Reviewerly/exHarmony huggingface dataset:
Potential Trend for Online Shopping Data Based on the Linear Regression and Sentiment Analysis

How to reduce the cost of competition in the industry, identify effective customers, and understand the emotional needs and consumer preferences of customers, so as to carry out fast and accurate commercial marketing, is an important research topic. In this paper, we discussed the method for the analysis of three product data which represent the customer-supplied ratings and reviews for microwave ovens, baby pacifiers, and hair dryers sold in the Amazon marketplace over the time period. The sentiment analysis, linear regression analysis, and descriptive statistics were implemented to analyze the three datasets. Based on the sentiment analysis given by the naive Bayesian classification algorithm, we found that the star rating is positively correlated with the reviews, while the helpfulness ratings have no specific relationship with the star rating and reviews. We use multiple regression analysis and clustering algorithm analysis to get the relationship between the 4 indexes such as time, star rating, reviews, and helpfulness rating. We find that there is a positive correlation between the 4 indexes, and the reputation of the product online market is improving as time grows. Based on the analysis of the positive reviews and star ratings, we suggested indicating a potentially successful or failing product by the positive reviews. We also discussed the relations between the star ratings and number of reviews. Finally, we selected the words from the Amazon sentiment dictionary as candidate words. By counting the candidate words’ appearance in the review, the keywords that can reflect the star rating were found.
Blockchain Architecture for Secured Inter-healthcare Electronic Health Records Exchange

In this on-going research, we propose a blockchain-based solution that facilitates a scalable and secured inter-healthcare EHRs exchange. These healthcare systems maintain their records on separate blockchain networks and are independent of each other. The proposed architecture can detect and prevent malicious activities on both stored and shared EHRs from either outsider or insider threats. It can also verify the integrity and consistency of EHR requests and replies from other healthcare systems and presents them in a standard format that can be easily understood by different healthcare nodes. In the preliminary result, we evaluate the security analysis against frequently encounter outsider and insider threats within a healthcare system. The result shows that the architecture detects and prevents outsider threats from uploading compromising EHRs into the blockchain and also prevents unauthorized retrieval of patient’s information.
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Keyphrase Extraction by Improving TextRank with an Integration of Word Embedding and Syntactic Information

Background: As a known key phrase extraction algorithm, TextRank is an analogue of PageRank algorithm, which relied heavily on the statistics of term frequency in the manner of co-occurrence analysis. Objective: The frequency-based characteristic made it a neck-bottle for performance enhancement, and various improved TextRank algorithms were proposed in the recent years. Most of improvements incorporated semantic information into key phrase extraction algorithm and achieved improvement. Method: In this research, taking both syntactic and semantic information into consideration, we integrated syntactic tree algorithm and word embedding and put forward an algorithm of Word Embedding and Syntactic Information Algorithm (WESIA), which improved the accuracy of the TextRank algorithm. Results: By applying our method on a self-made test set and a public test set, the result implied that the proposed unsupervised key phrase extraction algorithm outperformed the other algorithms to some extent.
Enabling Differentially Private in Big Data Machine Learning

Using the machine learning technology to explore the potential value of Big Data brings us into a smarter world, and the way data is mined through data sharing patterns also threatens the privacy of personal data. Differential privacy is a prevalent mechanism to effectively protect the personal data privacy due to the strict and the provable privacy definition, although there are several achievements have reached by combining the differential privacy and traditional machine learning algorithms in a stand-alone mode, little to talk about the distributed environment. To fill this gap, this paper proposes a method to embed the differential privacy mechanism into distributed platform, respectively implements the DPLloyd, GUPT k-means and GUPT logistic regression on the platform of Spark. The evaluation demonstrates that the approach barely interferes the effect of distributed machine learning algorithms and thus achieves the goal of differential privacy.
Chatbot Analytics Based on Question Answering System and Deep Learning: Case Study for Movie Smart Automatic Answering

Question Answer (QA) systems are established to retrieves accurate and concise answers to human queries posted in natural language.The primary focus of the QA system is to achieve efficient and natural interaction between machines and humans.To achieve the above several researchers are directed towards Natural Language Processing (NLP) based deep learning.With the rise of a variety of deep NLP models, it is now possible to obtain a vector form of words and sentences that stores the meaning of the context.NLP considerably aids deep learning-based mathematical models in understanding the semantic and syntax of natural human language.The Cornell Movie-Dialogs Corpus created at Cornell University, and Movie Dialog Dataset created at Facebook are preprocessed and used to train the chatbot.Deep learning model has been built to answer questions about movies from Moview reviews.The encoder and decoder of the Seq2Seq model comprise of LSTM cells and are defined using Bidirectional Dynamic RNN and Dynamic Decoder RNN package of the tensor flow library.Additionally, to ensure the chatbot performs well on long sentences attention mechanism from the tensor flow library is applied to the decoder.In this paper, research is conducted on build a smart chatbots based QA system that employs a deep learning model.The deep learning model employs a sequence-to-sequence (Seq2Seq) word embedding that was proposed by Ilya Sutskever in 2014, which had laid the foundation for building chatbot model build in this paper.
Variation of Quantum Speed Limit with Correlated Channels

