Reviewerly/exHarmony huggingface dataset:
GetIT for Students:

Everything is accessible by one click nowadays, that is the beauty of mobile application. The existence of mobile application has ease the work of humans in so many ways. Mobile application itself has become an indispensable factor in human daily lives. Predominantly, what the society have been utilized is services through mobile application. Mobile application that offers services, i.e. GrabFood, foodpanda and much more are proven useful as their services are highly demanded by the society. However, those services are mostly available in urban area. To those who are in outskirt area especially students will really hope those services can reach them. Even though the demands are high among students, the distance and cost are some of the barriers and obstacles that need to consider by the food and other services provider. Due to the limitation of those services available to students and the cost is quite high, this has motivated the study to create a mobile application that works in a campus consisting numerous services. This mobile application is called GetIT. With some comparative study that has been done in this study on the system development process, this study has adopted user experience (UX) design in creating GetIT. Until to date, the creation of GetIT is expected to continue to build; test and launch and maintain phase. Upon completion of GetIT, it is hope that the students are able to benefits the services offer with the success development of this mobile application.
BEAD: Batched Evaluation of Iterative Graph Queries with Evolving Analytics Demands

Simultaneous evaluating a batch of iterative graph queries on a distributed system enables amortization of high communication and computation costs across multiple queries. As demonstrated by our prior work on MultiLyra [BigData'19], batched graph query processing can deliver significant speedups and scale up to batch sizes of hundreds of queries.In this paper, we greatly expand the applicable scenarios for batching by developing BEAD, a system that supports Batching in the presence of Evolving Analytics Demands. First, BEAD allows the graph data set to evolve (grow) over time, more vertices (e.g., users) and edges (e.g., interactions) are added. In addition, as the graph data set evolves, BEAD also allows the user to add more queries of interests to the query batch to accommodate new user demands. The key to the superior efficiency offered by BEAD lies in a series of incremental evaluation techniques that leverage the results of prior request to "fast-foward" the evaluation of the current request.We performed experiments comparing batching in BEAD with batching in MultiLyra for multiple input graphs and algorithms. Experiments demonstrate that BEAD's batched evaluation of 256 queries, following graph changes that add up to 100K edges to a billion edge Twitter graph and also query changes of up to 32 new queries, outperforms MultiLyra's batched evaluation by factors of up to 26.16 × and 5.66 × respectively.
Report on the International Conference on Design of Experimental Search &amp; Information Retrieval Systems (DESIRES 2018)

This is a report on the first edition of the International Conference on Design of Experimental Search &amp; Information REtrieval Systems ( DESIRES 2018) held in Bertinoro, Italy, from August 28 to August 31, 2018.
A Perceptive Fake User Detection and Visualization on Social Network

Online reviews give a significant asset to potential clients to settle on buy choices. Be that as it may, the sheer volume of accessible reviews just as the enormous varieties in the review quality present a major hindrance to the compelling utilization of the reviews, as the most accommodating reviews might be covered in the huge measure of low quality reviews. The objective of this framework is to create models and calculations for anticipating the supportiveness of reviews, which gives the premise to finding the most accommodating reviews for given items. We first show that the supportiveness of a review relies upon three significant variables: the reviewer's mastery, the composing style of the review, and the practicality of the review. Contrasted with different all around examined conclusion investigation and feeling rundown issues, less exertion has been made to break down the nature of online reviews. The target of this paper is to fill right now naturally assessing the “supportiveness” of reviews and subsequently creating novel models to distinguish the most accommodating reviews for a specific item.
Extracting Multi-Scale Rotation-Invariant Features in Convolution Neural Networks

Recently, many image recognition applications employing convolutional neural networks have great success with satisfactory accuracy performances due to the rapid development of computer hardware and neural network technology. In these applications, convolutional neural networks used a group of rotated images in random angles from the same object as the network inputs during training to effectively recognize rotated objects. However, as shown in this study, this rotated-image approach could not effectively learn rotational features. In this paper, we present the multi-scale rotation-invariant features used in the convolutional neural network to identify rotational invariance. It can successfully capture rotational features for various data sets. Our model is established through the concepts of dihedral group transformations, multi-level, multi-scale, rotation-invariant pooling, and sharing weights of the convolutional neural network to learn convolution kernels that can achieve rotational invariance. Our model showed a significant improvement over other ones and practically learned the rotational invariance of an object.
Studying Execution Time and Memory Transfer Time of Image Processing Using GPU Cards

