Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis Using Naive Bayes Approach with Weighted Reviews - A Case Study

Online reviews are critical in many aspects, for business as well as customers. Yet the accuracy and trustworthiness of these reviews are usually unsubstantiated and little research has been performed to investigate them. In this work, we use a set of Yelp reviews on various topics (food, hotel, etc.) as an example to perform sentiment analysis and investigate the correlation between review comment sentiment and its numeric rating. We use feature selection techniques to statistically remove redundant words from reviews, thus improving run time and accuracy. Our method gives higher weight to those terms/words appearing in reviews with more useful votes. These techniques combined with Naive Bayes approach achieves an overall accuracy of 75%. More interestingly, our method is shown to perform well in 1-star and 5-star reviews, with a 92% accuracy for the latter. With such a strong accuracy, we argue that the proposed sentiment analysis technique can be used to shed light on all online comments, especially those without numerical ratings.
Robust Feature Detection Method in High-density Structured Light System

How to achieve high coding density with a small coding window by using a few coding elements is an interesting and challenging topic in the research domain of structured light techniques (SLT). This paper designed a high-density coding pattern in SLT and a corresponding subpixel feature detection algorithm was proposed. The pattern primitives were designed as checkerboard blocks with a simple embedded geometrical shape - L. Two different types of features are involved to guarantee high coding density. One is checkerboard grid points, which were taken as the main features (MFs) and the other is L shape feature points, which were taken as the auxiliary features (AFs). Specially-designed templates were utilized to locate MFs and a robust feature detecting method based on K-means clustering and Shi-Tomasi algorithm were utilized to locate the AFs accurately. With both MFs and AFs, the density of reconstructed point clouds can be increased by threefold.
Design and evaluation of a biologically-inspired cloud elasticity framework

Abstract The elasticity in cloud is essential to the effective management of computational resources as it enables readjustment at runtime to meet application demands. Over the years, researchers and practitioners have proposed many auto-scaling solutions using versatile techniques ranging from simple if-then-else based rules to sophisticated optimisation, control theory and machine learning based methods. However, despite an extensive range of existing elasticity research, the aim of implementing an efficient scaling technique that satisfies the actual demands is still a challenge to achieve. The existing methods suffer from issues like: (1) the lack of adaptability and static scaling behaviour whilst considering completely fixed approaches; (2) the burden of additional computational overhead, the inability to cope with the sudden changes in the workload behaviour and the preference of adaptability over reliability at runtime whilst considering the fully dynamic approaches; and (3) the lack of considering uncertainty aspects while designing auto-scaling solutions. In this paper, we aim to address these issues using a holistic biologically-inspired feedback switch controller. This method utilises multiple controllers and a switching mechanism, implemented using fuzzy system, that realises the selection of suitable controller at runtime. The fuzzy system also facilitates the design of qualitative elasticity rules. Furthermore, to improve the possibility of avoiding the oscillatory behaviour (a problem commonly associated with switch methodologies), this paper integrates a biologically-inspired computational model of action selection. Lastly, we identify seven different kinds of real workload patterns and utilise them to evaluate the performance of the proposed method against the state-of-the-art approaches. The obtained computational results demonstrate that the proposed method results in achieving better performance without incurring any additional cost in comparison to the state-of-the-art approaches.
A Neural Network Architecture for Information Extraction in Chinese Drug Package Insert

There is a lot of useful information in the medical photocopying materials.The correct extraction and identification of this information are of great significance for the construction of digital medical.In most previous research, researchers have been working on clinical data, and there is little discussion on the extraction of information from Chinese drug package insert.To settle this issue, a neural network model is proposed in this paper.This model uses OCR's post-document as the data source, which can not only correct these data but also classify sentences.It is mainly composed of three layers: the first layer is employed to correct the data using the language model and the seq2seq model, the second layer is defined by convolution neural network (CNN) aiming to enrich the processed sentences, and another layer is used to determine the label of each sentence.The quantitative experimental results verify the feasibility and validity of the proposed model.In addition, the comparing experiments demonstrate that our method outperforms the regular rule-based approaches, which indicated 4%-6% higher in F1 score.
Insulator Contamination Measurement Based on Infrared Thermal and Visible Image Information Fusion

