Reviewerly/exHarmony huggingface dataset:
Mobile App Success Prediction

Google play store has thousands of apps and several thousand apps are added to its list every day.The competition is so high that it is difficult for a developer to find out whether he is working in the direction of making a successful app or not.The success of an app can be determined by factors like ratings, number of installs and reviews.In this paper we have applied Exploratory Data Analysis to discover relationships among features of an app to predict which app will succeed.Data from google play store was used to train three different models to predict the success of the app-Random Forest, Support Vector Machine and Linear Regression.
Implementation of Cloudflare Hosting for Access Speed on Trading Websites

Companies that are currently developing pretty much do marketing via the website. Website becomes something very important that must be owned by trading companies engaged in the Export and Import sector. Trading websites that are currently running have flaws in terms of speed of access. The process of cloudflare hosting implementation research for access speed on trading websites uses the waterfall method which has stages such as Requirement Analysis, System Design, Implementation, Testing, and Maintenance Operations. The results obtained from this research process in accordance with the waterfall method used is successfully implementing cloudflare hosting to increase the speed of website quality to reach 100%, can even reduce spam, save bandwidth and cut open time for website pages 50% which means higher speeds. Knowing the shortcomings of the website before implementation, through cellular access speed is only 13% while through the desktop 50% and knowing the details of numbers and speed units of the website both before and after implementation. The next research that can be done is to compare the performance of cloudflare with radware, Akamai Prolexic, and Neustar.
A New Image Classification System Using Deep Convolution Neural Network And Modified Amsgrad Optimizer

A new deep Convolutional Neural Network (CNN) with six convolutional layers and one fully-connected layer is developed and trained by backpropagation using a new optimization algorithm called Fast-AMSgrad which is modified from AMSgrad. The aims are to speed up the training process while achieving acceptable accuracy. The application of the network using both, the Fast-AMSgrad and the AMSgrad algorithms to CIFAR-10 dataset for image classification reveals that the developed CNN performs better when trained with Fast-AMSgrad for both cases, with and without Batch Normalization (BN) layers. The training time is reduced by 50% when Fast-AMSgrad algorithm is used. Also the accuracy and loss values of the training and validation are improved when Fast-AMSgrad is used. &nbsp;The training and validation accuracies provided by Fast-AMSgrad with BN are (91.18% and 86.92%) at epoch number (50) and (94.13% and 86.758%) at epoch number (100), while the corresponding accuracies that are provided by AMSgrad with BN are (82.65% and 81.4%) at epoch (50) and (88.82% and 85.85%) at epoch (100). The overall test accuracy and classification metric measures indicate that the given architecture of CNN and optimization algorithm perform reasonably well
Learning Sense Representation from Word Representation for Unsupervised Word Sense Disambiguation (Student Abstract)

Unsupervised WSD methods do not rely on annotated training datasets and can use WordNet. Since each ambiguous word in the WSD task exists in WordNet and each sense of the word has a gloss, we propose SGM and MGM to learn sense representations for words in WordNet using the glosses. In the WSD task, we calculate the similarity between each sense of the ambiguous word and its context to select the sense with the highest similarity. We evaluate our method on several benchmark WSD datasets and achieve better performance than the state-of-the-art unsupervised WSD systems.
Self-Training for Unsupervised Parsing with PRPN

Neural unsupervised parsing (UP) models learn to parse without access to syntactic annotations, while being optimized for another task like language modeling.In this work, we propose self-training for neural UP models: we leverage aggregated annotations predicted by copies of our model as supervision for future copies.To be able to use our model's predictions during training, we extend a recent neural UP architecture, the PRPN (Shen et al., 2018a), such that it can be trained in a semi-supervised fashion.We then add examples with parses predicted by our model to our unlabeled UP training data.Our self-trained model outperforms the PRPN by 8.1% F1 and the previous state of the art by 1.6% F1.In addition, we show that our architecture can also be helpful for semi-supervised parsing in ultra-lowresource settings.
Sedimentology and Paleogeographic Synthesis of Cretaceous Sediments in Auchi Area of Anambra Basin

