep learning technique using long short-term memory whit attention mechanism applied for IAI. Particularly, this work addresses aspect term extraction and aggregation, the two sub-tasks of IAI, involving adjectives and verbs. The effects of this approach are empirically examined on multiple datasets of electronic products and restaurant reviews with multiple aspect granularity levels. Comparing this method with similar approaches clearly shows the benefits of this method: (i) the use of an appropriately selected WordNet semantic relations of adjectives and verbs that significantly helps classifiers for IAI. (ii) Using the hybrid model helps classifiers better handle these selected WordNet semantic relations and therefore deal better with IAI.
Bridging symbolic and subsymbolic reasoning with minimax entropy models

In this paper, we investigate MiniMax Entropy models, a class of neural symbolic models where symbolic and subsymbolic features are seamlessly integrated. We show how these models recover classical algorithms from both the deep learning and statistical relational learning scenarios. Novel hybrid settings are defined and experimentally explored, showing state-of-the-art performance in collective classification, knowledge base completion and graph (molecular) data generation.
Web Service for Search Engine Bahasa Indonesia (SEBI)

Abstract The purpose of this research is to produce an Indonesian language search engine (SEBI) that is in accordance with the objectives of the 2016 KKNI-based curriculum. SEBI 2017 is one of the complete search engines that is able to collect web pages from the internet, do preprocessing, build indexes, handle query and categorize documents. SEBI 2017, as a prototype has shortcomings, namely time and access. Based on this, the researchers built a new architecture called the SEBI API (2018). In the SEBI API architecture (2018), the ease and breadth of access, as well as the integration of data from various applications and different owners. RESTful API added to SEBI allows SEBI to be accessed from various access devices using various programming languages, libraries or frameworks that support REST API client (consumer) functions. Broadly speaking, the 2018 SEBI architecture has been equipped with RESTful API (provider) and client (consumer) functions. After testing by entering keywords, the results show that the SEBI API architecture (2018) has been able to correct the weaknesses contained in the SEBI architecture (2017)
Hue-Correction Scheme Considering Non-Linear Camera Response for Multi-Exposure Image Fusion

We propose a novel hue-correction scheme for multi-exposure image fusion (MEF). Various MEF methods have so far been studied to generate higher-quality images. However, there are few MEF methods considering hue distortion unlike other fields of image processing, due to a lack of a reference image that has correct hue. In the proposed scheme, we generate an HDR image as a reference for hue correction, from input multi-exposure images. After that, hue distortion in images fused by an MEF method is removed by using hue information of the HDR one, on the basis of the constant-hue plane in the RGB color space. In simulations, the proposed scheme is demonstrated to be effective to correct hue-distortion caused by conventional MEF methods. Experimental results also show that the proposed scheme can generate high-quality images, regardless of exposure conditions of input multi-exposure images.
VDFlow: Joint Learning for Optical Flow and Video Deblurring

Video deblurring is a challenging task as the blur in videos is caused by the combination of camera motion, object moving and depth variation. Recent deep neural networks improve the performance of video deblurring by using the concatenated neighboring frames to estimate the latent images directly. In this paper, we propose a united end-to-end network, called VDFlow, for both optical flow estimation and video deblurring simultaneously. The VDFlow contains two branches where feature representations are bi-directional propagated. The deblurring branch employs an encoder-decoder style network while the optical flow branch is based on the FlowNet network. The optical flow is no longer a tool for alignment but serves as an information carrier of motion trajectories, which helps to restore the latent sharp frames. Extensive experiments demonstrate that the proposed method performs favorably against the state-of-the-art video deblurring approaches on challenging blurry videos and improves the performance of optical flow estimation as well.
A Study of Assessment and Mapping of Carbon Footprints to Campus Activities in Hasanuddin University Faculty of Engineering

Abstract The Hasanuddin University Faculty of Engineering in Gowa is the largest engineering campus in South Sulawesi. The lecture activities are very complex which are not only teaching and learning but also households and offices activities. Therefore, it is important to know the mapping of existing carbon footprint on the campus due to the complexity of the campus activities. The purpose of this study is to determine the level and mapping of the carbon footprint on campus. The method used is to halve emissions into primary and secondary carbon footprint emissions. The results of this study indicate that the greatest emissions is generated from the secondary carbon emissions which is electricity consumption amounting to 1,315,429.63 KgCO 2 /Year, and the lowest emissions is resulted from the use of paper at 49.39 kgCO 2 /Year. For the buildings the highest CO 2 emissions is generated from the Architecture Department building at 289,176.26 KgCO 2 /year and the lowest CO 2 emissions is generated from the Naval B Department building at 20,599.20 KgCO 2 /year. The mitigation scenario in reducing CO 2 emissions effectively is by implementing Earth Hour for 1 hour every month which can reduce electricity consumption by 582,541.78kWh/Year and CO 2 emissions at 164,428.70 KgCO 2 /year. The effective scenario to decrease waste is by implementing 3R with 2% waste reduction which is 167,332.79 Kg/Year and emissions CO 2 are 4,772.41 KgCO 2 /Year.
Measuring the Impacts of Virtualization on the Performance of Thread-Based Applications

