Reviewerly/exHarmony huggingface dataset:
Learning Meaningful Sentence Embedding Based on Recursive Auto-encoders

Learning meaningful representations for different granularities of texts is a challenging and on-going area of research in natural language processing. Recently, neural sentence modeling that learns continuous valued vector representations for sentences in a low dimensional latent semantic space has gained increasing attention. In this work, we propose a novel method to learn meaning representation for variable-sized sentence based on recursive auto-encoders. The key difference between our model and others is that we embed the sentence meaning while jointly learning evolved word representation in unsupervised manner and without using any parse or dependency tree. Our deep compositional model is not only able to construct meaningful sentence representation but also to keep pace with the words meanings evolving. We evaluate our obtained embeddings on semantic similarity task. The experimental results show the effectiveness of our proposed model and demonstrate that it can achieve a competitive performance without any feature engineering.
Research of Integrated Reorganization of Mineral Rock and Fossil Specimen Data Based on ETL Technology

In the integrated reorganization of mineral, rock and fossil specimen data [1], standards and norms are the prerequisite, and the collection and integration of resource data are the core. ETL technology has the ability to realize data extracting, transforming and loading. In this paper, it has been introduced into the integrated reorganization of mineral, rock and fossil specimen data. Kettle which is an ETL tool was selected to collect and integrate multi-source and heterogeneous specimen data, and have get good application results. It proved the effectiveness of ETL technology in the integrated reorganization of specimen data. The method has a certain practical reference significance, providing some scientific reference for the integrated reorganization of other resources data.
Fundamentals of spiritual, moral, patriotic and cultural education in work with students of the Mozhaisk Technical School

The article discusses the main directions of the educational and psychological adaptation course in the spiritual and moral education of students of the Mozhaisk Technical School, the formation of a correct civil position through the love to history, the Fatherland, and small motherland. Respect for the representatives of various nationalities and denominations of religion becomes every year an increasingly relevant topic. In this regard, spiritual, moral and patriotic education becomes a guarantee of a correct civil position.
When CI and Decentralized Systems Effectively Meet Smart Cities and Grids

A global trend has been motivating programmers, investors, and the academia to go towards decentralized systems. Besides providing efficient solutions for complex problems faced in our daily life, these peer-to-peer communication protocols have been promoting greater freedom and transparency. In this paper, we point out open fields for researching, thinking, and developing in the context of Smart Cities and Smart Grids. Future cities will surely rely on efficient, autonomous, transparent, collaborative, and decentralized environment. In particular, we consider how renewable energy resources could be integrated with mini-/microgrids, which will be hearth of the future cities. Furthermore, we discuss possibilities for promoting territorial development, through the assistance of Blockchain-based platforms embedded with Computational Intelligence tools.
Proposed Low Cost Library Automation in Developing Countries Using Modern Programming & Information Technologies

Background/Objectives: Manual Record Keeping results in lot of inconvenience for readers and librarians.The objective of this study is to explore the automation possibility of public libraries in developing countries.Methods/Statistical Analysis: Two-part solution using Modern Programming Platforms has been proposed to the above stated problem: 1.A cross platform application with a local DataStore and Sync capabilities is needed which will be installed on every target library and operated by a librarian, and 2. A server is required to store a Global Datastore derived from all the active local datastores connected to the server, from which users can query information through webpage.Findings: Proposed automation not only supports all Operating Systems namely MS-Windows, Linux and MacOS but it also supports existing APIs on Java and C++.For JAVA, separate VM needs to install but it is fully supported on C# due to usage of Electron Framework.Improvements/Applications: All the interactions between reader and librarian are automated successfully and authorized user time is saved due to supported browsing at home.The automation helps in trending analysis about particular book, article and periodical reading.
Brain Volume Segmentation Outliers Correction in Structural MRI Images

The skull stripping procedure is an important image preprocessing step commonly applied in many neuroscience studies. Even though several efforts have been made in order to create robust brain extraction algorithms, minor segmentation errors still remain, often requiring manual refinement. In this study, an automatic Brain Volume Refinement (BVeR) method is proposed. The method interprets segmentation outliers as local interference in brain tissue signal contrast, offering a suitable solution for external brain boundary adjustment of structural T1 and T2 weighted MRI. Two publicly available structural MRI image datasets of healthy adults and two commonly used brain extraction methods (BET and FreeSurfer) were used for evaluation. Quantitative segmentation evaluation for accuracy and reproducibility were applied to evaluate the performance of BVeR, showing that the average brain volume refinement showed a significant improvement (p < 0.001) in most metrics. In conclusion, the BVeR method offers an automatic alternative to the manual correction often requested in brain MRI studies, in which it considerably reduces human errors and processing time.
Alpha-tree segmentation of human anatomical photographic imagery

