Reviewerly/exHarmony huggingface dataset:
Case Study

This paper describes the journey of an IT program in a mid-west large research university (44,000+ students). For the past seventeen years, the program grew from nonexistence to the largest computing program on campus and the seventh largest overall. The undergraduate program evolved into an academic unit that later added a Master's program and most recently a PhD program in Information Technology. The paper documents the evolution as a case study on IT program implementation, accreditation, ranking, keeping up with the evolving IT landscape, developing graduate programs in IT, articulating the role of the IT discipline to stakeholders, defining the IT research space, managing change, and securing internal and external funding. In addition, the paper will describe key initiatives that are now positioning the program to regional, state, a possibly national leadership. Finally, the paper provides lessons learned, strategies, and recommendations.
Longest increasing subsequences in involutions avoiding patterns of length three

We study the longest increasing subsequences in random involutions that avoid the patterns of length three under the uniform probability distribution. We determine the exact and asymptotic formulas for the average length of the longest increasing subsequences for such permutation classes.
Enigma: A Hybrid Approach to File Security in Cloud

Advances in Information Technology field have replaced local storage of data with cloud storage which has become the crux of the day. As the number of people adopting storage as a service is increasing, security of personal and private information stored on cloud remains a major concern. Different approaches to achieve security have been used like encryption, file division, access control security (ACS), authentication, authorization and many more. We demonstrate ‘Enigma’: A hybrid approach to file security in cloud that outdoes all these traditional methods and provides unmatched security. In our approach, we introduce the concept of ‘jigsaw division’ which not only enhances division approach but also allows us to employ selective encryption thus combining best of both worlds. We show that our strategy provides better security and at the same time minimally hampers the retrieval time. We model our system using graph and demonstrate the use of vertex coloring algorithm for distributing file fragments across different nodes. By doing this we aim to turn the act of hacking and retrieving meaningful information into a puzzle for all the Black Hats out there!
Multi-organ Segmentation from Abdominal CT with Random Forest based Statistical Shape Model

An automatic multi-organ segmentation method from upper abdominal CT image is proposed in this paper. A group of statistical shape models for multiple organs are generated by learning the statistical distribution of organs' shapes and intensity profiles. Then, a random forest regression model is trained to find the candidate position to initialize the statistical shape model. The proposed method is evaluated at segmentation of four abdomen organs (spleen, right kidney, left kidney and liver) from training set of 26 cases of upper abdominal CT images. The accuracy shows that the initialization improves the accuracy for statistical shape model-based segmentation.
OffVid: A System for Linking Off-Topic Concepts to Topically Relevant Video Lecture Segments

We present a system for automatically connecting off-topic concepts from a video lecture to appropriate and topically relevant video lecture segments. The linked video lectures are expected to provide more detailed account of the corresponding off-topic concept. The system is realized with three modules: off-topic identification, topic base generation and segment linking. We modelled the problem of finding off-topic concept identification task as a community structure analysis in concept similarity graph. Word embedding-based technique has been used to generate topic specific video segments that act as the targets of off-topic concept connection candidates. The segmented videos are indexed using extracted topic by Solar search engine and are retrieved against queries formulated from off-topic concepts. The system has been evaluated on video lecture picked up from NPTEL MOOC platform. User study on the quality of recommendation has been found to be promising.
Connecting People Through Virtual Assistant on Google Assistant

Technology is a major driving force in our lives today. Social media platforms like Facebook, Twitter, Whatsapp, Hike etc. have taken the world by storm and have become a nonseparable part of life. Google assistant (GA) is an Artificial Intelligence (AI) based virtual assistant developed by Google. Initially, the GA lacked the feature of chatting with random people. This research paper is about developing such a feature for GA by creating an “Action on Google”. Action on Google is a custom built application that adds features to Google Assistant. This application (named as Chat Hub) connects two random people available online. People are free to chat as per their area of interest. Since GA is inbuilt into all android devices, the subscriber count is increasing significantly each day. The chat facility was introduced in GA on October 3, 2018. The objective of developing such a feature is to make GA more user friendly and benefit people by helping to find others with similar interests.
A New Data Structure for Processing Natural Language Database Queries

