Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence and Commonsense

The use and development of artificial intelligence (AI) capabilities in a company’s production environment is critical to improving assembly process times and product quality. Manufacturing processes are complex and require highly skilled operators to build quality products. Production processes and engineering designs are even more complex and new methods must be employed to address these complexities. AI technologies hold promise to address these complexities using ‘commonsense’ knowledge (CSK) tools. Implementing and using CSK capabilities has accelerated the growth of AI applications in industry. The development of AI capabilities has been a slow and painstaking process in its attempt to fully mimic the capabilities of humans. However, there is much work to be done to duplicate human process capabilities in an AI system. As new technologies are developed and made available to the design and development engineers, the acceleration and growth of AI capable systems will grow exponentially.
Context-Aware Crosslingual Mapping

Cross-lingual word vectors are typically obtained by fitting an orthogonal matrix that maps the entries of a bilingual dictionary from a source to a target vector space. Word vectors, however, are most commonly used for sentence or document-level representations that are calculated as the weighted average of word embeddings. In this paper, we propose an alternative to word-level mapping that better reflects sentence-level cross-lingual similarity. We incorporate context in the transformation matrix by directly mapping the averaged embeddings of aligned sentences in a parallel corpus. We also implement cross-lingual mapping of deep contextualized word embeddings using parallel sentences with word alignments. In our experiments, both approaches resulted in cross-lingual sentence embeddings that outperformed context-independent word mapping in sentence translation retrieval. Furthermore, the sentence-level transformation could be used for word-level mapping without loss in word translation quality.
Deformable registration of 3D ultrasound volumes using automatic landmark generation

US image registration is an important task e.g. in Computer Aided Surgery. Due to tissue deformation occurring between pre-operative and interventional images often deformable registration is necessary. We present a registration method focused on surface structures (i.e. saliencies) of soft tissues like organ capsules or vessels. The main concept follows the idea of representative landmarks (so called leading points). These landmarks represent saliencies in each image in a certain region of interest. The determination of deformation was based on a geometric model assuming that saliencies could locally be described by planes. These planes were calculated from the landmarks using two dimensional linear regression. Once corresponding regions in both images were found, a displacement vector field representing the local deformation was computed. Finally, the deformed image was warped to match the pre-operative image. For error calculation we used a phantom representing the urinary bladder and the prostate. The phantom could be deformed to mimic tissue deformation. Error calculation was done using corresponding landmarks in both images. The resulting target registration error of this procedure amounted to 1.63 mm. With respect to patient data a full deformable registration was performed on two 3D-US images of the abdomen. The resulting mean distance error was 2.10 ± 0.66 mm compared to an error of 2.75 ± 0.57 mm from a simple rigid registration. A two-sided paired t-test showed a p-value < 0.001. We conclude that the method improves the results of the rigid registration considerably. Provided an appropriate choice of the filter there are many possible fields of applications.
Examining Microscopic Organisms under Augmented Reality Microscope: A 5E Learning Model Lesson

As a fundamental strategy for all science curriculum, inquiry is of prime importance. In order to facilitate inquiry during science education in middle school, 5E learning model was applied in this...
Are We Agile or Not? A Survey on Brazilian Software Processes

How many software practitioners use agile methods in Brazil? We currently have little knowledge about Brazilian developers profile and about the software processes applied. One of the issues that remain unanswered is whether these practitioners are using agile software processes or not. With the aim to start filling this gap, we conducted a study with the objective to identify whether Brazilian software practitioners are agile. Our research approach was the survey as the method for collecting data. We applied a clustering algorithm to analyze data and characterize the software process, and text mining techniques to identify respondents perceptions of their software processes. Our results show a preliminary profile for Brazilian software processes and practitioners positive and negative perceptions about these processes. We contribute with a method to characterize agile, traditional and hybrid software processes.
Quantum process randomness

We argue that the optimization of a quantum process based on the customarily used fidelity between the actual and the expected outputs may not be sufficient. We introduce a quantity namely the “quantum process randomness”, which is the standard deviation of the distribution of Born probabilities corresponding to the orthonormal measurement that projects the expected output onto the eigenstates of the actual output, and obtain a trade-off between fidelity and randomness for an arbitrary quantum process. We consider approximate quantum cloning and teleportation processes to illustrate the concept. We find that the optimal approximate universal and state-dependent quantum cloning machines obtained by maximizing the fidelity is the same as that obtained by optimizing the difference between randomness and fidelity.
A New Tool for Identifying Unconformities During Exploration Drilling

