Reviewerly/exHarmony huggingface dataset:
Computational Science, Convergence Culture, and the Creation of Archaeological Knowledge and Understanding

The NSF-grant funded, 3D Philadelphia Project—Digital Restoration of Thin-Shell Objects for Historical Archeological Research and Interpretation (NSF no. 0803670), brought together computer scientists, a computer engineer, a media researcher, and archaeologists to work on computer vision technologies in aid of a vast assemblage of mass-produced, industrial age ceramics excavated from Independence National Historical Park—a prominent, urban, cultural history unit within the US Department of the Interior National Park Service. The overall research aims for the project involved community participation, collaboration, and outreach beyond both the federal agency and the field of archaeology to advance development of novel computer vision technologies that would allow machine-based reconstructions of 3D objects. Such technology promised to be a boon for archaeologists in terms of collections management and interpretation needs—once fully developed and accessible. But in the short term, it directly contributed to other research communities active in the Philadelphia, Pennsylvania area: a team of computer scientists and engineers and a team of media arts researchers. For the media arts community, the computational image data produced in the collaborative study could be leveraged as digital assets useful for populating virtual environments being developed as academic research and training projects. For the computer scientists and engineers, the research engaged with artifact shapes and designs useful for their needs of writing mathematical algorithms central to computer vision and pattern recognition development needed for Artificial Intelligence (AI) and machine-learning (ML) (namely decision tree learning, inductive logic programming, clustering, reinforcement learning, and Bayesian networks used in mimicking of the biological structure of the brain). The project was not something done in collaboration as we (archaeologists) knew it, but rather represented the emergence of a new creative space created beyond the realms of all the participants’ disciplinary (and federal agency) silos. The resulting entirely new community space was possible only through non-archaeologist participation in the planning and carrying out of the research project’s design. Here we examine the interplay of these differing, contributing, knowledge communities in a case study in heritage studies that both refines our understanding of the concept of heritage while reminding us of the necessity and benefits of sharing archaeological knowledge through engagement with “others.” We draw upon Pierre Lévy’s ideas of “Collective Intelligence” and Henry Jenkins’ notion of “Convergence Culture” to explore the relationships, communications, learning, and opportunities that evolved in this “collaboration” between archaeologists and non-archaeologists applying computer vision technology to cultural heritage.
ResumeVis: Interactive Correlation Explorer of Resumes

Resumes are critical for individuals to find jobs and for HR to select staffs. Almost all key events about career and demographic information are recorded on the resumes, which are commercial secrets. Too many valuable patterns can be explored from resumes. In this paper, we collect 372,829 Chinese resumes with complete attributes, such as income and Chinese ID number. Then a system called ResumeVis is developed to explore the correlations among the attributes. In the visualization system, we propose a new correlation representation – parallel coordinates with multi-valued attributes to adapt to the characteristics of resumes. Besides, user-friendly interactions, such as filter elements, reorder attributes, brushing and linking, are integrated to provide an easy-use interface. Lots of patterns and inspiring results can be found by using the system. Three case studies are illustrated as examples to validate the system usability. The system can be used as a recommendation system for job seekers and HRs.
Region of interest detection based on visual saliency analysis and iteratively clustering for remote sensing images

Saliency analysis is essential to detect common regions of interest (ROI) in remote sensing images. However, many methods imply saliency analysis in single images and cannot detect common ROI accurately. In this paper, we propose the joint saliency analysis based on iterative clustering (JSIC) method to detect common ROIs. Firstly, the size of superpixel patch is adaptively determined by texture feature. Secondly, color feature and intensity feature are utilized to get initial saliency maps and Otsu is utilized to obtain initial ROIs. Finally, iterative clustering is applied to obtain final ROI with less background inference. Quantitative and qualitative experiments results show that the iterative clustering joint saliency analysis method not only has better performance when compared to the other state-of-the-art methods, but also can eliminate image without ROI. Our contributions lie in three aspects as follows: 1) We propose a novel method to calculate the number of superpixel blocks adaptively. 2) A new joint saliency analysis method is proposed based on color feature and intensity feature. 3) We propose a novel saliency modification strategy based on the iterative cluster, which could reduce the background inference and eliminate images without ROIs.
DEEM 2019

The DEEM workshop brings together researchers and practitioners at the intersection of applied machine learning, data management and systems research, with the goal to discuss the arising data management issues in machine learning application scenarios.
Indirect Reports and Translation