Segmentation of anatomical imagery is important in several areas, such as forensics, medical analysis and educational material. The manual segmentation of such images and the subsequent labelling of regions is a very laborious task. We propose an interactive segmentation scheme which we evaluate on a new data set of anatomical imagery. We use a morphological tree-based segmentation method, known as the alpha-tree, together with a Hu-moment thresholding mechanism in order to extract segments from a number of structures. Both qualitative and quantitative results in anatomical imagery of embalmed head, arm and leg specimens indicate that the proposed method can produce meaningful segmentation outputs, which could facilitate further refined labelling.
A flipped lesson on email etiquette: An instructor and student's perspective

TESOL JournalVolume 11, Issue 1 e00463 CLASSROOM EXPLORATIONS A flipped lesson on email etiquette: An instructor and student's perspective Ilka Kostka, Ilka Kostka Northeastern UniversitySearch for more papers by this authorZiru Wang, Ziru Wang Northeastern UniversitySearch for more papers by this author Ilka Kostka, Ilka Kostka Northeastern UniversitySearch for more papers by this authorZiru Wang, Ziru Wang Northeastern UniversitySearch for more papers by this author First published: 20 May 2019 https://doi.org/10.1002/tesj.463Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume11, Issue1March 2020e00463 RelatedInformation
The Application of An Optimized Convolutional Neural Network Model in Film Criticism

Constructing a model of online film and television commentary sentiment classification can effectively guide film and television producers to comprehensively understand the audience acceptance of film and television works, and improve it. Traditional methods based on sentiment lexicon and machine learning exist in a series of Insufficient: ignore context semantics, too single word, sparse features, etc. Based on the existing convolutional neural network model, this paper systematically optimizes its internal structure, and proposes a NCNM (New Convolutional Neural Network model) model based on multi-sliding window and new pooling method, and uses feature vectors to cluster feature words. . In this paper, the Stanford SST dataset and Cornell MRD dataset are used to verify the classification effect of the proposed model. The experimental results show that ncnnm has a certain improvement in the accuracy of the emotional classification of short text video reviews compared with the existing mainstream methods..
Mouse Control Interface Using Electrooculogram and Genetic Programming

The ability to communicate without using speech or hand gestures poses a great improvement in the quality of life of patients that suffer from movement impairment. Human-machine interaction tools are being studied and developed in order to optimize the usage of biological signals not affected by the individual’s disease. Among different approaches electrooculography signals are an alternative for those who can still move their eyes. This work proposes the use of Genetic Programming to interpret bio signals in the control of a mouse cursor. A digital system was designed to record and filter the EOG signal. Thereafter a Genetic Programming algorithm was used to find the best description for the cursor movement. We show that the algorithm was able to find an equation that describes the moment with 92.5 and 93.0% hit rate for each subject respectively. These preliminary results are compatible with the literature and show that Genetic Programming can be used to find a description of a cursor movement in a simple EOG system with no need of prior knowledge about the movement neither threshold definition.
Methods and Technologies of Machine Phraseological Translation Based on the Generalized Syntagm Model

The studies performed by the authors show that when using the model of generalized syntagmas, it is possible to implement the PMT process using a shorter and simpler technological scheme. This technological scheme can be reduced to two stages: 1) the stage of formation of several search syntagmas characterizing the semantic content of the source text sentences (on the base of results of morphological and simplified syntactic analysis); 2) the stage of searching for semantic information in the reference dictionaries of translations of global and local constructions of sentences, having which it will be possible to automatically form translated sentences of text on their basis. A comparison of the effectiveness of phraseological machine translation using the method of generalized syntagmas and without using it was made.
Information Extraction with Humans in the Loop