With advancements in technology, the size and quantity of captured images to be analyzed are rapidly increasing. As the number of pixels in an image increases (more than 20 megapixels), additional processing power is required to process the images in a time-efficient manner. Modern computer systems with multicore Central Processing Unit (CPU) and General Purpose Graphics Processing Unit (GPU, short for GPGPU) show promise in processing images. However, different GPU cards provide different kinds of performance on different imaging techniques. In this work, we study execution time and memory overhead of three different GPU cards on two image processing algorithms, namely Sobel filter along with Bilateral filter and Canny edge detector. GPU cards used are Nvidia Tesla/Fermi C2075, GeForce/Maxwell GTX-965, and Tesla/Kepler K20. Experimental results with more than 120 megapixels show that K20 provides the best execution time speedup (more than 300x) for the Sobel filter. Whereas, GTX-965 provides the best speedup (almost 120x) for the Canny edge detector. It is also observed that the memory transfer time due to GTX-965 is minimum (about 0.21 seconds for the Sobel filter and 0.23 seconds for the Canny detector). For both algorithms, GTX-965 performs better or about the same as C2075. This study is helpful to select the right GPU card for a given problem.
Ecology and STEM

Abstract This paper is devoted to combining ecology challenges and STEM technologies. We consider ecology as an integral part of STEM process. The including of ecology in the STEM allows not only to develop mathematician calculations and engineering skills but also forms students’ ecological consciousness. The attempt to combine ecology and STEM is aimed to make the knowledge in ecological sphere more practical. STEM education can help in solving the main ecological problems and challenges. STEM lessons, projects, conferences, exchanges could explain and give a living example of damages caused to our planet by irrational using of natural resources. It can improve public awareness and show the careful attitude to our planet.
3D-FFS: Faster 3D object detection with Focused Frustum Search in sensor fusion based networks

In this work we propose 3D-FFS, a novel approach to make sensor fusion based 3D object detection networks significantly faster using a class of computationally inexpensive heuristics. Existing sensor fusion based networks generate 3D region proposals by leveraging inferences from 2D object detectors. However, as images have no depth information, these networks rely on extracting semantic features of points from the entire scene to locate the object. By leveraging aggregated intrinsic properties (e.g. point density) of point cloud data, 3D-FFS can substantially constrain the 3D search space and thereby significantly reduce training time, inference time and memory consumption without sacrificing accuracy. To demonstrate the efficacy of 3D-FFS, we have integrated it with Frustum ConvNet (F-ConvNet), a prominent sensor fusion based 3D object detection model. We assess the performance of 3D-FFS on the KITTI dataset. Compared to F-ConvNet, we achieve improvements in training and inference times by up to 62.80% and 58.96%, respectively, while reducing the memory usage by up to 58.53%. Additionally, we achieve 0.36%, 0.59% and 2.19% improvements in accuracy for the Car, Pedestrian and Cyclist classes, respectively. 3D-FFS shows a lot of promise in domains with limited computing power, such as autonomous vehicles, drones and robotics where LiDAR-Camera based sensor fusion perception systems are widely used.
Answer Book Valuation Using Semantic Similarity

Abstract Conventional examination systems are a tedious task for the universities and the faculties. It takes a lot of time and human effort for the valuation of the answer books, updation of the marks and the declaration of the results. Automation of descriptive answer book assessment would be useful for universities and academic institutions to simplify the valuation system to a large extend. It ensures a uniform valuation and also helps to publish the results without much delay after the examination. We design a system for automatic assessment of descriptive answer books of technical subjects. Semantic similarity is a metric used to assess the similarity between documents and it gives the degree of similarity as a numeric value. It can be used to value the students’ answer books by checking the similarity with original answers given in the answer key and then award appropriate marks.
An Augmented Treble Stream Deep Neural Network for Video Analysis

Video analysis for human action recognition is one of the most important research areas in pattern recognition and computer vision due to its wide applications. Deep learning-based approaches have been proven more effective than conventional feature engineering-based models. However, the performance is still unreliable when facing real-world application scenarios. Inspired by the Convolutional Neural Network (CNN) and Recurrent Long-Short Term Model (LSTM), this paper presents an augmented treble-stream deep neural network architecture that supports direct extraction of spatial-temporal features from video streams and their corresponding dense optical flows. This innovative approach assists effective detection of complex video event features that are annotated by rich event "appearance" and motion features. Substantially improved recognition accuracy is recorded during the experiments that are carried and benchmarked over public video event datasets, for example, UCF 101 and HMDB 51. Analytical evaluation approves the validity and effectiveness of the treble-stream neural network design.
Downloading and Installing R

