Reviewerly/exHarmony huggingface dataset:
Automatic Stopword Detection Using Term Ranking between Written and Machine Speech Recognition Transcribed Reviews

Video feedback and machine speech recognition are fast-becoming a popular choice for companies to gain insight into their products. In conjunction with this, text analytics can be used to extract insight from these video translations. Currently, there is little work in the area to analyse and compare techniques for natural language processing, information retrieval and information extraction. A commonly practiced technique in text analytics is the extraction of stop words; words whose presence do not contribute context or information to a document. In this paper, we explore statistical techniques for the automated extraction of stop words, comparing 4 datasets from written and translated reviews. Using statistical variations of the successful technique `term ranking', we evaluate their performance using a common list of stop words. Results suggest that variation, TFnormIDFnorm, was the most successful with a best performing precision rate of 46.7% and a recall rate of 86.6%. The best results were seen in the largest dataset using written reviews, however comparison of the remaining 3 datasets revealed that spoken text performed 0.4% better in precision than the next best dataset and 2.6% better in recall. Initial results show marginally better performance in machine speech recognition transcribed texts from videos in comparison to comparably size datasets of written reviews.
Mutual Authentication and Location Privacy using HECC and SHA 2 in Mobile Cloud Computing Environment

Cloud computing alludes to information sharing, putting away of information in distributed storage and sharing assets to the customer. Regarding the data security is a top undermining issues, in this paper we analyze about the data uprightness issues of the cloud. A protected correspondence is exceptionally required and is acted in the proposed calculation. Preserve the privacy of the data. SPYSECUREOU (Secure Documents Care Owner and User) data, which contains personal data of the client, is dealt with in re-appropriated condition. Here the endorsing of the data is checked by the information proprietor or a TPA-outsider inspecting utilizing Secure Hash Algorithm (SHA2) which work alongside the Hyper Elliptic Curve Crypto framework which is an encryption method for the information which make use of multiple phases, conversion and paradigm related to data in binary form provide high security in low computation time. To preserve the security of that data is the main objective of this dissertation. The approach also exhibit data shared efficiency in comment (Data posting) phase and accessing phase. A further extension to the work can be opting out in giving efficiency with encryption model.
New Technologies Shaping Learning?

Jean Piaget described that intelligence is shaped by experience. In augmented reality (AR) learning environments, the learner may have an immersive experience, from a sensor-motoric opportunity as a person to 3D experience. Few studies in the academic literature directly evaluate and analyze learning technology with regard to immersive experience in training. This chapter seeks to examine learning experiences when playing with AR learning technologies and suggests an alternative implementation model for the integration of immersive learning content to adult training. Specifically, this study examines a learning tool and a game targeted for the professionals working in security and peacebuilding context. This study points out a relevance of cognitive and constructive learning processes with a special attention to experience and reflection, and that technological immersive tools can positively support training when designed properly. Moreover, case study findings led to proposing an implementation model to integrate immersive content, AR tools, and games into adult training.
A Web-Component-Based Cross-Platform Mobile Application Development Environment for Ordinary Users

The rapid progress of the mobile internet has been promoting the popularity of mobile devices, and mobile application development is getting more pervasive. However, state of the art development environments have a high learning barrier for ordinary users. In this paper, we take consideration of ordinary users’ requirements and propose a WYSIWYG cross-platform web-component-based mobile applications creation environment for ordinary users. This environment has a WYSIWYG visual editor with drag-and-drop web component. A web component library model is proposed to standardize customized libraries. A cross-platform application model based on composite web components is imported to implement a rapid application build approach with one-click buttons, which helps ordinary users generate installing packages within simple operations for multiple platforms. A native plugin model is proposed to assist web components to invoke native functionalities. The experiment result shows that ordinary users could easily start to create mobile applications in our environment.
Analysis of Recent Trends in Automatic Object Identification

Automatic object identification (auto-ID) involves techniques for automatically identifying objects using visual features or tags with unique identification codes. These auto-ID systems then transfer the collected identification information to for further . In this paper, we analyze the existing auto-ID techniques for physically tagged objects.
Solving the playing strategy of Dou Dizhu using convolutional neural network: A residual learning approach