The sedimentary rock in the Auchi area of Edo State constitutes part of the Upper Cretaceous Deposits of the Anambra Basin, which has its depocentre in Eastern Nigeria. Lithostratigraphic and Sedimentological studies carried out on twenty eight (28) selected samples from a section of a road–cut show that the sediments range from fine through medium to coarse grained. Sorting is poor to moderate. Grain size analysis shows that the sediments are strongly fine skewed and they exhibit mesokurtic to platykurtic kurtosis. These coupled with the various colours observed in the sediment, ranging from whitish sand through yellowish brown, pink and reddish brown possibly indicate a non–marine environment and a fluviatile to deltaic environment of sedimentation is suggested. The sediments might have been transported in a fairly high energy medium and deposited under a slightly fluctuating velocity.&#x0D; Keywords: Sedimentology, Cretaceous, Paleogeography, Grain size, Anambra Basin
Distribution, Sources and Modified Degree of Contamination of Trace Elements in Linggi Estuary Surface Sediments, Malaysia

The purpose of this study is to assess the trace elements of barium (Ba), bromine (Br), cesium (Cs), cobalt (Co), hafnium (Hf), iron (Fe), manganese (Mn), rubidium (Rb), scandium (Sc) and vanadium (V) in the surface sediments of Linggi estuary in Negeri Sembilan, Malaysia, using the neutron activation analysis (NAA) technique.Results showed that the average concentration of Br, Cs, Hf, Rb, Sc and V were 75.1, 12.6, 10.0, 146, 10.6 and 80.5 mg/kg, respectively, which were all higher than the upper continental crust (UCC) reference values.However, the average concentration values of Ba, Co, Mn, and Fe were 272, 6.39, 350, and 26,547 mg/kg, respectively, which were all relatively lower compared to the UCC values of the respective elements.The provenance of Br and Hf were from seawater and marine organisms, while Cs and V came from from anthropogenic activities, such as discharge from electronic factories and vehicle workshops.The element of Rb and Sc originated from geogenic processes, such as erosion, landslide and weathering.Overall, the trace element contamination in surface sediments of Linggi estuary was categorised as moderate to highly modified degree of contamination (mC d ), and these could result in adverse effects on benthic dwelling organisms and aquatic life.
Research on Task Discovery for Transfer Learning in Deep Neural Networks

Deep neural network based machine learning models are shown to perform poorly on unseen or out-of-domain examples by numerous recent studies.Transfer learning aims to avoid overfitting and to improve generalizability by leveraging the information obtained from multiple tasks.Yet, the benefits of transfer learning depend largely on task selection and finding the right method of sharing.In this thesis, we hypothesize that current deep neural network based transfer learning models do not achieve their fullest potential for various tasks and there are still many task combinations that will benefit from transfer learning that are not considered by the current models.To this end, we started our research by implementing a novel multi-task learner with relaxed annotated data requirements and obtained a performance improvement on two NLP tasks.We will further devise models to tackle tasks from multiple areas of machine learning, such as Bioinformatics and Computer Vision, in addition to NLP.
Conceptual Persian Text Summarizer: A New Model in Continuous Vector Space

Traditional methods of summarization are not cost-effective and possible today. Extractive summarization is a process that helps to extract the most important sentences from a text automatically, and generates a short informative summary. In this work, we propose a novel unsupervised method to summarize Persian texts. The proposed method adopt a hybrid approach that clusters the concepts of the text using deep learning and traditional statistical methods. First we produce a word embedding based on Hamshahri2 corpus and a dictionary of word frequencies. Then the proposed algorithm extracts the keywords of the document, clusters its concepts, and finally ranks the sentences to produce the summary. We evaluated the proposed method on Pasokh single-document corpus using the ROUGE evaluation measure. Without using any hand-crafted features, our proposed method achieves better results than the state-of-the-art related work results. We compared our unsupervised method with the best supervised Persian methods and we achieved an overall improvement of ROUGE-2 recall score of 7.5%
Communicating Collections Cancellations to Campus: A Qualitative Study

Academic libraries around the world are cancelling big deal journal subscriptions at an increasing rate. This is primarily due to budgetary challenges, the unsustainable hyperinflationary pricing of these packages, and a need to move toward new open access models. It is a complex situation with many vested interests and stakeholders. Some libraries have been the target of angry backlash from faculty after such cancellations. The purpose of this qualitative study is to discover strategies for communicating to the campus community about collections cancellations so that they will better understand and support the library in making these difficult decisions.
Hybrid Approaches to Fine-Grained Emotion Detection in Social Media Data

This paper states the challenges in fine-grained target-dependent Sentiment Analysis for social media data using recurrent neural networks. First, the problem statement is outlined and an overview of related work in the area is given. Then a summary of progress and results achieved to date and a research plan and future directions of this work are given.
Increasing the Engagement of Conversational Agents through Co-Constructed Storytelling