Abstract This chapter is a primer on getting started with the R language. It provides a brief background and history of R and a guide to downloading and installing R on three different operating systems (Windows, Mac, and Linux). It also gives an introduction to the Comprehensive R Archive Network (CRAN), the installation and use of the RStudio Integrated Development Environment (IDE), the basic commands concerning saving code that’s been written, and changing working directories.
Hierarchical Annotation Event Extraction Method in Multiple Scenarios

In the event extraction task, considering that there may be multiple scenarios in the corpus and an argument may play different roles under different triggers, the traditional tagging scheme can only tag each word once, which cannot solve the problem of argument overlap. A hierarchical tagging pipeline model for Chinese corpus based on the pretrained model Bert was proposed, which can obtain the relevant arguments of each event in a hierarchical way. The pipeline structure is selected in the model, and the event extraction task is divided into event trigger classification and argument recognition. Firstly, the pretrained model Bert is used to generate the feature vector and transfer it to bidirectional gated recurrent unit+conditional random field (BiGRU+CRF) model for trigger classification; then, the marked event type features are spliced into the corpus as known features and then passed into BiGRU+CRF for argument recognition. We evaluated our method on DUEE, combined with data enhancement and mask operation. Experimental results show that our method is improved compared with other baselines, which prove the effectiveness of the model in Chinese corpus.
Multilingual automation of transcript preprocessing in Alzheimer's disease detection

Abstract Introduction Analyzing linguistic functions can improve early detection of Alzheimer's disease (AD). To date, no studies have focused on creating a universal pipeline for clinical transcript preprocessing. Methods This article presents a simple and efficient method for processing linguistic and phonetic data, sequencing subproblems of cleaning, normalization, and measure extraction tasks. Because some of these tasks are language‐ and context‐ dependent, they were designed to be easily configurable, thus increasing their scalability when dealing with new corpora. Results Results show improved performances over previous studies in this time‐consuming preprocessing task. Moreover, our findings showed that some discursive markers extracted from transcripts revealed a significant correlation (&gt;0.5) with cognitive impairment severity. Discussion This article contributes to the literature on AD by presenting an efficient pipeline that allows speeding up the transcripts preprocessing task. We further invite other researchers to contribute to this work to help improve the quality of this pipeline ( https://github.com/LiNCS-lab/usAge ).
AI, tecnologie innovative e produzione normativa: potenzialità e rischi

The paper aims at investigating the innovative perspectives of using Artificial Intelligence (AI) in the law-making process. The idea of the essay is that the evolution of AI, maybe transforming itself in a General AI, could allow a deeper inclusion of AI devices even in the production of “political law”, not only in the judicial or administration fields. The possible connection between AI and political processes is individuated in collective intelligence systems that, through the crowdsourcing, can generate political Big Data coming from participation and open debate. From this point of view the paper proposes a comparative analysis concerning the use of crowdsourcing platforms and digital tools for political decisionmaking(and constitutional deliberation), focusing in particular on Icelandic and Finnish models. Another hypothesis of subsidiary utilization of AI could be linked with the existing proposals regarding the establishment of parliamentary internal bodies aimed at empowering the role of representative assemblies in facing scientific-technical issues. At the end, the paper discusses the possible impact of algorithmic legislative decision on the concept of political representation, and underlines the enduring criticalities concerning creation of political opinions and political data.Keywords: Artificial Intelligence; Legislative process; Political representation; Crowdsourcing; Deliberative democracy.
Formation of a Programmatic Management Mechanism for Risk Reduction in Educational Activity

The modern educational environment is characterized by an increase in the influence of factors of uncertainty and instability. Based on a comparative analysis of the concepts of control, their improved typology has been formed. To form the model of the risk control system, the authors have chosen the concepts of control, focused on the management information system, the management system with an emphasis on management functions and coordination of the management process. The definition of the economic category “risk control” was formulated. The theoretical foundations of the formation of the risk control system of educational institutions have been improved.
Transparent Shepherding: A Rule-Based Learning Shepherd for Human Swarm Teaming

