Reviewerly/exHarmony huggingface dataset:
Impact of the shape of digital micro-mirrors on super high- resolution 3D shape measurement (Conference Presentation)

The state-of-art three-dimensional (3D) shape measurement techniques based on digital fringe projection (DFP) techniques assume that the influence of the shape of projector pixel shape is negligible. However, our research reveals that when the camera pixel size is much smaller (e.g., 1/10 or less) than the projector pixel size in object space, the shape of the projector pixel significantly can impact ultimate measurement quality even when binary defocusing technique is employed. This paper evaluates the performance of two different types of projector pixels: rectangular and diamond shaped. Both simulation and experimental results demonstrated that when, the camera pixel size is significantly smaller than the projector pixel size, it is advantageous to use a projector with rectangular pixels than the projector with a diamond shaped pixels.
Towards a Performant Multilingual Model Based on Ensemble Learning to Enhance Sentiment Analysis

The aim of sentiment analysis, known as opinion mining, is to discover subjective information by understanding the meaning over public opinions, standpoints and attitudes from the shared text, such as consumers feedback, which focuses on automated tools. In this paper, we introduce a novel model based on a learning approach to enhance the rate of understanding and predicting the sentiment of a text. Our proposed learning approach establishes an effective penalty mechanism to map out the links between the analyzed context in which the sentiment is similar. With a gold standard corpus we released, the results obtained are better in terms of precision, recall, and computation time cost using a multithreading model.
A Tale of Two Modalities for Video Captioning

Recent advances in machine learning have led to significant accuracy improvements for the task of generating textual captions from videos based on audio and visual signals. In this work, we focus on the influence of modality (audio and visual input) on semantic coherence and well-formedness of the generated captions. We explore both architectural and algorithmic choices that potentially influence the utilization of these modalities. (Algorithmic choices include pretraining while architectural choices include modality-specific weighing schemes.) We study the influence of our choices on a popular video captioning dataset, MSRVTT, by providing quantitative and ex-tensive qualitative evaluations that measure the influence of audio-visual modalities, cohesiveness and ranked relevance of keywords. We are able to assert qualitative improvements on metrics characterizing the quality of the captions, along with obtaining performance that is comparable to state-of-the-art on standard quantitative metrics such as BLEU-4, METEOR, etc.
Hosting Inspec on Engineering Village or Web of Science

Purpose As library budgets continue to constrict, librarians will need to become more familiar with comparing database host platforms. This paper aims to compare Inspec on Elsevier’s Engineering Village (EV) and Clarivate’s Web of Science (WOS) from a novice user experience. The main objectives are to identify some R1 institutions that subscribe to Inspec and highlight some of the key differences between the two platforms. Design/methodology/approach Information on Inspec was gathered from various sources such as the home website, IET, and the host platform websites of Elsevier and Clarivate Analytics. Additional evidence was also collected from brochures and guides to help illustrate some of the main features and differences that novice users would be familiar with. Findings Most institutions subscribe to Inspec via the EV platform. Results from the study conclude that EV was the choice over WOS for hosting Inspec owing to a more user-friendly interface, potential lower cost and faster platform updates in response to meeting user needs. Research limitations/implications As database platforms change over time, feature areas such as content, interface and features remain important for information professionals and librarians to stay current with those changes. Also, this work can help librarians with understanding the planning and developing of a process for comparing identical or similar databases on different platforms. Originality/value Much of the literature focuses on the unfamiliar details and not so much on the novice user. This paper provides a unique perspective in how a novice user would prefer the attributes of one host platform over the other. Additionally, the same review criteria can be applied in other subjects and disciplines.
A Hybrid Machine Learning Approach for Analysis and Classification of Social Network Sentiments

with the evolutionary improvement and establishment of social networking sites huge amount of data associated with user's perceptions, emotions, posts, comments, reactions etc. Meanwhile, extraction of valuable information like user's opinion, sentiment from those massive and higher dimensional dataset have become one of the most crucial, complex and convoluted tasks nowadays. Moreover, immediate realization of user's perspective regarding any topic or product can ensure feasible as well as user friendly environment throughout the social networking sites. In this respect, from current point of view available natural language processing approaches are less applicable because of their limitations. Therefore, effective machine learning approaches long with the concept of NLP need to be invented to negotiate with this sort of situation. Realizing worth of this situation and for negotiating with the concept of big data, we have proposed a machine learning approach by combining unsupervised machine learning approach (Self-Organizing Map) for clustering as well as dimensionality reduction and classification approach (Adam Deep Learning) together. Moreover, for better outcome we have adopted MaxAbsScaler initially. For further clarification we have also considered principal component analysis based deep learning, Logistic Regression, Normalization less SOM based CNN mechanism for comparative study. To ensure effective performance and better feasibility in case of big data we have also employed our approach on different sizes of social network datasets. Overall analysis and demonstration interprets superiority of our proposed approach with accuracy of 88.83%.
Climbing the Occasionally Cataclysmic Cascades