Natural Language Query Interfaces (NLQIs) have once again captured the public imagination, but developing them has proven to be non-trivial. One way is by using a Compositional Semantics (CS) to directly compute the answer to a query from the meanings of its parts. The query is treated as an expression of a formal language, and interpreted directly with respect to a database which provides meanings for words, which are the basic components. The meanings of compound phrases in the query, and the answer to the query itself, are computed from their constituent words and phrases using semantic rules that are applied according to the query’s syntactic structure. Montague Semantics (MS), which is a type of CS, has been used in various NLQIs previously. MS accommodates common and proper nouns, adjectives, conjunction and disjunction, intransitive and binary transitive verbs, quantifiers, and intentional and modal constructs. MS does not provide an explicit denotation for n-ary transitive verbs nor does it provide an explanation of how to handle prepositional phrases. By adding events to MS and by introducing a new data structure, transitive verbs and prepositional phrases can be accommodated as well as other NL features that are often considered to be non-compositional.
Incremental Adaptation of NMT for Professional Post-editors: A User Study

A common use of machine translation in the industry is providing initial translation hypotheses, which are later supervised and post-edited by a human expert. During this revision process, new bilingual data are continuously generated. Machine translation systems can benefit from these new data, incrementally updating the underlying models under an online learning paradigm. We conducted a user study on this scenario, for a neural machine translation system. The experimentation was carried out by professional translators, with a vast experience in machine translation post-editing. The results showed a reduction in the required amount of human effort needed when post-editing the outputs of the system, improvements in the translation quality and a positive perception of the adaptive system by the users.
A Speech Test Set of Practice Business Presentations with Additional Relevant Texts

We present a test corpus of audio recordings and transcriptions of presentations of students’ enterprises together with their slides and web-pages. The corpus is intended for evaluation of automatic speech recognition (ASR) systems, especially in conditions where the prior availability of in-domain vocabulary and named entities is benefitable. The corpus consists of 39 presentations in English, each up to 90 s long. The speakers are high school students from European countries with English as their second language. We benchmark three baseline ASR systems on the corpus and show their imperfection.
Improving CAT Tools in the Translation Workflow: New Approaches and Evaluation

This paper describes strategies to improve an existing web-based computer-aided translation (CAT) tool entitled CATaLog Online. CATaLog Online provides a post-editing environment with simple yet helpful project management tools. It offers translation suggestions from translation memories (TM), machine translation (MT), and automatic post-editing (APE) and records detailed logs of post-editing activities. To test the new approaches proposed in this paper, we carried out a user study on an English--German translation task using CATaLog Online. User feedback revealed that the users preferred using CATaLog Online over existing CAT tools in some respects, especially by selecting the output of the MT system and taking advantage of the color scheme for TM suggestions.
Robustness of Neural Networks to Parameter Quantization

Quantization, a commonly used technique to reduce the memory footprint of a neural network for edge computing, entails reducing the precision of the floating-point representation used for the parameters of the network. The impact of such rounding-off errors on the overall performance of the neural network is estimated using testing, which is not exhaustive and thus cannot be used to guarantee the safety of the model. We present a framework based on Satisfiability Modulo Theory (SMT) solvers to quantify the robustness of neural networks to parameter perturbation. To this end, we introduce notions of local and global robustness that capture the deviation in the confidence of class assignments due to parameter quantization. The robustness notions are then cast as instances of SMT problems and solved automatically using solvers, such as dReal. We demonstrate our framework on two simple Multi-Layer Perceptrons (MLP) that perform binary classification on a two-dimensional input. In addition to quantifying the robustness, we also show that Rectified Linear Unit activation results in higher robustness than linear activations for our MLPs.
Autonomous control of unmanned aerial multi-agent networks in confined spaces

In this thesis, the functionality of existing Crazyflie models is extended to include current implementations of attitude and velocity control. The models are validated both as individual agents and as a swarm through comparisons of swarm behaviour during step, ramp and frequency response testing. The models are found to accurately replicate the dynamics of the real system, but additional research on measurement noise and accuracy would be necessary to ensure that the model remains accurate in less than ideal conditions. Several suggestions for further improvements are presented. Additionally, two separate swarm controllers are implemented and tested in a simulated environment as well as the real world to demonstrate the capabilities of the model and to evaluate the controller usability in a number of practical use cases. Both controllers are found to behave well on the Crazyflie system, and demonstrate the practicality of the Crazyflie platform as well as the controllers implemented. (Less)
Ontology Learning from Clinical Practice Guidelines