Poker is the typical game of incomplete information, and remains a longstanding challenge problem in artificial intelligence (AI). The game of Dou Dizhu has been viewed as a thorny topic in AI since it is featured with hidden information and large branching factors, and the cooperation and competition should also be handled. In this article, deep learning is adopted to train a supervised learning playing strategy network (PSN) for Dou Dizhu directly from expert human playing. Through experiments, it was found that the sample design with the appropriate historical playing hand sequence and more features of the playing situation, can help the PSN learn more competitive and accurate playing strategies faster. In the online game platform, the strategy network-based game agent reaches an average winning rate of 52.22% against the human players. In addition, the analysis of the gameplay data against human players shows that the playing strategy network has learned the rules of playing and the characteristics of card recognition and reasonable demolition, cooperation and reasoning. Finally, we improve the performance of the PSN in the aspect of sample design. Then, the experimental results show that with proper marking of the number of remaining hands, the performance of the PSN can be enhanced.
Mobile Interface Attentional Priority Model

Mobile devices are now the most popular computing technology for accessing the Internet. This has resulted in designers devoting more of their time to improving users’ mobile experiences. This can be achieved in part by helping users find content quickly and easily. Understanding what supports or harms a user’s visual search is key to creating displays that meet usability efficiency requirements. Broadly, searches are guided by a combination of visual salience and previous experiences. We recommend revealing these influences by employing a computational saliency model and by employing our mobile spatial convention map. The research presented extends the previous attentional priority (AP) modeling work, from web pages to mobile interfaces. Notably, we reveal that users typically search a mobile web page by using a “railroad-like” viewing pattern rather than the “F” pattern that is typically described in web page research. Also, we propose a mobile web page-specific attentional priority (AP) model. The AP model combines our experience-based spatial convention map with a saliency model map. We examined the predictive performance of a saliency model, compared to the mobile-specific convention map. It was discovered that the convention map better predicted the initial deployment of attention, and the saliency map better accounted for later selection.
Video Enhancement Utilizing Old and Low Contrast

It has been relied upon and is still found in the fields of scientific research, especially astronomy, medicine (for accurate disease diagnosis), biology, archeology, and industry on video and still images. The low accuracy and quality of some videos are often due to a poor lens type or angle, which may be due to a lack of photographic experience, or because of the older sections, which can be affected by the coolness of the surrounding perimeter. This research was completed using simple methods of processing based on using a program to convert video to individual images, then a number of image processing operations to improve quality, and finally re-assemble the images to the video more accurately than the original and in our own way. The proposed process consists of several steps: cutting the video clip into a set of images, performing various operations, such as using the contrast filter first, discovering the edges, smoothing the image, and improving image density prior to assembly. We finally assemble the images back into clips. This has been the process we used on many of the films affected by noise, or damaged for a long time, and has proven our ability to improve the quality of the video.
An Analysis on OCL/UML Constraints in E-commerce Application

E-commerce is the most efficient and time-effective method to promote a product to worldwide customers. Its transaction may require submitting customer’s personal information such as username, password, credit card numbers, and financial data. Unified Modeling Language (UML) or Object Constraint Language (OCL) was one of the standardized languages for software developers with visualizing the software system in e-commerce. In e-commerce, purpose security is the primary issue meant for transferring personal data, online payment, and user details. Due to the lack of such security, a significant portion of the customers is feeling uncomfortable to send their respective information over the Internet. This study investigated completely 40 different kinds of literature which are collected from standard publications. Each research paper analyzes different ideas to secure online shopping based on the UML/OCL. In the existing papers, some of the security drawbacks are pointed out and they are overcome by various languages with different software effectively is reviewed by using new research papers.
Partitions on the Projective Line and Plane of Order 17k,k=1,2,3

In this research, the main purposes are making partitions of the projective line, PG (1, q3), q=17, and embedding the projective line, PG (1, q ) into PG (1, q3), as well as finding some partitions of PG (2, q2) as subplanes, orbits, triangles and arcs, and studying the properties of these subsets. Furthermore, the arcs with different degrees and sizes are found besides the embedding of the projective plane, PG (2,q) into PG (2, q2). In the coding theory, there are 14 projective codes that are introduced.
Response to Discussants

I am deeply grateful to Steven Stern for his remarkable and brilliant discussion of my paper in which he provides a theoretical scaffolding for my work with Drum, a scaffolding largely missing in m...
Obstacle Avoidance Path Planning Based on Target Heuristic and Repair Genetic Algorithms