Living in Geologic Time: Every one of the Pacific Northwest’s volatile volcanoes is likely to erupt again before the range goes extinct.
Data Mining Strategies for Drug Analysis on Twitter

This research discovers and analyses various drugs (weed) associated tweets on twitter. In this regard, we have gathered 5000 drugs associated tweets throughout October 2018 in our research study. Our study related to texting top of algorithms and data analysis reveal some interesting models that add in users' attitude which would be distinguished by the subsistence of outer link in tweet. From the recent survey, it is evident that 72% people upload their text using their smart phones while other users operate via computers etc; people usually tweet on Saturday and Sunday in large number when compared with the weekdays.
NoSq, database, interaction mechanism, GIS

Based on the data interaction mechanism between NoSql database and relational database, this paper proposes a new solution to the real-time and effectiveness of cross-platform data interaction.Develop a middleware for NoSql and relational database synchronization data.By actively requesting GIS services, the GIS service accepts the request and returns the request data.
TOWARDS REFERENCE MODELS FOR REQUIREMENTS TRACEABILITY

A Reference model in systems, enterprise and software engineering is abstract framework or domain specific ontology consisting of an interlinked set of clearly defined concepts produced by an expert or body of experts in order to encourage clear communication. There are many uses of reference models. Main use of reference model is to create standards for both the objects that inhibit the model and their relationships. By creating the standards, the work of engineers and developers who need to create objects that behave according to the standard is made easier. In this paper we are proposing S-cube model of Quality reference model. Reference models are therefore an abstraction of best practice, condensed from many case studies over an extended period of time, followed by more case studies to refine and evaluate the proposed reference model. There are challenges towards the service-based systems. The network of excellence on software services and systems (S-cube) performs cross –discipline research to develop solutions for those challenges. In this paper we outlined the S-cube reference model life cycle and how is used to know how a service is developed. S-cube knowledge model which is a continuously updated on-line encyclopedia and a reference library. In this paper we are presenting about S-cube framework which is used to align and bring together the involved disciplines and co-existing of services.
Ambient seismic noise suppression in COST action G2Net

&amp;lt;p&amp;gt;The analysis of low-frequency gravitational waves (GW) data is a crucial mission of GW science and the performance of Earth-based GW detectors is largely influenced by ability of combating the low-frequency ambient seismic noise and other seismic influences. This tasks require multidisciplinary research in the fields of seismic sensing, signal processing, robotics, machine learning and mathematical modeling.&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;In practice, this kind of research is conducted by large teams of researchers with different expertise, so that project management emerges as an important real life challenge in the projects for acquisition, processing and interpretation of seismic data from GW detector site. A prominent example that successfully deals with this aspect could be observed in the COST Action G2Net (CA17137 - A network for Gravitational Waves, Geophysics and Machine Learning) and its seismic research group, which counts more than 30 members.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;div&amp;gt;In this talk we will review the structure of the group, present the goals and recent activities of the group, and present new methods for combating the seismic influences at GW detector site that will be developed and applied within this collaboration.&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt; &amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;This publication is based upon work from CA17137 - A network for Gravitational Waves, Geophysics and Machine Learning, supported by COST (European Cooperation in Science and Technology).&amp;lt;/p&amp;gt; &amp;lt;/div&amp;gt;
Performance comparison of different loss functions for digital breast tomosynthesis classification using 3D deep learning model

Artificial intelligence (AI) algorithms, especially deep learning methods have proven to be successful in many medical imaging applications. Computerized breast cancer image analysis can improve diagnosis accuracy. Digital Breast Tomosynthesis (DBT) imaging is a new modality and more advantageous compared to classical digital mammography (DM). Therefore, development of new deep learning algorithms compatible with DBT modality are potent to improve DBT imaging reading time efficiency and increase accuracy for breast cancer diagnosis when used as additional tool for radiologists. In this work, we aimed to build a 3D deep learning model to distinguish malignancy and benign breasts using DBT images. We also investigated effects of different loss functions in our deep learning models. We implemented and evaluated our method on a large data set of 546 patients (205 malignancy and 341 benign). Our results showed that different loss functions lead to an influence on the models performance in our classification tasks, and specific loss function may be selected or customized to adjust a specific performance metric for concrete applications.
IMRPOVING STUDENTS’ VOCABULARY ACHIEVEMENT THROUGH STORY COMPLETION TECHNIQUE AT SMPN 8 BANDARLAMPUNG

