Reviewerly/exHarmony huggingface dataset:
Developing Instructional Model of Panel Discussion for Rational Decision Making of University Students

The purpose of this study is to develop an instructional model of panel discussion for university students to practice a rational decision-making on controversial STS issues. The research participants consisted of 21 pre-service teachers including 1 moderator, 6 panelists and 14 audience. The moderator and panelists were majoring in technology education, and audience was collected from various majors. A model was developed by a systematic instructional design, and a panel discussion was conducted and evaluated by group interview, and then a model was revised. A final model was completed after modifying a model three times with implementation of panel discussion. The model consisted of five steps. The first step is `Keynote Speeches` in which the delegates of the two groups, pros and cons, announce group opinions about a controversial STS issue. The second step is ‘Panelists` Speeches’ in which six panelists make audience understand knowledge and value about an issue. In the third step, ‘Semi-Structured Debate’, panelists have a debate with a semi-structured scenario. This step is the most important step of this model, in which audience compares panelists’ opinions in different perspectives. Step 4 is `Final Remarks` in which a representative of each panel group summarizes group opinions. Step 5 is `Questions and Answers` step, in which audience and panelists communicate by questions and answers. This step, the second important one in this model, allows audience to participate directly in a panel discussion and to get a deeper understanding an issue. The implications and limitations of this model for a panel discussion in university lessons were discussed.
System for Determining Public Health Level Using the Agglomerative Hierarchical Clustering Method

Regions having higher level of welfare do not always have better indicator values than other regions having lower level of welfare. The problem is the lack of information related to the indicator values needed to determine the health level. Therefore, clustering using health data becomes necessary. Data were clustered to see the maximum or the minimum level of similarity. The clustered data were based on the similarity of four morality indicator values of the regional health level. Morality indicator values used in this research are infant mortality rate, child mortality rate, maternal mortality rate, and rough birth rate. The method used is Agglomerative Hierarchical Clustering (AHC) - Complete Linkage. Data were calculated using Euclidean Distance Equation, then Complete Linkage. Four clustered data were grouped into two clusters, healthy and/or unhealthy. The result, combining from all clusters into two large clusters to see healthy and unhealthy results.
An Efficiency Evaluation Model for Designing Optimal Scheduling Mechanisms in N+X Hybrid Blockchain

With the widespread of Bitcoin, blockchain has been focused by researchers from many fields, such as digital cryptocurrencies, finance security exchange, decentralized trade platform and so on. However, the operation efficiency (about 10min/per block) has limited its practical application except of Bitcoin. In this paper, we conduct further research based on the existing hybrid blockchain architectures, say N + X Hybrid Blockchain, which may significantly improve blockchain efficiency with the negligible cost of security. Moreover, we gave an evaluation model for the feedback process of nodes in the N+X Hybrid Blockchain and obtained optimal strategies. For the application of Energy Internet (EI), we proposed a scheduling mechanism based on the above evaluation model, and given an algorithm to implement it.
ParaXplore

Generative design tools together with large screen displays provide designers an opportunity to explore large numbers of design alternatives. There are numerous design studies on exploring multiple simultaneous designs, but few present interface solutions and system features for such exploration. To the best of my knowledge, no study probes exploring a large design space with multiple simultaneous states. The premise of my research is that, if designers can work directly with large numbers of designs with new representations and tools as part of the design workflow, we should expect new patterns and strategies to emerge and change the design process. Such task environments may present novel actions, task sequences, methods and techniques. What are new actions and techniques that would enable working seamlessly with multiple designs? My research aims to answer this (and similar) questions; and, more specifically, to uncover how designers' augment their work through spatial structuring of the task environment to reduce the cognitive cost of working with multiple simultaneous designs on a large work-surface. I conducted a lab experiment with nine designers. The results suggest design features for new front-end gallery interfaces for managing a large set of design variations while enabling simultaneous editing.
Semi-automatic System for Title Construction

In this paper, we introduce a two-phase, semi-automatic system for title construction. Our work is based on the hypothesis that keywords are good candidates for title construction. The two phases of the system consist of - extracting keywords from the document and constructing a title using these keywords. The proposed system does not generate the title, instead it aids the author in creatively constructing the title by suggesting impactful words. The system uses a pre-trained supervised keyword extraction model to extract important words from the text. Our KE approach gains from the advantages of graph-based keyword extraction techniques. We empirically establish the effectiveness of the proposed method, and show that it can be applied to any texts across domain and corpora. The keywords thus extracted are suggested to the author as potential candidates for inclusion in the title. The author can use creative transformations of the suggested words to construct an appropriate title for the manuscript. We evaluate the proposed system by computing the overlap between the list of title-words and the extracted keywords from the documents, and observe a macro-averaged precision of 82%.
Guess or Not?