Information Extraction (IE) techniques enables us to distill Knowledge from the abundantly available unstructured content. Some of the basic IE methods include the automatic extraction of relevant entities from text (e.g. places, dates, people, ...), understanding relations among them, building semantic resources (dictionaries, ontologies) to inform the extraction tasks, connecting extraction results to standard classification resources. IE techniques cannot decouple from human input - at bare minimum some of the data needs to be manually annotated by a human so that automatic methods can learn patterns to recognize certain type of information. The human-in-the-loop paradigm applied to IE techniques focuses on how to better take advantage of human annotations (the recorded observations), how much interaction with the human is needed for each specific extraction task.
Image processing for snake indentification based on bite using Local Binary Pattern and Support Vector Machine method

False identification snake bite from observation of visual features is a dangerous thing, because if it is get wrong in handling of first aid against poisonous snake bites, it would be result in death. In Indonesia there are 348 types of venomous snakes, of course with a very large number of it must be wary if bitten by snake. In general, ordinary people with snake bite patterns will panic and do not know what to do for first aid if they are bitten by snake. Even a doctor can be wrong in identifying snake bites. Difficulty of identifying snake directly, then made an Image Processing System for Identification snake bite using Local Binary Pattern and Support Vector Machine method that helps identify and classify snakes automatically. This system classifies venomous and non-venomous snake based on its bite pattern image using two methods ie Support Vector Machines(SVM) and Local binary Pattern(LBP). Final result from this research yielded 89% accuracy with P=8, r=1 for LBP and C=1, Ϭ=0.15 for SVM using the RBF kernel. The dataset used has 28 data, the data divided into 19 training data and 9 test data.
Latent Relational Model for Relation Extraction

Analogy is a fundamental component of the way we think and process thought. Solving a word analogy problem, such as mason is to stone as carpenter is to wood, requires capabilities in recognizing the implicit relations between the two word pairs. In this paper, we describe the analogy problem from a computational linguistics point of view and explore its use to address relation extraction tasks. We extend a relational model that has been shown to be effective in solving word analogies and adapt it to the relation extraction problem. Our experiments show that this approach outperforms the state-of-the-art methods on a relation extraction dataset, opening up a new research direction in discovering implicit relations in text through analogical reasoning.
Implementation of Robust Solid State Drive Controller Using LZ77 Compression and SHA-1 Encryption Technique

Solid State Drive (SSD) Technology is used to read/write data from host to the flash device with high speed and low power. Data compression technique represents data in compact form which can be achieved by data redundancy, while retaining the data content using lossless data compression. Secure hashing algorithm-1 (SHA-1) encryption algorithm encrypts data by modifying information and by inserting messages from fraudulent source, deletion and recording sequence. This paper deals with solid state drive using LZ77 compression algorithm and SHA-1 encryption algorithm for robust compression and encryption in SSD devices. The proposed method LZ77 Compression algorithm employs a text window in conjunction with a look-ahead buffer. For matched character, if pointers are selected with effective encoded offset position then it results in better compression ratio. For the unmatched character, compression ratio is 3.12 times better than the existing algorithm. To securely transfer the data in SSD device, data must be encrypted using SHA-1 encryption algorithm. Since a lossless compression is used, the process is reversible. Thus, fast and secure data transmission is possible with encryption. SSD comprises of both write and read cycle; the compressed and encrypted data is stored in flash and read at read cycle with reversible technique. In read cycle, decryption is used to retrieve the encrypted data back into its original compressed form. Here, the data is retrieved with higher speed. Hence, the performance of buffer in SSD increases. Therefore, speed of the read/write cycle can be improved upto 8 bytes/cycle.
Deep learning based super resolution of aerial and satellite imagery

Super-resolution is the process of creating high-resolution (HR) images from low-resolution (LR) images. Single Image Super Resolution (SISR) is challenging because high-frequency image content typically cannot be recovered from the low-resolution image and the absence of high-frequency information thus limits the quality of the HR image. Furthermore, SISR is an ill-posed problem because a LR image can yield several possible high-resolution images. To address this issue, numerous techniques have been proposed but recently deep learning based methods have become popular. Convolutional Neural Network (CNN) approaches to deep learning have shown great success in numerous computer vision tasks. Therefore, it is worthwhile to explore CNN-based approaches to address this challenging problem. This paper presents a deep learning based super resolution (DLSR) approach to find a HR image from its LR counterpart by learning the mapping between them. This mapping is possible because LR and HR images have similar image contents and differ primarily in high-frequency details. In addition, DLSR utilizes residual learning strategy where the network learns to estimate a residual image. DLSR is applied to both aerial and satellite imagery and resulting estimates are compared against the traditional methods using metrics such as Peak Signal to Noise Ratio (PSNR), Structure Similarity Index Metric (SSIM), and Naturalness Image Quality Evaluator (NIQE) also called perceptual quality index. Results obtained depict that DLSR outperform the traditional approaches.
Research on College English Teaching System Based on Resource Library and Network Support Platform