Storytelling can be used by conversational agents in a wide variety of domains to maintain user engagement, both within a single interaction and over dozens or hun- dreds of interactions over time. The majority of agents designed with this ability to date deliver their stories as monologues without user input. However, people rarely tell stories in conversations this way, and instead rely on listener contributions to guide the storytelling process. Corpus-based studies of human-human conversational storytelling have demonstrated greater engagement, in the form of longer stories, when listeners co-construct stories this way. We describe a research framework for the generation and evaluation of co-constructed social stories in the context of task-based conversations, and a study on the effects of degree of user-agent story co-construction on user engagement. We find that users are more en- gaged with storytelling agents that allow them to co- construct stories in a contentful manner by asking ques- tions, compared to co-construction through acknowl- edgments only.
A Systematic Evaluation of Object Detection Networks for Scientific Plots

Are existing object detection methods adequate for detecting text and visual elements in scientific plots which are arguably different than the objects found in natural images? To answer this question, we train and compare the accuracy of Fast/Faster R-CNN, SSD, YOLO and RetinaNet on the PlotQA dataset with over 220,000 scientific plots. At the standard IOU setting of 0.5, most networks perform well with mAP scores greater than 80% in detecting the relatively simple objects in plots. However, the performance drops drastically when evaluated at a stricter IOU of 0.9 with the best model giving a mAP of 35.70%. Note that such a stricter evaluation is essential when dealing with scientific plots where even minor localisation errors can lead to large errors in downstream numerical inferences. Given this poor performance, we propose minor modifications to existing models by combining ideas from different object detection networks. While this significantly improves the performance, there are still two main issues: (i) performance on text objects which are essential for reasoning is very poor, and (ii) inference time is unacceptably large considering the simplicity of plots. To solve this open problem, we make a series of contributions: (a) an efficient region proposal method based on Laplacian edge detectors, (b) a feature representation of region proposals that includes neighbouring information, (c) a linking component to join multiple region proposals for detecting longer textual objects, and (d) a custom loss function that combines a smooth L1-loss with an IOU-based loss. Combining these ideas, our final model is very accurate at extreme IOU values achieving a mAP of 93.44%@0.9 IOU. Simultaneously, our model is very efficient with an inference time 16x lesser than the current models, including one-stage detectors. Our model also achieves a high accuracy on an extrinsic plot-to-table conversion task with an F1 score of 0.77. With these contributions, we make a definitive progress in object detection for plots and enable further exploration on automated reasoning of plots.
MOBILE ASSISTED LANGUAGE LEARNING: EFL LEARNERS� PERCEPTIONS TOWARD THE USE OF MOBILE APPLICATIONS IN LEARNING ENGLISH

Although extensive research has been carried out on the positive effect of Mobile Assisted Language Learning (MALL) for students� learning, there have been few studies concerning on university students� perceptions toward the use of MALL in learning English. The study was undertaken to examine university students� perceptions toward MALL specifically the use of mobile application in learning English. The participants of this study were ninety six students (68 females and 28 males). Data collection instruments used in this study were questionnaire and semi-structured interview. It reported that all participants apply MALL by downloading and using English mobile applications to help their learning. Six mobile applications which are often used by all participants were Kamusku, Google Translate, Elsa Speak, Youtube, Zoom and Google Meet. The results of the questionnaires also showed that their perceptions toward the use of MALL was that MALL, in this case the English mobile application was considered to be useful and helped their learning, besides it was overall easy to use.
Terremoti con eﬀetti macrosismici in Sicilia nel periodo gennaio 2014 ­ dicembre 2018

The macroseismic surveys of earthquakes occurred in Sicily during the time­span 2014­2018, are hereinafter presented. Analysed data mainly concern significant or damaging events located in the volcanic region of Mt. Etna, together with other regional shocks in southern Sicily and Aeolian islands. Data have been collected and processed following the guidelines of the QUEST working group (QUick Earthquake Survey Team) of INGV and then parametrised according to the procedures used for compiling the CPTI catalogue and DBMI macroseismic database.
Learning based Coding for Medical Image Compression