In order to assist professionals and doctors to make decisions about appropriate health care for patients who are at risk of cardiovascular disease, we propose a decision support system based on OWL (Ontology Language Web) ontology with SWRL (semantic web rule language) rules. The idea consists to parse clinical practice guidelines (i.e. documents that contain recommendations and medical knowledges) to enrich and exploit existing cardiovascular domain ontology. The enrichment process is conducted by ontology learning task. We first pre-process the text and extract the relevant concepts. Then, we enrich the ontology not only by OWL DL axioms, but also SWRL rules. To identify the similarity between terms texts and ontology concepts, we have used a combination of methods as levenshtein similarity and Word2Vec.
Special issue on Semantic Deep Learning

Numerous success use cases involving deep learning have recently started to be propagated to the Semantic Web. Approaches range from utilizing structured knowledge in the training process of neural networks to enriching such architectures with ontolo
A Novel Approach of Ontology-Based Activity Segmentation and Recognition Using Pattern Discovery in Multi-resident Homes

Activity recognition is one of the preliminary steps in designing and implementing assistive services in smart homes. Such services help identify abnormality or automate events generated while occupants do, as well as intend to do, their desired activities of daily living (ADLs) inside a smart home environment. However, most of the existing systems are applied for single-resident homes. Multiple people living together create additional complexity in modeling numbers of overlapping and concurrent activities. In this paper, we introduce a hybrid mechanism between ontology-based and unsupervised machine learning strategies in creating activity models used for activity recognition in the context of multi-resident homes. On comparing with related data-driven approaches, the proposed technique is technically and practically scalable to real-world scenarios due to fast training time and easy implementation. An average activity recognition rate of 95.83% on CASAS Spring dataset was achieved and the run time per recognition operation was measured as 12.86 ms.
Chemical Named Entity Recognition with Deep Contextualized Neural Embeddings

Chemical named entity recognition (ChemNER) is a preliminary step in chemical information extraction pipelines. ChemNER has been approached using rule-based, dictionary-based, and feature-engineered based machine learning, and more recently also deep learning based methods. Traditional word-embeddings, like word2vec and Glove, are inherently problematic because they ignore the context in which an entity appears. Contextualized embeddings called embedded language models (ELMo) have been recently introduced to represent contextual information of a word in its embedding space. In this work, we quantify the impact of contextualized embeddings for ChemNER by using Bi-LSTM-CRF (bidirectional long short term memory networks - conditional random fields) networks. We benchmarked our approach using four well-known corpora for chemical named entity recognition. Our results show that incorporation of ELMo results in statistically significant improvements in F1 score in all of the tested datasets.
Network-Based Pooling for Topic Modeling on Microblog Content

Topic modeling with tweets is difficult due to the short and informal nature of the texts. Tweet-pooling (aggregation of tweets into longer documents prior to training) has been shown to improve model outputs, but performance varies depending on the pooling scheme and data set used. Here we investigate a new tweet-pooling method based on network structures associated with Twitter content. Using a standard formulation of the well-known Latent Dirichlet Allocation (LDA) topic model, we trained various models using different tweet-pooling schemes on three diverse Twitter datasets. Tweet-pooling schemes were created based on mention/reply relationships between tweets and Twitter users, with several (non-networked) established methods also tested as a comparison. Results show that pooling tweets using network information gives better topic coherence and clustering performance than other pooling schemes, on the majority of datasets tested. Our findings contribute to an improved methodology for topic modeling with Twitter content.
Application of alphazzle writing for basic school children base on android

Abstract The education process is one of the human activities in which the motivational function in the education process is to generate encouragement to do activities in education. Activity can produce changes in cognitive, psychomotor and affective students. This applies to every learning in the teaching and learning process. Student learning success is determined by several factors that support the success of the teaching-learning process, including the existence of innovative learning techniques in this case is learning to write. Android games are an alternative software that is chosen, with the reason Android tends to be used on mobile drivers such as smartphones and tablet PCs. In the current technological development, children tend to be more familiar with various gadgets, even children under five are now exposed to various gadgets. With the existence of android games and supported by attractive features, it is expected that children know computer technology, lessons for following directions and rules, problem solving and logic exercises, motoric nerve training and spatial skills, can help parents to teach their children in a fun way.
Data Mining Techniques for Early Detection of Breast Cancer

Nowadays, millions of people around the world are living with the diagnosis of cancer, so it is very important to investigate some forms of detection and prevention of this disease. In this paper, we will use an ensemble technique with some data mining algorithms applied to a dataset related to the diagnosis of breast cancer using biological markers found in routine blood tests, in order to diagnose this disease. From the results obtained, it can be verified that the model got an AUC of 95% and a precision of 87%. Thus, through this model it is possible to create new screening tools to assist doctors and prevent healthy patients from having to undergo invasive examinations.
Preliminary Report: Archaeology Education in Southeast Asia