Tujuan penelitian ini adalah untuk mengetahui pengaruh penerapan Story Completion Technique dalam meningkatkan kemampuan penguasaan kosa kata siswa dalam belajar Bahasa Inggris, dan juga untuk mengetahui partisipasi. Desain yang digunakan adalah one group pretest posttest. Sampel penelitian adalah siswa kelas VIII F SMPN 8 Bandarlampung pada tahun akademik 2018/2019. Pengambilan data dilakukan dengan tes objektif penguasaan kosa kata dan juga lembar observasi Berdasarakan hasil statistik menggunakan SPSS 16 membuktikan bahwa skor siswa meningkat secara signifikan dengan nilai signifikansi adalah p <0.005 (p = 0.000). Nilai rata-rata pre-test siswa adalah 3.93 dan pada post-test skor rata-rata adalah 6.20. Hal itu juga berpengaruh pada partisipasi siswa dalam hal bertanya. Berdasarkan temuan, dapat disimpulkan bahwa Story Completion Technique dapat meningkatkan kemampuan penguasaan kosa kata siswa. The objectives of the research were to investigate the effect of the implementation of Story Completion Technique in improving students’ vocabulary achievement in learning English, and also to find out students’ participation. The research design was one group pretest and posttest . Then, the sample of this research was class VIII F of SMPN 8 Bandarlampung in academic year 2018/2019. The data were collected by using objective vocabulary test and observation sheet. The statistical result by using SPSS 16 proved that the students’ score improved significantly with the significance value was p<0.005 (p=0.000). The students’ mean score is 3.93 and in the post-test mean score is 6.20. It also affected on students’ participation in the aspect of asking, they frequently asked questions. Based on the finding, it can be concluded that teaching English by using Story Completion Technique could improve  students’ vocabulary achievement. Keywords : improve, vocabulary, story completion technique
A survey of computation techniques on time evolving graphs

Analysing time evolving graphs (TEG) in a timely fashion is critical in many application domains, such as social network. Many research efforts have been made with to address the challenges of volume and velocity from dealing with such datasets. However, it remains to be an active and challenged research topic. Therefore, in this survey, we summarise the state-of-art computation techniques for TEG. We collect these techniques from three different research communities: 1) the data mining for graph analysis; 2) the theory for graph algorithm; 3) the computation for graph computing framework. Based on our study, we also propose our own computing framework DASH for TEG. We have even performed some experiments by comparing DASH and graph processing system (GPS). We are optimistic that this paper will help many researchers to understand various dimensions of problems in TEG and continue developing the necessary techniques to resolve these problems more efficiently.
Preliminary data on TruviewMAG to assess the visibility of microcalcifications and image quality of mammograms: comparison with the standard magnification view

<strong>Purpose:</strong> To compare the visibility of microcalcifications and image quality of specimen mammograms obtained with the digital magnification algorithm(TruviewMAG) view and standard magnification view <strong>Materials and Methods:</strong> Nineteen women (median age, 49 years; ranges, 38-71 years) who underwent stereotactic biopsy or ultrasound-guided vacuum-assisted biopsy for suspicious microcalcifications detected at mammography were prospectively enrolled. After biopsy, specimen mammograms were obtained to confirm the removal of microcalcifications. A pair of specimen mammogram were obtained with both TruviewMAG view (DRTECH Corp., Seongnam, Korea) and standard magnification view. Four blinded readers independently evaluated visibility of microcalcifications, contrast and resolution of specimen mammograms using a 10-point scale (1, poor; 10, excellent), and preference. In addition to specimen mammograms, we performed pilot clinical test to evaluate the visibility of microcalcifications in two patients. <strong>Results:</strong> The visibility of microcalcifications on specimen mammograms obtained with TruviewMAG view was comparable to those obtained with standard magnification view in overall reading (mean, 8.2±1.8 vs. 8.1±1.4, P = .893). The contrast of specimen mammograms obtained with TruviewMAG view was comparable to those obtained with standard magnification view in overall reading (mean, 7.8±1.6 vs. 7.8±1.4, P = .827). The resolution of specimen mammograms obtained with TruviewMAG view was comparable to those obtained with standard magnification view in overall reading (mean, 7.7±1.7 vs. 7.5±1.3, P = .173). Four readers preferred TruviewMAG view and standard magnification view at equal rates. The mean entrance surface dose for TruviewMAG view was 2.88mGy, which was 58.3% reduction compared to 4.94 mGy in standard magnification view. The visibility of microcalcifications were similar in two patients (one with benign fibrocytic change, and the other with invasive ductal carcinoma) <strong>Conclusion: </strong>The visibility of microcalcifications and image quality of the specimen mammograms obtained with TruviewMAG view was comparable with those obtained with standard magnification view. In clinical patients data, microcalcifications were well visualized on TruviewMAG view, even though prospective study with larger study population is needed to confirm our finding.
A Study on the Error Pattern of Word Order by Korean Learners : Based on Proficiency and L1.