Now examinations and scores serve as the main criterion for a student's academic performance. However, students use guessing strategies to improve the chances of choosing the right answer in a test. Therefore, scores do not reflect actual levels of the student's knowledge and skills. In this paper, we propose a brain-computer interface (BCI) to estimate whether a student guesses on a test question or masters it when s/he chooses the right answer in logic reasoning. To build this BCI, we first define the "Guessing'' and employ Raven's Progressive Matrices as logic tests in the experiment to collect EEG signals, then we propose a sliding time-window with quorum-based voting (STQV) approach to recognize the state of "Guessing'' or "Understanding'', together with FBCSP and end-to-end ConvNet classification algorithms. Results show that this BCI yields an accuracy of 83.71% and achieves a good performance in distinguishing "Guessing'' from "Understanding''.
Generating Reliable Feedback About Students’ Performances Within an Automated Reading Tutor

Personalized learning technologies such as automated reading tutors have been proposed as a way to help struggling readers acquire needed skills while simultaneously encouraging engaged, sustained reading of entire books. This article investigates a key step in the development of such technologies: translating an entire novel into a sequence of nonoverlapping reading passages, so that valid feedback about students’ performances can be generated at the end of each tutoring session. A critical reliability issue that may be introduced at this step is identified, an approach for addressing that issue is proposed, and an evaluation conducted with respect to three popular middle-grade novels is presented. The findings suggest that our new approach provides more reliable evidence about students’ abilities and thus may be more appropriate for use when building automated reading tutors.
Technologies for Super Micro Websites: Itty.bitty vs. Docs365 Itty Bitty

In this paper, we discuss the "Itty.bitty" technology. It is a new way to share HTML/text information. We pay attention to the effectiveness of its tools by comparing it with the Docs365 Itty Bitty from the point of view of variety and richness of expression. To make a comparison, we redesigned the existing academic laboratory Website and encoded it into the Itty.bitty URL and Docs365 Itty Bitty document. The following features were selected as criteria for this comparison: the implementable components such as textual, graphical, audio, and visual items; tools to adjust fonts, screens, and graphical elements; instruments to design the Website presented in these technologies, methods to deploy Websites, size Websites in KB, speed to appear in the browsers, type of browsers for viewing Websites, opinions of the users on the design, etc. Technological steps to create Websites are as follows: For the Itty.bitty URL, we can convert the HTML directly on the Itty.bitty Website. Once we put the target string (In this case, HTML), it automatically converts the string into the encoded URL. On the other hand, for the Docs365 Itty Bitty, we can do it by redesigning the content of a particular web page into a Google Docs document and translating it into Itty.bitty URL utilizing the Docs 365 Itty Bitty add-on. Our general conclusion on the results of this comparison are as follows: These technologies have their own niches. Applications implemented in Itty.bitty are suitable for super small Websites with very limited features. An approach to utilize Docs365 Itty Bitty gives more freedom, but there is a number of restrictions, and the developer needs to check them in the case by case manner.
A Data Fusion Scheme for Wireless Sensor Networks Using Clustering and Prediction

This paper intends to reduce the communication cost, while ensuring data prediction accuracy and data transmission efficiency in wireless sensor networks (WSNs). A data fusion scheme using clustering and prediction algorithms is proposed. Initially, nodes are clustered by using historical data, and then they are linked based on the actual geographical distance. Next, during the data fusion process, the base station and sensor nodes both use the online recurrent extreme learning machine (OR-ELM) to predict the future sensing data, which can guarantee that the data sequence in the base station and sensor nodes are synchronous. If the prediction fails, data will be transmitted to other nodes in the link and forwarded. Finally, experimental results reveal that the proposed data fusion scheme not only can effectively predict the sensor data, but also can reduce spatial and temporal redundant transmissions with low computational cost.
A Real-Time Atypical Amplitude Detection based on Measurement Metadata