There are similarities (and also differences) between the practice of indirect reporting and translation. Their similarities and differences are important especially when it comes to issues such as translating/reporting slurring as well as the complicated topic of accountability in reporting/translating. Although there are more limitations in translation than indirect reporting, new developments in translation studies have provided the translator with a lot of freedom to make subjective changes based on sociocultural features. In this chapter, it is also argued that indirect reporting and translating are rational procedures that should meet four requirements: verifiability, plausibility, situational adequacy, and value-orientedness. Moreover, the challenging case of Paraphrasis/Form Principle is discussed. It is also shown that, based on Goffman’s theory of dramaturgy, indirect reporting is more relevant and representative of the idea of ‘masking’ since it is more immediate and dialogic than translating. In this chapter, the role of the hearer/reader is discussed and it is shown that the hearer/reader has an important effect on the way the reporter/translator exerts changes to the original utterances. In the end, the neglected case of ‘non-verbal communication’ is covered and it is argued that both the translator and indirect reporter should take non-verbal communication into account during reporting/translating.
How to be a maker: Have a ball

The only thing better than a mirror ball is one that automatically starts turning when it hears music, says Hannah Joshua
The parameter estimation of conditional intensity function temporal point process as renewal process using Bayesian method and its application on the data of earthquake in East Nusa Tenggara

Abstract A temporal point process is a one-dimensional point process where events are observed by time. A temporal point process can be viewed as a renewal process with time between events as random variables. The conditional intensity function of the process represents the expected rate dependent on time between events so that we can predict earthquakes activities. This research will be discussed about parameters estimation of conditional intensity function using Bayesian method and be applied to earthquake data in East Nusa Tenggara (ENT) from January 2015 to April 2018. The ENT is categorized as earthquake prone, because it is flanked by two zones of earthquakes. In southern region there are the Eurasian and Indo-Australian plates sub ducted, and in the north there is a back arc thrust. Based on the research results, it was obtained the conditional intensity estimation. The application of conditional intensity estimation represents the expected rate of earthquake occurrence in East Nusa Tenggara at a certain time interval. The time intervals are in units of days. The greatest conditional intensity is at the time interval (day 1096 th to 11897 th ) that is 0.058798617. It means that the probability earthquake occurrence in East Nusa Tenggara at that time period is very low with the total number of occurrences are 28 earthquakes.
Telekinetic Thumb Summons Out-of-reach Touch Interface Beneath Your Thumbtip (demo)

As personal interactive devices become more ingrained into our daily lives, it becomes more important to understand how seamless interaction with those devices can be fostered. A typical mechanism to interface with a personal device is via a touch screen, in which users use their fingertip or stylus to scroll, type, select, or otherwise control device usage. Touch-based techniques, however, can become restrictive or inconvenient under a variety of scenarios. For example, personal devices such as phones or tablets are continuously increasing in size, making one-handed interaction difficult because one cannot easily hold the phone and touch the screen (with the thumb) at the same time with one hand. Therefore, in this demo, we present a new technique to interact with personal devices in which the screen and touch screen interactions can adapt to a user's grip or current touch constraints.
Using Adaptive Novelty Search in Differential Evolution

Novelty search ensures evaluation of solutions in stochastic population-based nature-inspired algorithms according to additional measure, where each solution is evaluated by a distance to its neighborhood beside the fitness function. Thus, the population diversity is preserved that is a prerequisite for the open-ended evolution in evolutionary robotics. Recently, the Novelty search was applied for solving the global optimization into differential evolution, where all Novelty search parameters remain unchanged during the run. The novelty area width parameter, that determines the diameter specifying the minimum change in each direction needed the solution for treating as the novelty, has a crucial influence on the optimization results. In this study, this parameter was adapted during the evolutionary process. The proposed self-adaptive differential evolution using the adaptive Novelty search were applied for solving the CEC 2014 Benchmark function suite, and the obtained results confirmed the usefulness of the adaptation.
Exact distribution of the non-central Wilks’s statistic of the second kind

We derive the exact expressions of the density of Wilks’s non-central statistic of the second kind in the linear and planar cases. Those expressions are given in rapidly converging infinite series and can be used for numerical computation.
THE PROBLEM OF LEARNING IN ENGLISH LISTENING COMPREHENSION AT THE SECOND GRADE STUDENTS OF SMA N 8 BATANG HARI ACADEMIC YEAR 2017/2018