Abstract A powerful new tool for unconformity identification in a range of geological environments is presented together with very strong evidence of its utility. Commonly in an exploration setting correct sequence interpretation has taken years and multiple detailed studies, now with the new tool it can be done quite easily in near real time. Recognition of unconformities in boreholes, particularly where correlation with outcrop is not available, traditionally relies on paleontological methods, normally palynology or micropalaeontology and correlations between wells where sections of the observed sequence are missing. Observations in recently drilled wells in Dubai have provided evidence for another useful tool. While drilling Well A, bulk rock phosphate concentrations were obtained in near real time while drilling using X-ray fluorescence (XRF). These were then plotted against well depth. Phosphate values were taken as indicators of long duration and high intensity of organic production or conversely a low rate of sedimentation. Unconformities were marked by significant and obvious phosphate peaks while drilling in a marine sequence. Higher than average concentration of phosphates in marine environments during periods of non-deposition or very slow deposition have been known for some time but their use as markers for unconformities while drilling has not been widespread due to the practical difficulties with sample analysis. With advances in XRF technology routine wellsite XRF analysis services are now available. Plots of phosphate concentrations in Well B which was drilled through a sub-aerially deposited sequence also showed phosphate peaks, some of which correlated with known and recognisable unconformity surfaces. Further evaluation, particularly comparison with palynology data, showed that the phosphate peaks which did not correlate with known unconformities indicated previously unrecognised unconformities. Phosphate peaks on unconformity surfaces in sub-aerially deposited sequences have not, as far as the authors can determine, been previously recognised. Well C is an older well which penetrated a similar sub-aerially deposited sequence to Well B with no XRD analyses available. Correct interpretation of the Well C sequence was not possible until the key points were derived from the more complete Well B data. Evidence is presented showing that phosphate peaks are practical and useful indicators of unconformities in near real time, especially when interpreted with other geological information. An example is also given of an unconformity which displays no phosphate peak together with an explanation as to why there is no peak. In an exploration setting analysis of phosphate trends can significantly enhance and simplify sequence and palaeoenvironmental interpretation and understanding of regional tectonics thus providing greater insight when planning follow up wells leading to a higher success rate. As such it is a new and novel exploration tool with a potentially high economic value.
Design of Questioning Strategies for Knowledge-Based Systems

This chapter describes a new method of generating questions for knowledge-based systems dealing with multiple contexts. This has been accompanied by the description of the concrete design of a questioning strategy in the Clinaid knowledge-based system. Two strategies of Clinaid for generating questions were compared: one based on signs/symptoms only, and the other using the additional structural information provided by introducing syndromes. The purpose of the questioning strategy is to ask the best questions and find the best inference path with respect to the information as early as possible during the expert system’s reasoning cycle. In most medical expert systems dealing with diagnoses, the information which the users of the system have initially given to the system is not sufficient for the expert system to make the ultimate relevant conclusions. During the inference process, the expert system has to acquire some additional information from the user if, and when, it is required.
Foot Location Algorithm considering Geometric Constraints of Façade Cleaning

The purpose of this study is to propose a foot location algorithm for a glass façade cleaning robot. As an initial stage of this study, we set three assumptions regarding aimed module robot and window shape. These assumptions set the system some geometric constraint conditions: Right edge of the right cleaning unit moves along with the line of the right window frame. The cleaning area of right and left cleaning unit overlaps a little. The module robot walks with a step at a time. From the conditions, a foot location determination algorithm is constructed corresponding to four situations. The effectiveness of the algorithm is verified through a numerical simulation.
Progressive and hierarchical share-in-share scheme over cloud

Recently sharing of secret information (image) before dissemination is gaining popularity as a means of reducing the vulnerability against the wide attacking surface on cloud computing. An integrated scheme comprising of secret image sharing (SIS) and data hiding is suggested here that offers progressive quality access control of the decoded images. This scheme is a (t,k,n)-hierarchical SIS technique that uses affine Boolean classification and compressed sensing (CS) in the share generation process. Prediction error representation of the secret image in spatial domain is explored to make the signal more sparse leading to CS reconstruction from less number of measurements. In the decoding process, an improved quality of the secret image is achieved with the involvement of ‘t’ essential stego shares (ESSs) and ‘k-t’ non-essential stego shares (NSSs) over ‘t’ ESSs and ‘n-k’ general shares (GSs). A large set of results illustrate the merits of the proposed method in terms of hierarchical access structure, flexible shadow size, progressive quality access on the decoded image and robustness against varieties of data manipulation.
Stochastic Methods to Find Maximum Likelihood for Spam E-mail Classification