There is a bound on the speed of evolution of quantum system. Out of many bounds few bounds have been studied. In this article we study the effect of quantum speed limit in correlated noisy channel. Our model serves as a platform for a detail study of the effect of quantum speed limit on correlated noise channel. It has found that how the quantum speed limit varies by increasing the correlation between consecutive uses of noisy channels.
A First-Order Logic of Limited Belief Based on Possible Worlds

In a recent paper Lakemeyer and Levesque proposed a first-order logic of limited belief to characterize the beliefs of a knowledge base (\KB). Among other things, they show that their model of belief is expressive, eventually complete, and tractable. This means, roughly, that a \KB\ may consist of arbitrary first-order sentences, that any sentence which is logically entailed by the \KB\ is eventually believed, given enough reasoning effort, and that reasoning is tractable under reasonable assumptions. One downside of the proposal is that epistemic states are defined in terms of sets of clauses, possibly containing variables, giving the logic a distinct syntactic flavour compared to the more traditional possible-world semantics found in the literature on epistemic logic. In this paper we show that the same properties as above can be obtained by defining epistemic states as sets of three-valued possible worlds. This way we are able to shed new light on those properties by recasting them using the more familiar notion of truth over possible worlds.
Inputs, Outputs, and Composition in the Logic of Information Flows

The logic of information flows (LIF) is a general framework in which tasks of a procedural nature can be modeled in a declarative, logic-based fashion. The first contribution of this paper is to propose semantic and syntactic definitions of inputs and outputs of LIF expressions. We study how the two relate and show that our syntactic definition is optimal in a sense that is made precise. The second contribution of this paper is a systematic study of the expressive power of sequential composition in LIF. Our results on composition tie in the results on inputs and outputs, and relate LIF to first-order logic (FO) and bounded-variable LIF to bounded-variable FO.
THE ELEMENTS OF IDEATIONAL GRAMMATICAL METAPHOR IN READING TEXTS FOR SENIOR HIGH SCHOOL ENGLISH TEXTBOOKS

This study investigated ideational grammatical metphor in Senior High School English Textbook. The objectives of the study were to identify what ideational grammatical metaphors were used in Senior High School English Textbooks. The research was conducted by applying descriptive qualitative design. The data for this study were texts which were taken from Texts in Senior High School English Textbooks. The data were collected by selecting all the texts in the textbooks, then retyping the passage of the texts, editing the passage by breaking down the clauses into a data, and finally printing out the data. After the data had been collected, they were analyzed by some procedures; identifying the ideational grammatical metaphor in the data, rewording or unpacking the data into a congruent form, making comparisons between the two forms, classifying the classes of ideational grammatical metaphor and its function and analyzing the reason the ideational grammatical metaphor was used in Senior High School English Textbooks. The findings of the study showed that there were four ideational grammatical metaphor used in Senior High School English Textbook, namely the data of epithet = thing, event = thing, event = epithet and head = modifier.Keywords: Ideational Grammatical Metaphor, Senior High School English Textbooks,
Sedimentological Study of Dokan Conglomerate in Kalakan Anicline / NE Iraq

A sedimentological characteristics of Dokan conglomerate was studied in Sulaymaniyah Governorate specifically in the Dokan region in northeastern Iraq. The study deals with four sections representing the study area; are Kani Watmman, KaniZard, Mewzha and Baloka sections. The conglomerate consist in all the sections from successive cycles of Clasts and fining upward. The thickness of each cycle ranges from a few to tens of meters. Dokan conglomerate is deposited above the Tangerio Formation in the KaneWatman and Kane Zerd sections, and on the Kometan Formation in Mewzha section and Baloka section. The contact betweenthe Formations is angular unconformity surface. The thickness of the sections in the study area are (350 m) in kani watman section, (220m) in KaniZard, (12m) in Mewzha section and (10 m) in Baloka section. The Dokan Conglomerate were divided into four Facies depending on the field rock characteristics such as color, hardness, granular size, sorting, sedimentary structures, and supported ratio, whether its supported by clast or matrix, it is composed sand stone, mudstone, orthoconglomerate and paraconglomerate lithofacies. The sedimentary environment of the area was derived using Facies analysis Which represent deposition in Alluvial Fan. This Alluvial Fan was divided into three zons, The First zone represents the nearest to the base of fan (proximal), Composed of bimodal orthoconglomerate depostes. The second zone represents the middle of fan, composed of sets of sedimentary structures such as river with channels imbricated gravel which referes to the paleocurrent direction. The third zone which is the farest from the base of the fan and consists of bolymodal conglomerate and lenses sanstone..
A Trust Value Sharing Scheme in Heterogeneous Identity Federation Topologies