The rapid advancements in computer hardware and software facilitated the ability for conventional users and developers to use various virtualization techniques on their common personal computers. For example, users and developers are often using virtualization technologies such as VirtualBox, VMware, Docker, and WSL. These technologies enabled the ability to develop, test, and deploy software across various platforms and computing environments, each of which ensures isolation and security, but may often sacrifice performance due to the extra layer of communication. Thus, this paper presents a performance-based evaluation for thread-based applications that are hosted on different virtualization frameworks. Therefore, it evaluates the effects of virtualization techniques on sequential and multithreaded applications. The performance of the same application is measured on different environments, some of which are provided by a virtual machine that is powered by VirtualBox, while the others are contained in a lightweight virtualization that is provided by the WSL. This paper uses the same application on all environments and presents a performance wise comparison.
Centralized Decentralization: Simple Economics of Governance Attacks on the DPoS Blockchain

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
The La Bellière gold and antimony district (French Armorican Massif): A two-stage evolution model controlled by Variscan strike-slip tectonic

The La Bellière gold district, in the Ligerian domain of the Armorican Massif is the third one of France in term of production. It shows gold-bearing quartz veins neighboring with a complex network of Sb veins, hosted in the Neoproterozoic metasedimentary basement of the Mauges Nappe. New structural data, mineralogical, textural and geochemical assays have been carried out to clarify the gold and Sb veins relationships. Our results favor of two-step hydrothermal event with an early deep-seated gold event followed by a late shallower Sb event. The gold-bearing quartz veins are hosted by N60°E to N90°E sinistral strike-slip faults. These veins exhibit a four-stage paragenesis: 1) early barren quartz filling, 2) arsenopyrite-pyrite with minor scheelite assemblage, 3) gold and base metal sulfides crystallized after a ductile/brittle deformation stage, and 4) pyrite-carbonate coeval with a late brittle stage. These results support a continuous evolution model from a deep emplacement level (12 to 9 km) to a shallow one (7 to <5 km) with a brutal change in the P/T conditions that triggered the deposition of gold (stage 2) controlled by a seismic-valve mechanism. On the contrary, Sb veins correspond to open space vein types, hosted in a N20°W to N20°E conjugated strike-slip fault network arranged in a Riedel shear model, controlled by N130-140°E regional dextral strike-slip shear zone associated to the South Armorican Shear Zone system. A two-stage evolution is recognized, the first one consists of early pyrite-arsenopyrite with microcrystalline quartz, followed by a late stibnite stage formed after geodic quartz deposition. Quartz texture argues for a shallow emplacement (<5 km) of veins controlled by a suction-pump mechanism. Chronologically, the gold vein opening complies with a NE-SW maximum shortening direction different from the late-Variscan stress field responsible for the dextral Armorican shear zone during late Carboniferous, and constrains an indirect dating. Thus, a Visean age is proposed for the formation of gold veins. The overall features of gold deposits are close to those of mesozonal orogenic gold deposits in Europe. Conversely, Sb-bearing veins are generated by a N-S shortening during the late Carboniferous. The Sb event, distinct in time from the earlier gold one, might correspond to a different expression of the so-called “epizonal” type in the orogenic gold model. It is proposed that the Late Carboniferous “Or 300” event is not the unique metallogenic period for gold deposition in the French Variscan domain. Our study highlights the role of the sinistral strike-slip shearing of possible Visean age, in the gold metallogenesis of the Ligerian domain.
Efficacy of Deep Neural Embeddings based Semantic Similarity in Automatic Essay Evaluation