Using genetic algorithm (GA) to optimize the mobile robot path planning has the disadvantages of low initial population generation efficiency and low initial population quality, especially under large size and complex environment model in grids. In order to overcome this problem, a novel methodology contains a target heuristic operator and a reparation operator is proposed in this paper. In this study, the maps consist of the obstacles areas and the feasible areas are decomposed by grids. These two operators are integrated into the GA and applied to acquire the collision-free shortest path in a static two-dimension environment. The experimental data show that the methodology can decrease significantly the random search time for generating the initial population and improve the quality of the initial population generation. Results suggested that the proposed requires a shorter amount of time and possesses a better global searching performance, compared with the conventional methods.
Cloud computing and running your code on Google cloud

Cloud computing has taken over industry by storm, yet there aren't enough new college grads who know enough about it. This session begins with a vendor-agnostic, high-level overview of cloud comput...
Refinement of Print’s Model in Designing a Ladderized Program

This article describes the steps in the refinement of Print's Curriculum Model to make it suitable in designing a ladderized program in Hospitality Management. The factors to be considered in designing ladderized programs were identified through a thorough review of literature on the principles and requirements of lifelong learning systems and were incorporated into the 3 phases of Organization, Development and Application within Print's Model. Four factors were identified as essential to ladderized programs : the critical role of teachers, the primacy of situational analysis , alignment of the four elements of curriculum intent, content, learning activities and instructional evaluation with the development of lifelong learning skills; and the presence of educational reforms in the external environment which foster lifelong learning. These factors were incorporated into Print's Model to guide curriculum developers in designing ladderized programs.
Characterizing Boundedness in Chase Variants

Abstract Existential rules are a positive fragment of first-order logic that generalizes function-free Horn rules by allowing existentially quantified variables in rule heads. This family of languages has recently attracted significant interest in the context of ontology-mediated query answering. Forward chaining, also known as the chase, is a fundamental tool for computing universal models of knowledge bases, which consist of existential rules and facts. Several chase variants have been defined, which differ on the way they handle redundancies. A set of existential rules is bounded if it ensures the existence of a bound on the depth of the chase, independently from any set of facts. Deciding if a set of rules is bounded is an undecidable problem for all chase variants. Nevertheless, when computing universal models, knowing that a set of rules is bounded for some chase variant does not help much in practice if the bound remains unknown or even very large. Hence, we investigate the decidability of the k -boundedness problem, which asks whether the depth of the chase for a given set of rules is bounded by an integer k. We identify a general property which, when satisfied by a chase variant, leads to the decidability of k -boundedness. We then show that the main chase variants satisfy this property, namely the oblivious, semi-oblivious (aka Skolem), and restricted chase, as well as their breadth-first versions.
Self-Adaptive Optimization for Improved Data Sanitization and Restoration

Nowadays, Data Sanitization is considered as a highly demanded area for solving the issue of privacy preservation in Data mining. Data Sanitization, means that the sensitive rules given by the users with the specific modifications and then releases the modified database so that, the unauthorized users cannot access the sensitive rules. Promisingly, the confidentiality of data is ensured against the data mining methods. The ultimate goal of this paper is to build an effective sanitization algorithm for hiding the sensitive rules given by users/experts. Meanwhile, this paper concentrates on minimizing the four sanitization research challenges namely, rate of hiding failure, rate of Information loss, rate of false rule generation and degree of modification. Moreover, this paper proposes a heuristic optimization algorithm named Self-Adaptive Firefly (SAFF) algorithm to generate the small length key for data sanitization and also to adopt lossless data sanitization and restoration. The generated optimized key is used for both data sanitation as well as the data restoration process. The proposed SAFF-based algorithm is compared and examined against the other existing sanitizing algorithms like Fire Fly (FF), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution algorithm (DE) algorithms and the results have shown the excellent performance of proposed algorithm. The proposed algorithm is implemented in JAVA. The data set used are Chess, Retail, T10, and T40.
Zero Block Caching for CNN Applications Running on a Vision DSP