Pollution-induced flashover has a serious threat on the safe and reliable operation of power systems In this paper infrared image methods and visible image methods are utilized comprehensively to find out porcelain Insulator contamination defect in a laboratory This method is better than the traditional visible image and infrared thermal image in the detection of insulator pollution. The surface temperature and color information of insulators are represented by infrared thermal image and visible image respectively. To better preserve the useful information from source images in this paper, a novel image decomposition method based on latent low-rank representation (LatLRR) is developed, which is simple and effective. This modified image fusion method based on the guided image filtering is used, which can effectively extract small-scale texture detail information of the visible image and the large-scale edge information of the infrared image. Firstly, the source images are decomposed into low-rank parts (global structure) and saliency parts (local structure) by LatLRR. Then, the low-rank parts are fused by weighted-average strategy, and the saliency parts are simply fused by sum strategy. Finally, the fused image is obtained by combining the fused low-rank part and the fused saliency part. Experiments demonstrate that the proposed method is better than the traditional multi-scale decomposition method based on image fusion algorithms in both subjective and objective evaluations for insulator contamination measurement and we shall present the results.
Interaction between Microwave and Mesoscopic Circuits in Cavity-circuit Quantum Electrodynamics

Mesoscopic electric circuits can exhibit a large number of effects relevant to quantum mechanics, quantum electrodynamics and quantum statistics. The topic presented here can be identified as an analog of conventional cavity quantum electrodynamics, where the quantum objects are multilevel atoms and quantized optical fields (photons), namely, a theoretical subject called “cavity-circuit quantum electrodynamics”, where the atoms are replaced with quantum mesoscopic circuits, will be developed. We will study the quantum characteristics of the interaction between the quantum mesoscopic circuits and the quantized electromagnetic field at microwave frequencies. These issues include quantum entangled eigenstates of two coupled mesoscopic circuits, time evolution of the circuit energy quanta (governed by the time-dependent Schrödinger equation) and entanglement transfer between external photons and mesoscopic circuit energy quanta. Since there is inevitable electromagnetic interaction such as mutual capacitance and inductance coupling between two neighboring circuits, the quantum effects resulting from the aforementioned quantized circuit coupling would unavoidably affect the relevant processes in quantum computing devices and hence they deserve consideration in some issues of quantum information.
Estimation of Distance to Wall Surface using Omnidirectional Speaker

In this paper, we propose a wall position estimation method for indoor mobile robots having omnidirectional speakers and microphones. In this method, a linear up-chirp signal is emit from the speaker and recorded by the microphone, and an impulse response of the room is measured by calculating a crosscorrelation function between the transmit and receive signals. Furthermore, the distance to the wall surface is estimated by obtaining the delay time of the detected an echo. We implemented the proposed method on an indoor robot and experimented it, suggesting that it can estimate with an accuracy of 0.2 m.
Visualization and Minima Finding of Multidimensional Hypersurface

Analysis of multidimensional function properties is required for industrial applications. The solution of its problems is a challenge in economics, sociology, chemistry, biology, biochemistry, and other sciences. For example, the study of the potential energy surface (PES) of a free molecule is of fundamental importance in structural chemistry because it is necessary to determine the stable conformations of a molecule and the ways of interconversion between them. However, if the PES is a function of more than three rotational coordinates, the costs of its quantum-chemical calculation rapidly increases and the problem of its graphical visualization can be hardly solved for a large number of variables. This work describes how a specially developed multidimensional interpolation procedure can contribute to solve these problems. To visualize a five dimensional (5D) hypersurface, the authors applied a special coordinate system.
Deep Thought

Undergraduate students often struggle to learn optimal logic proof solving strategies in Discrete Math courses, primarily because of the open-ended nature of the domain. Students can, therefore, benefit from personalized tutoring, where they can receive user-adaptive support. Over the past decade, the advancements in the field of intelligent tutoring systems (ITSs) have made it possible to provide personalized tutoring with minimal involvement of a teacher or a human expert. While such tutoring systems have the potential to augment student learning on a large scale, few intelligent tutors are made open source. Deep Thought is a logic tutor where students practice constructing deductive logic proofs. Extensive research has been conducted for 11 years to provide data-driven intelligent tutoring support in Deep Thought. The logic tutor provides adaptive support using data-driven approaches on two levels: problem level, where the tutor decides whether the student should view the next problem as a worked example or they should solve it, and step level, where the tutor decides when an unsolicited partially-worked step should be provided to the student to direct them towards optimal problem-solving strategies. We have found encouraging evidence to support that the intelligent policies in Deep Thought help undergraduate students learn logic. Deep Thought is currently being used in discrete math classes at two universities: North Carolina State University, and the University of North Carolina at Charlotte. Our aim is to make this tutor available to a larger audience so as to contribute to the Computer Science Education community.
Cost Minimization for Geo-Distributed Data Centers with Renewable Resources and Energy Storages