Deep neural embeddings are widely used in natural language processing (NLP) applications like question answering, prediction of next word or sentence, translation of language, word sense disambiguation, and many such applications.Recent methods like Google Sentence Encoder (GSE), Embeddings for Language Models (ELMo), and Global Vectors (GloVe) are also engaged in NLP.Traditional methods such as TF-IDF and Jaccard index are also beneficial in NLP.One of the primary steps performed by these methods is to determine semantic similarity, which is at the core of automatic essay evaluation.In this paper, we have proposed to use semantic similarity for an automatic essay evaluation.We have utilized all these text embedding methods to compute semantic similarity on the dataset of essays provided by the Center for Indian Language Technology (CFILT), IIT Bombay.Our experimental analysis of semantic similarity score distributions shows that the GSE outperforms other methods by accurately distinguishing essays from the same or different sets.Semantic similarity calculated using the GSE method is further used for finding the correlation with human-rated essay scores.Correlation of semantic similarity scores with different essay-specific traits given in the ASAP++ dataset is also performed.
Rule Based Method in Expert System for Detection Pests and Diseases of Corn

Abstract Corn is a multi-functional plant, both for direct consumption and as the main raw material for animal feed and the food industry. The need for corn in Indonesia, which continues to increase, needs to be balanced with an increase in production. So that various government policy strategies are carried out, in an effort to increase productivity. Constraints in the cultivation of corn, namely farmers have limitations in identifying diseases that attack plants, and ways to control pests and diseases. The purpose of this study is to propose the use of technology in agriculture by developing an expert system for the detection of corn pests and diseases, which will provide information and strategies for controlling pests and diseases, in order to reduce losses due to crop failure. The knowledge base in expert system contains a set of rules that use the IF-THEN pattern, and to reason on a rule base using forward cahaining method. Expert system is a tool to detection pests and diseases and how to control them like providing expert assistance. In addition, system also provides explanation facility related to the diagnosis results, according to the symptoms obtained from the user. The accuracy detecting pests and diseases 76.6%.
How Can Randomized Routing Protocols Hide Flow Information in Wireless Networks?

Preventing the source-destination network flow information from being disclosed is pivotal for anonymous wireless network applications. However, the advance of network inference, which is able to obtain the flow information without directly measuring it, poses severe challenges towards this goal. Randomized routing is capable of hiding the flow information by injecting substantial errors to the network inference process. In this paper, we systematically study the behavior of randomized routing protocols, and categorize them into three templates, k -random-relay, k -random-neighbor and k -random-path based on their routing behaviors. We propose technical models to characterize these templates in terms of their induced inference errors and their delay costs. We also use simulations to validate the theoretical results. Our work provides the first systematic study on understanding both the benefit and the cost of using randomized routing to hide the flow information in wireless networks.
Smart Computerized Essay Scoring Using Deep Neural Networks for Universities and Institutions

This chapter develops a web-based automated text scoring (ATS) system that can grade essays and check for spelling errors. The main reason behind this work is to alleviate the labour-intensive marking of essays and ensures equality in scoring for high-stakes exams like TOEFL. The researcher had performed a detailed investigation on deep learning techniques used in the field of ATS and developed a recurrent neural network model that can score essays in an end-to-end approach. Using the developed deep learning model, a web application was also developed to showcase the process of ATS by letting the web application to communicate with the trained model. The model was trained using Keras framework and TensorFlow library and the web application was done using the Flask framework. This work is the LSTM network that can capture sequential dependencies. The evaluation metrics chosen to evaluate the model are the quadratic weighted kappa (QWK) score, and the trained model can achieve 0.6 in QWK score.
Outdoor Robot Navigation System using Game-Based DQN and Augmented Reality

This paper presents a deep reinforcement learning based robot outdoor navigation method using visual information. The deep q network (DQN) maps the visual data to robot action in a goal location reaching task. An advantage of the proposed method is that the implemented DQN is trained in the first-person shooter (FPS) game-based simulated environment provided by ViZDoom. The FPS simulated environment reduces the differences between the training and the real environments resulting in a good performance of trained DQNs. In our implementation a marker-based augmented reality algorithm with a simple object detection method is used to train the DQN. The proposed outdoor navigation system is tested in the simulation and real robot implementation, with no additional training. Experimental results showed that the navigation system trained inside the game-based simulation can guide the real robot in outdoor goal directed navigation tasks.
Two in a Pod

Two in a Pod is a smart toy designed to help children learn how to grow edible plants, getting to know them and sharing their progress with their friends. It is composed of a four-slot wood planter, equipped with soil sensors that interact with a mobile app. Children can plant their seeds, monitor their growth through the app, obtain points for each successfully grown plant and share pictures and their progress, from planting the seed to eating their home-grown vegetables. We designed the toy to help children learn about gardening through gamification and sharing, to increase vegetable consumption and raise awareness about sustainable eating.