This study was intended to find out the s\students problem of learning English listening. The research method used qualitative method and questionnaire design. The population of this study was second grade students of SMA N 8 BATANGHARI JAMBI which consisted of 5 classes with 152 students divided into 69 men and 83 female. Tthe researcher only took randomly 5 class XI MIA 1, 5 class XI MIA 2, 5 class XI IIS 1 , 5 class IIS 2, 5 class IIS 3. The data of this research was collected by giving the questionnaire to students as the sample. The collected data were analyzed by using likert scale. After conducting the research, the researcher found that the problems in listening SMAN 8 as follows: 1) Difficult when students listen to the text just one time students need to practice their listening skills to capture text ideas, 2) listeners also need to take notes while listening to long listening texts. Students often ask to stop recording where there is a sound coming from the corridor or from another classroom. They feel more comfortable if the listening test is done in some places dedicated to listening like lab listening. Listening is one of the important skills in learning English. In addition, there are also three other components such as speaking, listening and writing. For students, it is recommended that they should improve their listening skills on time, so that students can perform tasks easily and correctly in the classroom.
Spoiler Detection from Review Comments using Story Documents

Users' review comments in shopping sites are useful for other users to decide whether or not buy the item. While users' comments or opinions are included in the reviews, descriptions about story contents are sometimes included in the reviews toward items with story like novels or movies. In some cases, these descriptions may spoil reader's or viewer's enjoyment and excitement. Hereinafter, we call these descriptions spoilers. Spoilers might be related to the position in the story line. In this study we use story documents that record all of the details of the given story. Using the story documents, we investigate the location to which the content of the spoilers correspond in the story documents. Based on the result of the investigation, we develop a method for detecting spoilers in users' review comments. We compared our proposed method with some baselines that detect spoilers using machine learning techniques with bag of words model. We found that our method performs as well as the baseline. This means that we can detect spoilers in the same level of precision even if we do not have labeled data on spoilers.
Weight Adjusting Algorithm Based on Sensor-space-time Quantum for Air-Information Fusion Precision Evaluation

Abstract The precision index in the performance test of air-information fusion system is one of the important indexes, which benchmark to be determined in complex environment is difficult. This paper proposes a space-time weight adjusting algorithm for the air-information fusion system to solve this problem, as follows: The first is to build the weight priority by the magnitude of radar detection precision; the second is to find the sets of the radars’ time domain that play a dominant role during fusing; the third is to calculate the time cumulate value of radar precision; the fourth is to use the time cumulate value of radar precision and fusion track to allocate all the radar precision weights, and calculate the reference precision by weighting the radar detection data precision; the last is to evaluate the precision index according to fuzzy evaluation method. This algorithm applies to the precision evaluation of the air-information fusion system under the static simulation or dynamic flight conditions with good adaptability and credibility. The simulation shows its value.
Analyzing Experiences with Active Objects in Interaction-Based Educational Activities

In this paper, some use cases of active objects in educational activities with tangible interaction (TI) are reviewed. As part of this review, several criteria are defined to characterize and compare the experiences being analyzed. These criteria are focused on the possibilities for active objects within the educational experience with TI. Criteria were grouped into 4 categories: A) overviews, B) TI-related aspects, C) tangible user interface (TUI) design-related aspects, and D) educational and methodological aspects. The relations between the categories proposed are analyzed, and the results show that the use of active objects helps develop new learning experiences, taking advantage of visual, auditory, and haptic feedback, and encourages students to participate in activities.
Top 10 Mobile Apps To Support LIS Students' Learning

This study intends to explore the use of mobile applications used by library diploma students, Universitas Airlangga in supporting their learning process. In this study, the 132 respondents were first, second, and third year of library diploma students who were willing to fill out questionnaires, taken from 200 active library diploma students. Online questionnaires were distributed for 2 weeks in February 2019, where students filled out online closed-ended questionnaires. The researcher also conducted in-depth interviews with several students to explore further data. There are 6 questions given, namely the type of smartphone used, the amount of memory and storage on the smartphone, the number of installed applications, applications that help in lectures, and application functions in supporting lectures. The most widely used applications are those which enable them to share information, communicate, and collaborate. Information sharing in the form of documents, videos, images, and text is mostly done by students through Whatsapp, Line, and Gmail. The mobile facebook application is used by students and students use this facebook group to monitor the tasks and information from lecturers and the department administrators. Mobile applications that are widely downloaded are translate applications and dictionaries; there are 3 applications that are commonly downloaded by students, namely Google translate, Kamusku, and KBBI (Indonesian Dictionary).
Fuzzy Rule-Based Classification with Hypersphere Information Granules