With the rapid development of cloud computing, data centers are increasing in quantity and scale for large Internet service providers to provide global Internet services. The enormous operation cost of data centers attracts lots of researchers' attention and their huge energy consumption have brought great challenges to the sustainable development of green data centers. In this paper, we investigate the workload scheduling and energy management issues of geo- distributed data centers with renewable resources and energy storages. We formulate the minimization problem of operation cost including electricity cost, water consumption and carbon emission as a stochastic optimization problem. For minimizing the long-term operation cost, we adopt the Lyapunov optimization technique to design an online algorithm which achieves a delicate tradeoff between system cost and performance of delay tolerant workloads. Moreover, extensive simulation results show the correctness and effectiveness of proposed online algorithm based on the real-world traces.
Fuzziness in Information Extracted from Tweets’ Hashtags and Keywords

Social media becomes a part of our lives. People use different form of it to express their opinions on variety of ideas, events and facts. Twitter, as an example of such media, is commonly used to post short messages—tweets—related to variety of subjects. The paper proposes on application of fuzzy-based methodologies to process tweets, and to interpret information extracted from those tweets. We state that the obtained knowledge is fully explored and better comprehend when fuzziness is used. In particular, we analyze hashtags and keywords to extract useful knowledge. We look at the popularity of hashtags and changes of their popularity over time. Further, we process hashtags and keywords to build fuzzy signatures representing concepts associated with tweets.
Red Alert: Break-Glass Protocol to Access Encrypted Medical Records in the Cloud

Availability of medical records during an emergency situation is of paramount importance since it allows healthcare professionals to access patient's data on time and properly plan the next steps that need to be taken. Cloud storage has the potential to provide a solution to the problem of data unavailability during an emergency situation. However, sharing medical records raises several concerns about security and privacy. In this paper, we study the problem of how to share encrypted patients' data during an emergency situation. To this end, we propose a protocol through which a team of healthcare professionals can securely decrypt the medical records of a patient who is under an emergency situation (e.g. acute stroke). Furthermore, our protocol ensures that a team of healthcare professionals will only have access to the patient's data for the time needed to complete a specific process related to the patient's situation (e.g. transfer patient to the hospital). In our study, the dynamically granting and revoking data access during an emergency treatment is the main novelty.
Non-dominated Sorting Genetic Algorithm III for Multi-objective Optimal Reactive Power Dispatch Problem in Electrical Power System

The non-dominated sorting genetic algorithm (NSGA-III) was introduced to solve multi-objective optimal reactive power dispatch (MORPD) problems. MORPD as a non-linear, multi-objective optimization problem has the characteristics of non-convex, multi-constraint, and multi-variable (mix of discrete and continuous variables). The aim is to minimize the real power losses and voltage deviations. The feasibility of the proposed method was tested on the IEEE 57-bus power systems. The comparison of simulation results with the previous studies which applied the mixed variables of continuous and discrete showed that the proposed optimization method is more efficient and reliable in minimize the real power losses and computing period compared to multi-objective enhanced particle swarm optimization (MOEPSO), multi-objective particle swarm optimization (MOPSO) and multi-objective ant lion optimization (MOALO).
SecReS: A Secure and Reliable Storage Scheme for Cloud with Client-Side Data Deduplication

In this paper, we propose a cloud storage scheme which simultaneously achieves security, reliability, and deduplication. Our scheme blends convergent encryption and perfect secret sharing method to achieve confidentiality of data and encryption keys. It also achieves fault tolerance by dispersing data to multiple storage servers using Reed-Solomon erasure code. We use Merkle hash tree to authenticate users by verifying Proof of Ownership (PoW) of data for carrying out secure deduplication. Both theoretical security analysis and experimental evaluation demonstrate that our scheme is provably secure and incurs only a small overhead compared to the existing systems.
SMLBoost-adopting a soft-margin like strategy in boosting