The increasing volume of unsolicited bulk e-mails leads to the need for reliable stochastic spam detection methods for the classification of the received sequence of e-mails. When a sequence of emails is received by a recipient during a time period, the spam filters have already classified them as spam or not spam. Due to the dynamic nature of the spam, there might be emails marked as not spam but are actually real spams and vice versa. For the sake of security, it is important to be able to detect real spam emails. This paper utilizes stochastic methods to refine the preliminary spam detection and to find maximum likelihood for spam e-mail classification. The method is based on the Bayesian theorem, hidden Markov model (HMM), and the Viterbi algorithm.
Extracting Access Patterns with Hierarchical Latent Tree Analysis: An Empirical Study on an Undergraduate Programming Course

Students commonly access learning resources with online systems nowadays. The access logs of those learning resources potentially provide rich information for course instructors to understand students. However, when there are many learning resources, the insights on students could be obscured by the large amount of unprocessed log data in its raw form. In this paper, we propose to extract the access patterns using a recently proposed method for topic modeling. The method, named hierarchical latent tree analysis, can capture co-occurrences of access to learning resources and group related learning resources together. We further show a way to consider the access periods during a course from the log data to reflect different behaviors of the students. We empirically test the proposed method using the log data captured in an undergraduate programming course with 63 students. We evaluate the performance of the proposed method based on the task for predicting students' scores. The regression model built on the features extracted by the proposed method shows significantly better performance over three baseline models. We further demonstrate meaningful insights on the student behaviors can be obtained from the extracted access patterns.
Finding Your Voice

This chapter aims to provide an indispensable introduction to content marketing based on industry best-practices and help academic libraries navigate this essential but often overlooked marketing practice. The chapter will begin by addressing some of the consistent challenges organizations have starting their social media marketing campaigns and developing a social media strategy. Next, the chapter will focus on defining the tone and voice of their social media messages. Then, it will discuss sustaining the campaign by curating content and avoiding “content fatigue.” Finally, the authors share an example of how an academic library but them into practice. They are confident this chapter will give academic librarians the vocabulary and techniques they need to talk and walk their way through meaningful and engaging marketing campaigns for their libraries using social media.
Online corpus of spoken Ilokano language

There has been a great effort in the collection of different languages in the past years all over the world, and the development of online corpus outside the country brought new possibilities in the Philippines. However, there is a limited resource for the Ilokano Language. This paper introduces the Corpus of Spoken Ilokano Language, an online repository of spoken Ilokano in the Philippines specifically in region 1. The main component of this study is spoken Ilokano. It has been specifically built for natural language processing. It shows the difference of Ilokano language as spoken by Ilokanos in the region. The database consists of 160 speakers, 40 speakers in each province of the region, each speaking about 74 statements. Spoken Ilokano language was audio recorded and transcribed. A web application has been developed making the dataset available online. The corpus was validated to provide a useful resource of data that can be used for automatic speech recognition models.
An Extended Data as a Service Description Model for Ensuring Cloud Platform Portability

Nowadays there is a myriad of different Cloud services deployed in datacenters around the world. These services are presented by means of models offered as services on demand (SaaS, PaaS and IaaS). At a different level, the companies aim at finding some services that manage their data in order to enhance their process and increase their benefits. The massive quantity of data and the heterogeneous types of data deployed in the Cloud made the discovery task much important. In addition, one big problem in discovery task is the absence of a description model to represent data services (Data as a service; DaaS). Moreover, the location of services made the selection and composition process much very hard, especially for functional and non-functional parameters. In this paper, we plan to solve these problems by introducing an extended description model for data services, in order to reduce the number of services during the selection and composition process. The implementation shows the effectiveness of the proposed model.
The Design of an Interactive Surface for Supporting Rehabilitation of Children with Developmental Coordination Disorder