Recent developments in heterogeneous identity federation systems have heightened the need for the related trust management system.The trust management system evaluates, manages, and shares users' trust values.The service provider (SP) members of the federation system rely on users' trust values to determine which type and quality of service will be provided to the users.While identity federation systems have the potential to help federated users save time and energy and improve service experience, the benefits also come with significant privacy risks.So far, there has been little discussion about the privacy protection of users in heterogeneous identity federation systems.In this paper, we propose a trust value sharing scheme based on a proxy ring signature for the trust management system in heterogeneous identity federation topologies.The ring signature schemes can ensure the validity of the data and hide the original signer, thereby protecting privacy.Moreover, no group manager participating in the ring signature, which naturally matches with our decentralized heterogeneous identity federation topologies.The proxy signature can reduce the workload of the private key owner.The proposed scheme shortens the calculation time for verifying the signature and then reduces the overall time consumption in the process of trust sharing.Our studies prove that the proposed scheme is privacy-preserving, efficient, and effective.
Shot Boundary Detection with Key Motion Estimation and Appearance Differentiation

With the rapid prevalence of video application, shot boundary detection (SBD) as a fundamental and essential technique in video analysis has aroused great attention. In this paper, to detect the boundary frames of video shots automatically we propose a joint learning framework over optical flow and color histogram for shot boundary detection. The proposed method carries out motion estimation on key points to obtain roughly video segmentation results, and simultaneously evaluate visual differentiation over spatial appearance to help derive more accurate boundary shot segmentation. By constructing differential sequences of consecutive frames, the abrupt shot and gradual shot are successfully distinguished with the variation in the dif-ferential sequences. The experimental performance demonstrate that the proposed method has a precision of 10% higher at least than that of the color histogram-based approach, and the F value is achieved about 10% higher.
Causing The Lecturers to Use Diagnostic Assessment to Find Out The Weaknesses of Students in Understanding Basic Reading in The Third Semester Students of Universitas Nahdlatul Ulama Blitar

This research is based on the still problematic confusion of basic reading lecturers to determine the weakness of students in understanding basic reading so that there are still a number of students who are completely weak in the analysis of discourse content and have not been detected clearly. Diagnostic assessment to find out the weaknesses of students in understanding basic reading in the third semester students of of Universitas Nahdlatul Ulama Blitar is applied in this study. The research method used is a descriptive qualitative method. data collection technique in this study is using interview technique. In this study, the source of the data is the third semester Basic Reading Lecturer at Universitas Nahdlatul Ulama Blitar. The results show that the strong desire of the third semester basic reading lecturers at Universitas Nahdlatul Ulama Blitar to use Diagnostic assessment resulted from several factors, namely: a) easy to understand student's ability in reading analysis, and b) easy factor to know the weaknesses of students in understanding reading. From the description above it can be concluded that Diagnostic assessment is an appropriate facility for lecturers to find out the weaknesses of students who are the top priority in learning outcomes.
Basic Dimensions of Early Reading Skills of Elementary School Students in Bandung

This study is motivated by the need for a descriptive and analytical research related to the basic dimensions of early reading skills of elementary school students to determine the level of students’ reading skills, including their difficulties in learning to read. This study aims to get a picture of the early reading skills of elementary school students in Bandung. The results of this study can be used as a guide for education practitioners, policy makers, and related institutions. Through a descriptive and analytical method, the findings are (1) 98% of students are able to identify letters in words, to analyze words, to identify the direction of letters, and to arrange letters into words. (2) 96% of students are able to strip words into syllables, to sort syllables in words, to identify words with the same forms, to identify words with the same meanings, to replace words with logical words, to insert words in incomplete sentences, and to identify abstract words. (3) The students seem to have difficulties in identifying similar consonant sounds, in identifying similarities in vowel and schwa sounds, in identifying sounds that are similar in sentences, and in identifying reversed sounds. Only 57% of students do not encounter obstacles. (4) 90% of students are capable of making sentences from random words, making sentences from words that are not sequential, correcting sentences that have wrong words, identifying simple sentences in paragraphs, and understanding simple sentences. (5) 70% of students have the ability to understand shapes, sizes, locations, and colors. This study has implications for the need for an integrated remedial program that can be used by both teachers and parents, especially for students who have difficulties with sound identification abilities.
Pose estimation from projective transformations for visual guidance of a wheeled mobile robot