Digital Signal Processors (DSPs) nowadays are widely used for edge devices due to their power-efficiency in inference operations of Convolutional neural networks (CNNs). In most DSP systems, the ratio of zero-valued feature map data in a CNN is very high due to fixed-point quantization and activation functions like ReLu. This paper presents a new cache for a DSP system, zero block skip (ZBS) cache, to reduce off-chip memory access by caching the addresses of zero blocks in which all data are zero. In addition, a data remapping method is proposed for more zero block accesses to off-chip memory, which successfully increases the portion of zero blocks among all feature data blocks from 21.66% to 3S.7l% in Tiny YOLO network. However, it is observed that a ZBS cache using typical cache structure suffers from frequent capacity misses in some cases. To relieve the frequent capacity misses, zero block skip vector (ZBSV) for a group of blocks, each bit of which indicates whether a block is zero or not, is proposed. A ZBSV cache keeps the ZBSV entry for the currently accessed group. With DDR3 as an off-chip memory model, experiments show that four 2 KB ZBSV caches operate together effectively, with relatively lower SRAM power consumption. As a result, the proposed ZBSV cache reduces the off-chip memory access by 19.15% and 15.87% for Tiny YOLO and VDSR networks, respectively.
Students’ Perception On The Use Of Inferring And Predicting Chart For Learning Interpretive Reading

Interpretive Reading course is designed to enable the students to apply effective reading strategy. The inferring and predicting chart is one of the strategies that aim to train the predicting and inferring strategy by creating picture or mental image for the readers. The students’ perceptions about the chart’s can give some data on the merit and drawbacks chart application for learning reading. This descriptive qualitative study used case study method. It used interview as the instrument of data collection and  Miles and Huberman model  for data analysis. The study showed most of the students gave positive perceptions on the strategy application. The merits of using the chart were; it is quite challenging, different from traditional lecturing method, and make them learn reading systematically. The drawbacks of using the chart were the puzzlement on making use the explicit and implicit information from the text to guess the author’s meaning/intention. There should be further and more massive data collection to support and follow-up the result of the study. Further investigations about the reasons behind the students’ negative perception for the chart is also  needed to  reveal the actual reasons for the answers and find the best solutions.
Artificial Intelligence based Comparative Study of Mortality Prediction

The mortality prediction of the patient becomes an important and critical prediction problem in the area of artificial intelligence. The aim of machine learning algorithms is to help doctors to make critical decisions here. Mortality prediction can be very helpful for taking critical decisions which can help in optimising the resources available in the hospital and also an extra opinion for doctors and family members in cases of euthanasia i.e. ending life of patient to relieve pain and suffering. We have been collecting data many years from multiple sources such as E-commerce sites, government portals, hospitals and etc., now the same massive data is used as a training data for machine learning models. In this paper we are using different artificial intelligence approaches to predict the survival of a patient based on his or her medical record. In this paper we have presented a comparative study of different AI techniques including artificial neural network and widely used machine learning algorithms such as logistic regression, random forest, and support vector machine by calculating the performance for each algorithm. We observed from performance that the logistic regression perform better, hence we selected regression model for predicting the mortality of the patients.
An Optimal Consensus Node Selection Process for IoT Blockchain

Blockchain and Internet of Things (IoT) are two trending technologies, when combined together can strengthen the security of various applications. However, security of blockchain depend on its consensus mechanism. The consensus mechanisms used by cryptocurrencies requires high computations and hence cannot be applied to IoT. Applying light weight consensus such as PBFT (Practical Byzantine Fault Tolerance) requires an authority or a protocol to select leader nodes and the nodes to be involved in consensus. However the current node selection process in many blockchain applications involves a central authority or is based on traditional round robin techniques. Hence we propose simple and efficient node selection mechanism that can perform consensus without wasting energy. Our approach uses PBFT, where the nodes to participate in the consensus are not predetermined by a central authority. Nodes are selected based on their performance in the blockchain.
FSIS: Fast and Seamless Image Stitching of High-Resolution Dunhuang Murals

Digital processing of Dunhuang murals is important for the permanent and real preservation of precious cultural relics information. The image stitching is one of the key and challenge steps to obtain complete murals with high resolution. Gradient-domain methods, which need to solve a sparse linear system of the Poisson equation, are widely used in the field of image stitching. However, it is a time consuming and resource-intensive task for solving the Poisson equations, especially when the image is large. To address the above problems, a fast and seamless image stitching method called FSIS implemented on graphics processing unit (GPU) architecture is proposed to improve the image stitching efficiency of high-resolution Dunhuang murals. Specially, the original image is firstly down-sampled into low-resolution border pixels images (BPIs) by using quadrilateral grids. Next, the Poisson equations are constructed and solved based on the BPIs. Finally, the final result image is obtained by interpolation. Through using the above steps, the time and memory consumed by solving Poisson equations can be reduced. Experimental results have shown the efficiency of the proposed method.