One key concept of Tangible User Interaction is interactive surfaces, in which we transform a surface into an active interface between the physical and virtual world. In this paper we describe the design process of a novel interactive surface that can be used for movement therapy for children with Developmental Coordination Disorder (DCD). Children with DCD suffer from impairment in motor development which influences their overall movement quality and affects their daily life. Traditionally, the rehabilitation techniques and tools are often static, non-interactive, monotonous and unappealing to children. The purpose of this study is to design an interactive surface that combines physical motor exercises with digital games. Through an iterative co-creation process with patients and physiotherapists, we developed "Matti" as an interactive gaming mat to increase the motivation of the children during their rehabilitation, by linking the therapy exercises with digital games thus providing more engagement and better results.
Research on the remote sensing prospecting model for typical uranium mining area along the Sino-Russian economic corridor

By leveraging the remote sensing technology, we have delved into the remote sensing prospecting model for typical uranium mining area along the Sino-Russian Economic Corridor. According to the remote sensing interpretation results of the basic geology along the Sino-Russian Economic Corridor, we have analyzed and summarized the remote sensing geological features of the structure, stratum and rock mass in this area. Moreover, by providing the remote sensing interpretation of basic geology in the Russian Streltsovsky typical hydrothermal uranium mining area based on the data of the domestic GF-1 satellite, we have made conclusive analysis on the characteristics of its ore-controlling elements and preliminarily summarized the remote sensing geological prospecting model for typical volcanic hydrothermal uranium deposits in this area. By using the remote sensing prospecting model, we have delineated three favorable ore-forming sections, which provides an important basis for regional mineralization prediction and the delineation of favorable ore-forming areas, and helps seek such deposits in the same or similar areas.
An Evaluation of Recent Local Image Descriptors for Real-World Applications of Image Matching

This paper discusses and compares the best and most recent local descriptors, evaluating them on increasingly complex image matching tasks, encompassing planar and non-planar scenarios under severe viewpoint changes. This evaluation, aimed at assessing descriptor suitability for real-world applications, leverages the concept of approximated overlap error as a means to naturally extend to non-planar scenes the standard metric used for planar scenes. According to the evaluation results, most descriptors exhibit a gradual performance degradation in the transition from planar to non-planar scenes. The best descriptors are those capable of capturing well not only the local image context, but also the global scene structure. Data-driven approaches are shown to have reached the matching robustness and accuracy of the best hand-crafted descriptors.
Effectiveness of Krisoker Janala in Disseminating Agricultural Information: An Innovative Tool

Krisoker Janala (Farmers Window) is a newly develop app that is used by the agricultural extension agent of Bangladesh to spread information to the farmers.In this regard, the objectives of this study were to assess the effectiveness of Krisoker Janala for disseminating agricultural information and to explore the contributory factors of using Krisoker Janala.Data were collected from 106 Sub Assistant Agriculture Officers (SAAOs) under four upazilas (sub district) of Chuadanga district.A face to face interview followed by structured questionnaire was conducted to collect data from the SAAOs during a period of early June to the end of July, 2018.The result of interview revealed that 64.2% of the respondents perceived that use of Krisoker Janala is moderately effective while 24.5% and 11.3% of them perceived as less and high effective respectively.Multiple regression analysis was applied to identify significant factors and it was found that out of seven factors, three namely extent of ICT use, the information quality of Krisoker Janala and use of Krisoker Janala had significant positive contribution to their perceived effectiveness of Krisoker Janala for disseminating agricultural information.So, the policy makers should consider to these important factors in order to increase the effectiveness of this agro based app as well as for providing better service to the farmers.
Constructing Language Models from Online Forms to Aid Better Document Representation for More Effective Clustering

Clustering is the practice of finding tacit patterns in datasets by grouping the corpus by similarity. When clustering documents this is achieved by converting the corpus into a numeric format and applying clustering techniques to this new format. Values are assigned to terms based on their frequency within a particular document, against their general occurrence in the corpus. One obstacle in achieving this aim is as a result of the polysemic nature of terms. That is words having multiple meanings; each intended meaning only being discernible when examining the context in which they are used. Thus, disambiguating the intended meaning of a term can greatly improve the efficacy of a clustering algorithm. One approach to achieve this end has been done through the creation of an ontology - Wordnet, which can act as a look-up as to the intended meaning of a term. Wordnet however, is a static source and does not keep pace with the changing nature of language. The aim of this paper is to show that while Wordnet can be affective, however it is static in nature and thus does not capture some contemporary usage of terms. Particularly when the dataset is taken from online conversation forums, who would not be structured in a standard document format. Our proposed solution involves using Reddit as a contemporary source which moves with new trends in word usage. To better illustrate this point we cluster comments found in online threads such as Reddit and compare the efficacy of different representations of these document sets.