This study aims to examine the interlanguage pattern of Korean word order by inductively analyzing errors of word order according to proficiency and L1 of Korean learners and confirming the distribution of word order error types. For this, the study identified the acquisition patterns of Korean word order based on 567,000 word Learners Corpus of 3,600 samples constructed by the National Institute of Korean Language. The analysis results are as follows: First, 157 word order errors were found in 149 Korean learners. Among them, Korean learners have the highest number of adverb word order errors which appeared 81 times (51.6%), followed by noun word order errors which appeared 50 times (31.8%). Discourse word order errors appeared 13 times (8.3%), sentence word order errors appeared 11 times (7.0%) and other word order errors appeared 2 times (1.3%) in this order. Second, the error frequency of adverb word order was the highest in common among beginner, intermediate, and advanced, and the error frequency of noun word order was the second highest. Though learners of beginner and intermediate level and learners of advanced and intermediated level each have differences in the error frequency ranking of sentence word order and discourse word order, it was not statistically significant. Third, the acquisition distribution of word order according to learner’s L1 was found to be statistically significant in the adverb order and the noun word order. In the case of adverb word order, Vietnamese learners had fewer errors in adverb word order than other learners, while Japanese learners had more adverb word order errors. Next, in the case of noun word order, Vietnamese learners had more noun word order errors than other learners of Korean, but English learners of Korean found fewer noun word order errors.
LRTanH: Substitution for the Activation Function Derivative during Back Propagation

Back-propagation algorithm is one of the key stages of neural network training. At this stage, the derivation of an activation function is employed to adjust the weights of neurons in the hidden layer. A successful back-propagation depends on a good activation function to converge desired values and minimize the classification error.This paper presents a new approach for training on neural networks by substituting a different activation function derivative to investigate its affects during back-propagation. We modify the derivation of hyperbolic tangent activation function (TanH) by adding a variable that depends on a linear model. Our modified function is called Linearized Hyperbolic Tangent function (LRTanH). We tested LRTanH on MNIST Digits and MNIST Fashion datasets. LRTanH increased accuracy to 88.4% versus 81.06% with 64 nodes on MNIST Digits dataset.
Needle and Syringe Exchange Program for HIV/AIDS Prevention : Areas to be Considered by Law Enforcement Agency for Implementation

Needle and Syringe Exchange Programme (NSEP) is a HIV/AIDS prevention programme targeting hardcore drug addicts. NSEP encourages addicts to exchange used needles with new syringe for free. The NSEP in Malaysia involves the cooperation of multi-sector agencies such as the Ministry of Health (MOH), the Royal Malaysian Police (RMP) and the Malaysian AIDS Council (MAC). The implementation of the NSEP creates controversy when it being seen to encourage continuous drug addicts activities and solely focus on HIV/AIDS prevention. An exploratory study being conducted to examine the involvement of multisectoral in the NSEP. This article would only discuss RMP's findings with regards to its discretionary dilemma as a drug law enforcement agency. Five police officers of the Narcotics Crime Investigation Department were selected as informants. Data collection being carried out by using an in-depth interview method. The analyses form theme from data that being carried out inductively. This article would discuss only two of the overall studies: i) the form of discretion given by the RMP to NSEP clients and ii) the challenges encountered by RMP in defending its discretion. The findings highlighted dilemma encountered by police on their discretion not to arrest or impose any detention procedures towards NSEP clients. The RMP found it difficult to exercise discretion towards client because: i) the discretion not to arrest the addict was against the law, ii) the RMP was concerned about the misuse of discretion by the client and iii) the discretionary giving could affect public perception of RMP responsibility and integrity. The study proposes a module in implementing the NSEP on a multisectoral network especially involving the police.
Multi-action Offline Policy Learning with Bayesian Optimization

Diagonal preconditioning has been a staple technique in optimization and machine learning. It often reduces the condition number of the design or Hessian matrix it is applied to, thereby speeding up convergence. However, rigorous analyses of how well various diagonal preconditioning procedures improve the condition number of the preconditioned matrix and how that translates into improvements in optimization are rare. In this paper, we first provide an analysis of a popular diagonal preconditioning technique based on column standard deviation and its effect on the condition number using random matrix theory. Then we identify a class of design matrices whose condition numbers can be reduced significantly by this procedure. We then study the problem of optimal diagonal preconditioning to improve the condition number of any full-rank matrix and provide a bisection algorithm and a potential reduction algorithm with $O(\log(\frac{1}{\epsilon}))$ iteration complexity, where each iteration consists of an SDP feasibility problem and a Newton update using the Nesterov-Todd direction, respectively. Finally, we extend the optimal diagonal preconditioning algorithm to an adaptive setting and compare its empirical performance at reducing the condition number and speeding up convergence for regression and classification problems with that of another adaptive preconditioning technique, namely batch normalization, that is essential in training machine learning models.