Boosting is well known for its excellence on regular datasets, nevertheless, boosting can also easily overfit the wrong patterns when the training set contains noise. This paper aims at tackling boosting’s noise sensitivity via simulating a soft-margin like procedure. In contrast to the previous studies, which build soft-margin via introducing some regularization terms into their loss functions, this paper aims to build a clean marginal (border) region near the classification hyperplane, thereby, the principle of margin can be rebuilt for boosting in the noisy scenario. The key ingredients of the proposed method consist in a noise detection strategy and a weight updating mechanism, which progressively assigns higher weight to the un-noisy instances that have been pushed into the marginal region defined by an unsupervised margin. We conducted experiments on 20 UCI datasets and 44 imbalanced datasets from KEEL. Experimental results not only demonstrate the superiority of the proposed method over other robust boostings on noisy datasets, they also confirm when combined with oversampling technique that might erroneously generate noisy samples to balance the class distribution, the proposed method can deliver promising imbalanced learning ability.
Robust Waypoints navigation using Fuzzy Type 2 Controller

In this paper, a robust waypoints navigation algorithm is proposed using the type 2 fuzzy logic controller. The proposed solution is compared to classical P-feedback controller and the type 1 fuzzy controller using a unicycle mobile robot the Pioneer 3AT. Many scenarios are performed, moving to a goal, moving to multiple waypoints with and without parameters uncertainties. The obtained results show clearly the performances of the type 2 fuzzy logic controller, comparing to the P-feedback and fuzzy type 1 controllers especially when significant uncertainties are considered.
Practical non-monotonic knowledge-base system for un-regimented domains: A Case-study in digital humanities

Information systems for un-regimented domains such as museums, art and book collections, face representational and usability challenges that surpass the demands of traditional information systems for regimented domains. While the former require complex conceptual models supporting a set of dynamic and evolving qualitative properties of a small number of objects, the latter focus on the quantitative aspects of a possibly very large number of objects but with a relatively small and stable set of properties. In this paper we study the use of a non-monotonic knowledge-base system for the development of information systems for un-regimented domains. We discuss the ontological assumptions of the formalism, its structure and its inferential mechanisms through a simple example. Then we present an information system for a highly un-regimented domain in the digital humanities with promising results. The present study shows that the so-called extensible, flexible, dynamic or evolving information systems need the expressive power of non-monotonic knowledge-base systems, and that such phenomena should be addressed explicitly.
Avoiding Collisions when Interacting with Levitating Particle Displays

Levitating particle displays are an emerging technology where content is composed of physical pixels. Unlike digital displays, manipulating the content is not straightforward because physical constraints affect the placement and movement of each particle: dragging a particle may cause it to collide with others along its movement path. We describe initial work into four new interaction techniques that allow users to avoid collisions when directly manipulating display content. Techniques such as these are required for interactive levitating displays to be practical when scaled up to large sizes.
Comparison and Analysis of the DVG+A* and Rapidly-Exploring Random Trees Path-Planners for the RoboCup-Small Size League

This paper provides an experimental analysis between Dynamic Visibility Graph A Star (DVG+A*) and Rapidly-exploring Random Trees (RRT) path-planners, in order to compare which one is more adequate to the scenario presented in the Small Size League (SSL). The metrics used to compare each algorithm were established based on the characteristics of a SSL game, which demand a short path, low computational cost and a safe distance from the opponent robots. For the comparison, both algorithms were tested in static and dynamic environments. After all the tests, DVG+A* has shown the best results.
Classifying material implications over minimal logic

The so-called paradoxes of material implication have motivated the development of many non-classical logics over the years, such as relevance logics, paraconsistent logics, fuzzy logics and so on. In this note, we investigate some of these paradoxes and classify them, over minimal logic. We provide proofs of equivalence and semantic models separating the paradoxes where appropriate. A number of equivalent groups arise, all of which collapse with unrestricted use of double negation elimination. Interestingly, the principle ex falso quodlibet, and several weaker principles, turn out to be distinguishable, giving perhaps supporting motivation for adopting minimal logic as the ambient logic for reasoning in the possible presence of inconsistency.
The Research on Flow Table Optimization Based on Dynamic Timeout Mechanism

SDN(Software Defined Networking) is a new network architecture, which decouples the control plane from data plane and operates the globle network with elaborate abstraction. The control plane advocates a centralized approach of network control, which provides both flexibility and high efficiency. However, there are lots of problems in this architecture and the bottleneck problem is the network's performance. Existing OpenFlow based flow expiry mechanisms relys on a fixed timeout after which the switch proactively removes the flow entries from its flow table which may causes unnecessary flow table occupation. Therefore, in this paper, a strategy of dynamically adjusting timeout time is proposed and the program is implemented. It can dynamically adjust the timeout time according to the actual situation of equipment and data flow, and can better adapt to the network with different data flows. The experiment shows that the strategy can improve the network's performance and the mechanism designed in this paper is proved to be effective.