This chapter aims to demonstrate how rule-based Artificial Intelligence algorithms can address a few human swarm teaming challenges. We will start from the challenges identified by the cognitive engineering community for building human autonomy teaming and how they scale to human swarm teaming. The discussion will follow with a description of rule-based machine learning with a focus on learning classifier systems as a representative for these algorithms and their benefits for human swarm teaming. Shepherding affords a human to manage a swarm by teaming with an autonomous single shepherd. A learning classifier system is designed to learn behaviour needed to be exhibited by the shepherd. Results demonstrate the effectiveness of the rule-based XCS model to capture shepherding behaviour, where the XCS model achieves comparable performance to the standard Strömbom’s shepherding method as measured by the number of steps needed by a sheep-dog to guide group of sheep to target destination. These results are promising and demonstrate that learning classifier systems could design autonomous shepherds for new type of shepherding tasks and scenarios that we may not have rules for today.
Formación en competencias informacionales y digitales para el fortalecimiento a los procesos de titulación en los posgrados del Centro Universitario del Norte

This research report is intended to give evidence of the work done by the members of the UDG-CA-919 innovation, technology and education, given the concern of the low degree rates in postgraduate courses offered at the University Center of the North (CUNorte) of the University of Guadalajara, through the design, development and implementation of the Diploma in Informational and Digital Competencies. The dynamic that was carried out for the realization of this study was the description and analysis of three stages of work: definition of the program, design and development of the diploma (hosted on the Moodle learning platform). The contribution of this report reflects the results of the design of a program that provides adequate informational and digital competencies to foment the construction of research projects for students and graduates of CUNorte postgraduates through the integration of an instructional design (ADDIE model), teaching strategies and use of technological tools through the design of the diploma that provides significant skills and learning; and its development for its implementation.
Performance analysis of shape, color and texture features on tracking information face based on CBIR

Abstract The image has the features of shape, color and texture that are vary. Each feature has a different performance in supporting the accuracy of information retrieval using a process approach to CBIR (Content-Based Image Retrieval). On the image with different objects different performance will be generated on each feature. For example, that the performance features of the form of the more dominant compared features color and texture on the image with the face, while the object on the image with the object of interest feature is more dominant than the features of texture and shape. In this research was conducted on the analysis of the performance features of the shape, color and texture in supporting the accuracy of a search using the approach of CBIR (Content Based Image Retrieval). The method used are invariant moment, color moment and GLCM (Grey Level Co-occurrence Matrix). The results showed that the best search accuracy is 95%, where the features of shape has a performance by 50%, 30% color feature and texture feature by 20% with 600test image with object database face.
Visualizing the Future of Collections: How to Make Data Visualization Accessible and Useful for Managing Collections and Museums

Data are vital to collections—from managing their holdings to quantifying their impact. Data visualization can be a powerful asset for collections because it translates complex data into something intuitive. In recent years, data visualization (and dashboards specifically) has gradually been applied in collections for various tasks and audiences. However, this frontier is still new, and there are significant barriers making data visualization inaccessible for most collections, including time and resources, programming experience, training materials, and consistent examples. In this case study, I directly address these barriers by designing a suite of collection management dashboards using the free and user-friendly software Tableau Public and tutorials that explain how to use and customize the them. These resources were finalized with the input from a representative pool of the target audience. Everything is freely available online at http://www.CollVizDashboards.com . As museums continue to evolve, data visualization ought to be accessible to all collections.
An Empirical Study on Efficiency of a Dictionary Based Viterbi Algorithm for Word Segmentation

In this paper we present an algorithm for segmenting English sentences, without spaces, into their constituent words based on a dictionary using a variation of the Viterbi algorithm. We call this variation the dictionary based Viterbi algorithm for word boundary identification. The input to the algorithm is a sequence of English words which are not space separated and the output is list of the top-k possible word segmentations. The top-k segmentation of the input into the constituent words is determined on the basis of an optimality criteria applied to a scoring function which computes the "segmentation score" of each possible word sequence for the given input. Our implementation works from the back (right) of the input string and creates a data structure for storing the possible word boundaries during the first pass. The data structure consists of an index (a list of nodes) of length equal to the number of input characters in the string. The i <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> node in the index contains a pointer to the optimal list of words for the substring starting at position i of the input sequence. The list of possible breakdowns including the optimal one is collected during a second pass through the data structure. Due to the way the initial data structure is built during the first pass we call our algorithm the Reverse Sequence Search (RSS) algorithm. We report experimental results on several datasets including the entire Brown corpus with over one million words to show how fast our algorithm works. We also compare our implementation with a dynamic programming implementation provided by Norvig [1] which is one of the classical algorithms for the problem of word segmentation. Furthermore we show that our algorithm runs in O(n) time and space.