The area of Image processing has emerged with different coding approaches, and applications which are ranging from fundamental image compression model to high quality applications.The advancement of image processing, has given the advantage of automation in various image coding applications, among which medical image processing is one of the prime area.Medical diagnosis has always remained a time taking and sensitive approach for accurate medical treatment.Towards improving these issues, automation systems have been developed.In the process of automation, the images are processed and passed to a remote processing unit for processing and decision making.It is observed that, images are coded for compression to minimize the processing and computational overhead.However, the issue of compressing data over accuracy always remains a challenge.Thus, for an optimization in image compression, there is a need for compression through the reduction of non-relevant coefficients in medical images.The proposed image compression model helped in developing a coding technique to attain accurate compression by retaining image precision with lower computational overhead in clinical image coding.Towards making the image compression more efficient, this research work introduces an approach of image compression based on learning coding.This research achieves superior results in terms of Compression rate, Encoding time, Decoding time, Total processing time and Peak signal-to-noise ratio (PSNR).
Cost-Reference Particle Filter for Cognitive Radar Tracking Systems with Unknown Statistics

A novel robust particle filtering algorithm is proposed for updating both the waveform and noise parameter for tracking accuracy simultaneously and adaptively. The approach is a significant step for cognitive radar towards more robust tracking in random dynamic systems with unknown statistics. Meanwhile, as an intelligent sensor, it would be most desirable for cognitive radar to develop the application of a traditional filter to be adaptive and to expand the adaptation to a wider scope. In this paper, after analysis of the Bayesian bounds and the corresponding cost function design, we propose the cognitive radar tracking method based on a particle filter by completely reconstructing the propagation and the update process with a cognitive structure. Moreover, we develop the cost-reference particle filter based on optimizing the cost function design according to the complicated system or environment with unknown statistics. With this method, the update of the estimation cost and variance arrives at the approximate optimization, and the estimation error can be more adjacent to corresponding low bounds. Simulations about the tracking implementation in unknown noise are utilized to demonstrate the superiority of the proposed algorithm to the existing methods in traditional radar.
iEpilepsy monitoring and alerting system using machine learning algorithm and WHMS

Advances in wireless electronics devices have led to easy design and develop of wearable sensor systems for health monitoring. These wireless sensors have been considered as one of the most important technologies that can change the future and has garnered lots of attention in the scientific community and the industry during the last years. These devices consist of small battery with limited computation and radio communication capabilities. These wireless sensor systems has become essential in such domains as industrial operations, health care, environmental infrastructure and research and development. Accelerometer sensor added with these wireless devices value to automatically detect seizures in temporal lobe epilepsy patients. The accelerometer sensor that is used in the wireless device is used to calculate the vibration threshold developed in the body. After calculating the threshold with various thresholds factors that has occurred in the body using seizure detection algorithm the device sends alerts to emergency contacts. In the future, we’ll see the mixing of a huge array of wireless networks into existing specialised medical technology. The aim of this paper is not to criticize, but to serve as a reference for researchers and developers in this scientific area and to provide direction for future research improvements.
Application of “BIM+” Architecture Based on Cloud Technology in Intelligent Management of Rail Transit

The rail transit industry is developing rapidly, the complexity of its projects is growing, and the corresponding information is also growing rapidly. The rail transit industry is affected by multi-disciplinary coordination, multi-sector management, and multi-link decision-making, which is prone to problems such as information faults and management fault. This study relies on the advantages of BIM throughout in the whole life cycle, based on cloud technology, combined with VR, AR, 3D scanning, and other cutting-edge technologies to form a "BIM+" architecture. In the construction stages, with the help of VR technology, the project construction is guided. The program experience and demonstration are immersed in the constructed virtual reality, and all construction time nodes and key technical links are intuitively grasped, thereby optimizing the plan and ensuring implement construction plan high-efficiency and orderly. In the operation and maintenance stages, based on BIM+AR technology, and comprehensive use of VR, 3D scanning, Cloud database, smart handheld terminals, and Internet of Things technologies to realize real-time data correlation of rail transit in the whole life cycle, multiple ways of information retrieval and modular maintenance instruction, etc. This research has opened up the information transmission path in the field of intelligent construction, laid the foundation for intelligent management, and promoted the informationization and digital development of the railway station reconstruction and expansion project.
Applying of information and communication technologies in the education process

The authors emphasize that the reform of the education system leads to changes in the technological and technical nature in the educational process. Information and communication technologies are now actively used in education, which, according to the authors, contribute to the design of a high-quality information educational space for higher education and the creation of the information culture of actors in the educational process. Based on the analysis, the authors revealed that in the communication space of the educational organization, circulate both external flows external relations with social groups that are outside the educational organization, and internal ones connected with the organization staff and students. External flows are built at the organizational and managerial level and based on media communications. Internal information and communication flows in the educational process are carried out through the introduction of electronic document management, corporate mail, student assessment through a remote system, and the use of onlinelearning.