Pose estimation is an essential task in many mobile robot navigation systems. Visual guidance provides a feasible means for pose estimation using the observed scene information as reference. This work presents an approach to estimate the pose of a mobile robot based on projective transformations. First, the Hough transform is used for lane detection. Next, a projective transformation is computed using the detected lines as reference. Finally, the robot's pose is estimated from the resulting projective transformation. The theoretical principles and computational implementation are analyzed. Experimental results of a visual navigation experiment are presented to validate the usefulness of the proposed approach.
Impact of Vehicle Dynamics Modelling on Feature Based SLAM for Autonomous Racing.

In autonomous racing there is a need to accurately localize the vehicle while simultaneously creating a map of the track. This information can be delivered to planning and control layers in order t ...
Video captioning using weakly supervised convolutional neural networks

The video captioning problem consists of describing a short video clip with natural language. Existing solutions tend to rely on extracting features from frames or sets of frames with pretrained and fixed Convolutional Neural Networks (CNNs). Traditionally, the CNNs are pretrained on the ImageNet-1K (IN1K) classification task. The features are then fed into a sequence-to-sequence model to produce the text description output. In this paper, we propose using Facebook's ResNeXt Weakly Supervised Learning (WSL) CNNs as fixed feature extractors for video captioning. These CNNs are trained on billion-scale weakly supervised datasets constructed from Instagram image-hashtag pairs and then fine-tuned on IN1K. Whereas previous works use complicated architectures or multimodal features, we demonstrate state-of-the-art performance on the Microsoft Video Description (MSVD) dataset and competitive results on the Microsoft Research-Video to Text (MSR-VTT) dataset using only the frame-level features from the new CNNs and a basic Transformer as a sequence-to-sequence model. Moreover, our results validate that CNNs pretrained with weak supervision can effectively transfer to tasks other than classification. Finally, we present results for a number of IN1K feature extractors and discuss the relationship between IN1K accuracy and video captioning performance. Code will be made available at https://github. com/flauted/OpenNMT-py.
Training Natural Language Understanding for the TV Context: A Visual Stimuli Approach for the Elicitation Process

Voice User Interfaces (VUI) are giving promising steps towards interaction scenarios based on natural language interpretation rather than simple voice commands. The adaptation of these systems for each usage context requires a deep training process of the Natural Language Understanding component with the support of human collaboration. Among the several phases involved in the training process, the first step, aimed at gathering a significant number of distinct “natural” phrases/expressions for each action to be addressed by the system, needs to be tackled very carefully. To be successful in collecting a significant number of phrases related to the intents to be made active in the system, it is important to stimulate participants in a way that promotes diversity on the uttered phrases. This task demands a methodology that allows participants to be triggered in an abstract way, avoiding them to be directly influenced by the stimulus presented to them. This paper reports on different stimulation strategies to prompt participants (either in group or individually) to utter distinctive phrases associated with natural language interaction with a TV set. The elicitation process that was designed was supported by visual stimuli which included still frames of video content and TV system user interfaces (showing content or apps), pictograms, comics and movies. Results show that, in general, the use of this kind of techniques can originate a significative number of diversified and relevant phrases specially if the user is familiar with the content used in the strategies.
Stereo vision parallelization for three-dimensional reconstruction

A parallel implementation of a proposed stereo vision algorithm for three-dimensional scene reconstruction is presented. The algorithm firstly estimates the disparity map of a scene from a pair of rectified stereo images using an adaptive template matched filter. Next, the estimated disparity is utilized to retrieve the three-dimensional information of the scene, by considering the stereo camera's intrinsic parameters. The proposed algorithm is implemented on an embedded graphics processing unit by exploiting massive parallelism for high-rate image processing. The performance of the proposed algorithm is evaluated in real-life scenes captured on a laboratory experimental platform in terms of accuracy and processing speed.
Working Pose and Layout Optimization of surgical robot

It is quite a challenge on positioning surgical robot to optimal place in a crowded and special environment. Compared with the structured industrial environment, the unstructured operating room (OR) environment brings higher requirements for safety of the surgical robot. Meanwhile, the efficiency has significant impact on the ease of surgical robot and repositioning requires additional time and labor. This paper introduces a new method of pose and layout planning to deal with the problem of interaction between robot and environment in operating room. The optimal layout and working pose planning of robot in OR environment are accomplished through APF calculation, pose traversal and layout traversal. The feasibility of this method is validated in simulated OR environment.