This report presents the preliminary results of the SEAMEO SPAFA Survey on Archaeology Education in Southeast Asia which was conducted online from September to December 2018. The aim of the survey was to understand the archaeology education landscape in Southeast Asia and identify the current needs in archaeology education and skills training. 330 people responded to the survey, which was available in multiple languages. These initial results outline where archaeologists in the region studied archaeology; public perceptions of archaeology education in the region; an overview of the archaeology profession and industry and the main training needs identified by those studying or working in Southeast Asian archaeology today.
ESLI: Enhancing slope one recommendation through local information embedding

Slope one is a popular recommendation algorithm due to its simplicity and high efficiency for sparse data. However, it often suffers from under-fitting since the global information of all relevant users/items are considered. In this paper, we propose a new scheme called enhanced slope one recommendation through local information embedding. First, we employ clustering algorithms to obtain the user clusters as well as item clusters to represent local information. Second, we predict ratings using the local information of users and items in the same cluster. The local information can detect strong localized associations shared within clusters. Third, we design different fusion approaches based on the local information embedding. In this way, both under-fitting and over-fitting problems are alleviated. Experiment results on the real datasets show that our approaches defeats slope one in terms of both mean absolute error and root mean square error.
Accounting for Object Weight in Interaction Design for Virtual Reality

Interaction design for virtual reality (VR) rarely takes the weight of an object -let alone its moment of inertiainto account.This clearly reduces user immersion and could lead to a break-in-presence.In this work, we propose methods for providing a higher fidelity in interactions with virtual objects.Specifically, we present different methods for picking up, handling, swinging, and throwing objects based on their weight, size, and affordances.We conduct user studies in order to gauge the differences in performance as well as sense of presence of the proposed techniques compared to conventional interaction techniques.While these methods all rely on the use of unmodified VR controllers, we also investigate the difference between using controllers to simulate a baseball bat and swinging a real baseball bat.Interestingly, we find that realism of the motions during interaction is not necessarily an important concern for all users.Our modified interaction techniques, however, have the ability to push user performance towards the slower motions that we observe when a real bat is used instead of a VR controller on its own.
A Study on Relative Importance of Service Convenience in the Convenience Store Using AHP : Gap Analysis between Consumers and Store Owners

[Purpose] It aims to suggest the differential factors to enhance the customer satisfaction and to activate the shopping in the convenience store (CVS). [Methodology] Convenience store service convenience (CVS SERVCON) was developed at the first time and the gap of relative importance level between the owners and the consumers was assessed by AHP analysis method..[Findings] First, the importance of access convenience was assessed as the first priority in both CVS owners’ group and the consumers’ group, while the detailed elements showed the differences which required the improvements. Based on the consumers’ assessment, the importance of location was relatively low. They assessed relatively high on 24 hours operation of CVS and personal services. Second with respect to the transaction convenience, CVS owners’ group rated it as the fourth priority while the consumers’ group rated as the second, which requires the improvement. Third in terms of benefit utility and value-added service of post-benefit convenience which are the sub-factors of benefit convenience, the rate on the importance from the consumers showed higher than that from the owners, which requires the improvement. [Implications] Based on this study results, CVS operating companies are anticipated to be able to satisfy the customers, moreover, to secure the loyal customers if they concentrate the resources to develop the differentiation strategy on the areas where the consumers’ demands are high while performs the maintenance strategy on the areas where their demands are low. In addition, this study contributed the theoretical expansion of SERVCON upon development of CVS SERVCON to comply with its definition.
Using Human Objects for Illumination Estimation and Shadow Generation in Outdoor Environments

In computer graphics and augmented reality applications, the illumination information in an outdoor environment enables us to generate a realistic shadow for a virtual object. This paper presents a method by which to estimate the illumination information using a human object in a scene. A Gaussian mixture model, in which the mixtures of Gaussian distributions are symmetrical, is employed to learn the background. The human object is then segmented from the input images and the disparity map obtained by a stereo camera. The ground plane in the scene, which is important for estimating the location of the human object on the ground, is then detected using the v-disparity map. The altitude and the azimuth value of the sun are computed from the geometric relationship of three scene elements: the ground, human object, and human-shadow region. The experimental results showed that the proposed method can estimate the sun information accurately and generate a shadow in the scene for a virtual object.