Fuzzy rule-based classification has been studied by a number of classification architectures. In this study, hypersphere information granules are used to form initial fuzzy classification model in an intuitive and interpretative way. The principle of justifiable granularity offers a certain way to optimizing information granules while facing the coverage and specificity criteria. By engaging a synergy of the principle of justifiable granularity and migrating prototypes, the refined classification model is constructed for better classification performance. A series of experiments concerning synthetic datasets and comparative studies are also implemented to exhibit the feasibility and effectiveness of the proposed classification method.
Optical quality metrics for image restoration

Image restoration is a process used to remove blur (from different sources like object motion or aberrations) from images by either non-blind or blind-deconvolution. The metrics commonly used to quantify the restoration process are peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Often only a small sample of test images are used (like Lena or the camera guy). In optical design research PSNR and SSIM are not normally used, here image quality metrics based on linear system theory (e.g. modulation transfer function, MTF) are used to quantify optical errors like spherical or chromatic aberration. In this article we investigate how different image restoration algorithms can be quantified by applying image quality metrics. We start with synthetic image data that is used in camera test stands (e.g. Siemens star etc.), apply two different spatially variant degradation algorithms, and restore the original image by a direct method (Wiener filtering within sub-images), and by an iterative method (alternating direction method of multipliers, ADMM). Afterwards we compare the quality metrics (like MTF curves) for the original, the degraded and the restored image. As a first result we show that restoration algorithms sometimes fail in dealing with non-natural scenes, e.g. slanted-edge targets. Further, these first results indicate a correlation between degradation and restoration, i.e. the restoration algorithms are not capable of removing the optically relevant errors introduced by the degradation, a fact neither visible nor available from the PSNR values. We discuss the relevance in the context of the automotive industry, where image restoration may yield distinct advantages for camera-based applications, but testing methods rely on the used image quality metrics.
Cooperative Quality Choice and Categorization for Multilabel Soak Up Process

The proposed system is going to deal with a very challenging task of automatically generating presentation slides for academic papers. The wide accessibility of web archives in electronic structures requires a programmed method to mark the records with a predefined set of subjects, what is known as customized Text Categorization (TC). Over the previous decades, it has been seen a substantial number of cutting edge machine learning calculations to address this testing errand. The produced introduction slides can be used as drafts to enable the moderators to set up their formal slides quickly. Documents are usually represented by the "bag-of-words": namely, each word or phrase occurs in documents once or more times is considered as a feature. It initially utilizes the relapse strategy to take in the significance scores of the sentences in a scholastic paper, and afterward a compelling calculation is created for multi-name grouping with using those information that are important to the objectives.The key is the development of a coefficient-based mapping among preparing and test cases, where the mapping relationship abuses the connections among the examples, instead of the unequivocal connection between the factors and the class marks of information and fabricates the staggered classifier on the adjusted low-dimensional data depictions in the meantime. It at first uses the backslide system to take in the importance scores of the sentences in an educational paper, and after that experiences the Latent Dirichlet Allocation (LDA) methodology to make especially sorted out slides by picking and modifying key articulations and sentences to a point for the slide. We set up a sentence scoring model in light of gullible Bayes classifier and use the LDA strategy to modify and expel key articulations and sentences for delivering the slides. Exploratory results exhibit that our technique can deliver very much wanted slides over regular procedures.
Blockchain-Based Platform for Smart Learning Environments

The Internet of Things (IoT) is making significant advances, especially in smart environments, but it still suffers from security issues and vulnerabilities. Hence, the security approach seems to be inappropriate for IoT-based workspaces due to the centralised architecture used by most connected objects. However, BlockChain (BC) is a technology that has been recently used to enhance security in mainly peer-to-peer (P2P) networks. The main research goal of this study is to determine whether BC, IoT, and decentralised models can be used to secure Smart Learning Environments (SLEs). In this paper, we propose a new secure and reliable architecture for connected workspaces that eliminates the central intermediary, while preserving most of the security benefits. The system is investigated on an environment containing several connected sensors and uses the BC application, offering a hierarchical network that manages transactions and profits from the distributed trust method to enable a decentralised communication model. Finally, an evaluation is conducted to highlight the effectiveness in providing security for the novel system.