These years, with the great development of electronic technology and the rise of the digital revolution, the advanced media of network multimedia, which integrates sound and picture animation, is being applied to college English teaching by more and more colleges and universities. Traditional education can meet the needs of information, digital and networked society anymore. A transformation that necessarily involves teaching content, teaching methods, teaching methods and teaching structure is taking place. Under the background of the limitations of thinking, material and technical conditions in China’s current education system, the use of online multimedia English teaching will inevitably encounter many problems. For example, it is difficult for students to adapt and accept the method of learning English through network multimedia. The ability to fully utilize and accept multimedia networks is not available; unchanging courseware cannot adapt to different levels of students to learn independently according to their own abilities; formative assessment and summative assessment cannot be effectively combined; teaching management is arranged in class, The scores of records, credits, attendance registration, etc. are unchanged or there are no corresponding reasonable changes. In view of the problems existing in the current transition from college English to network multimedia mode, this paper analyzes the causes of these problems and puts forward the countermeasures to solve college English teaching problem under the condition of network multimedia.
Fixation data analysis for complex high-resolution satellite images*

This paper reports an empirical study which inspects the human segmentation process for high-resolution satellite (HRS) images in the absence of target objects. The lack of standard guidelines for eye-tracking data analysis in the above-mentioned case motivates this study. Since a careful selection of area of interest (AOI) is necessary for meaningful interpretation of gaze data, this study represents a way to draw AOIs which metrics correlate with the human verbal response. This study formulates a hypothesis for AOI drawing and updates it until the metrics of the hypothesis based AOIs matches with the verbal response. The outputs of this study suggest the grouping of objects during AOI-based analysis of gaze data and provides some rules for the same. The AOIs drawing for hypothesis validation also highlights the dominance of perceptual grouping over the functional grouping of objects. Developing an autonomous system for segmentation and reference data creation are some possible future scopes of this study.
Research on a Network Communication Data Privacy Protection System Based on OFDM Technology

At present, most data theft cases originate from the malicious intrusion of individual hackers to the production database. Therefore, more and more countries attach importance to the next generation of network communication, and a large amount of human and financial resources are invested in the research and development of data privacy protection of network communication. In order to perform real-time monitoring and intelligent control of network communication, the intelligent network communication control center needs to continuously collect the user’s network communication data, and if the data leaks, it will expose the network communication rules and living habits of the individual users. In this paper, the actual system of high-speed data communication is realized by OFDM technology, and the problem of network communication data privacy protection is discussed and solved.
Learning Based Image Super Resolution Using Sparse Online Greedy Support Vector Regression

Kernel filters with sparse solutions has become highly advantageous because of the reduced computation. Sparse online greedy support vector regression algorithm, computes the coefficients only after the generation of sparse dictionary. This paper super resolves a low resolution image to high resolution image, with the model generated from the training set using sparse online greedy support vector regression. The method is evaluated with super resolution using support vector regression. Comparisons are done on the PSNR, time and memory scales. The sparse online greedy support vector regression shows good improvement in these scales.
A General Process for the Semantic Annotation and Enrichment of Electronic Program Guides

Electronic Program Guides (EPGs) are usual resources aimed to inform the audience about the programming being transmitted by TV stations and cable/satellite TV providers. However, they only provide basic metadata about the TV programs, while users may want to obtain additional information related to the content they are currently watching. This paper proposes a general process for the semantic annotation and subsequent enrichment of EPGs using external knowledge bases and natural language processing techniques with the aim to tackle the lack of immediate availability of related information about TV programs. Additionally, we define an evaluation approach based on a distributed representation of words that can enable TV content providers to verify the effectiveness of the system and perform an automatic execution of the enrichment process. We test our proposal using a real-world dataset and demonstrate its effectiveness by using different knowledge bases, word representation models and similarity measures. Results showed that DBpedia and Google Knowledge Graph knowledge bases return the most relevant content during the enrichment process, while word2vec and fasttext models with Words Mover’s Distance as similarity function can be combined to validate the effectiveness of the retrieval task.