PAbMM is a data stream engine which implements a real-time data processing strategy focused on the Measurement and Evaluation (M&E) projects. This allows an active monitoring of different kinds of the entities under analysis specified in the projects. The M&E projects are defined using an M&E framework with the aim of keeping the comparability and extensibility of the projects, fostering the comparability of the measures. The processing strategy runs a set of the online statistical analysis for real-time detecting inconsistencies in relation to the project definition (e.g. a data source miscalibration). In this work, a new component oriented to detect an atypical amplitude on the data in real-time using the measurement metadata related to the project definition is introduced. The underlying idea related to the new component and its incorporation in the processing strategy is shown. In addition, a simulation on the monitoring of the heterogeneous data sources is shown, which gives a processing rate of 2000 measures by each 0,7 seconds. It incorporates a complement to the current outlier detection strategy because the variation range could be atypical, even when all the values are considered inside the expected values.
JUniVerse

Current approaches for analyzing large number of open-source projects mainly focus on data mining or on static analysis techniques. On the contrary, research applying dynamic analyses such as Runtime Verification (RV) to open-source projects is scarce. This is due to lack of automatic means for executing arbitrary pieces of software that rely on complex dependencies and input parameters. In this paper, we present a fully automated infrastructure, JUniVerse, to conduct large-scale studies on unit tests in open-source projects in the wild. The proposed infrastructure runs on a cluster for parallel execution. We demonstrate the effectiveness of JUniVerse by conducting a large-scale study on Java projects hosted on GitHub. We apply a selection criteria based on static analysis to select 3 490 active projects. To show the feasibility of JUniVerse, we choose RV as a case study, and investigate the applicability of 182 publicly available JavaMOP specifications to the code exercised by unit tests. Our study reveals that 37 (out of 182) specifications (i.e., 20%) are not applicable to the code exercised by unit tests of real-world projects. Finally, with JUniVerse, we are able to identify a set of specs and projects for future RV studies.
Preliminary study to develop of learning media for Newton’s law of gravity using ICT based on contextual teaching and learning for senior high school

Learning of Physics should be a scientific work exercise conducted by students with teacher guidance. Such physics learning requires media and teaching materials that facilitate students to conduct scientific investigations. Besides the study of physics is a variety of events that occur either spontaneously or planned by humans. Because physics is contextual, it should be the subject of study in context-based learning. However, some of these contexts may not be presented directly in the classroom, because some are very fast, too long, or because they are very large or tiny. Therefore, engineering is required through the utilization of ICT. So, the learning media of physics as needed is contextual based and presented by ICT. A survey has been conducted to find out whether the scientific activities in physics learning to have been done in accordance with the demands and whether there is available instructional media and teaching materials as per the learning needs for high school physics. The survey results show that only half of the scientific activities required in the curriculum are implemented and only one-third of contextual based learning media and utilize ICT appropriately. One of the lesson material in physics on X grade that do not have media and teaching materials as needed is Newton's law of gravity. Therefore, it is necessary to do further research.
IPME Workbench

Today's small group interactions often occur in multi-device, multi-artifact ecosystems. CHI researchers studying these group interactions may adopt a socio-behavioral or sensing/data mining approach or both. A mixed-methodological approach for studying group interactions in collocated settings require collecting data from a range of sources, like audio, video, multiple sensor streams, and multiple software logs. Analyzing these disparate data sources systematically - with opportunities to rapidly form and correct research insights can help researchers who study group interactions. But engineering solutions assimilating multiple data sources to support different methodologies, ranging from grounded theory methodology to log analysis are rarely found. To address this frequent and tedious problem of data collection and processing in mixed-methodology studies of group interactions, we introduce a workbench tool: Interaction Proxemics in Multi-Device Ecologies (IPME). The IPME workbench synchronizes multiple data sources, provides data visualization, and opportunities for data correction and annotation.
Integrated Detection of Copy Number Variation Based on the Assembly of NGS and 3GS Data

The genomic coverage of copy number variations (CNVs) ranges from 5% to 10%, which is one of the essential pathogenic factors of human diseases. The detection of large CNVs is still defective. However, the read length of the third-generation sequencing (3GS) data is longer than that of the next-generation sequencing (NGS) data, which can theoretically solve the defect that the long variation can't be detected. However, due to the low accuracy of the 3GS data, it is difficult to apply in practice. To a large extent, it is a supplement to the NGS data research. To solve these problems, we developed a new mutation detection tool named AssCNV23 in this paper. Firstly, this tool corrects the 3GS data to solve the problem of high error rate, and then combines the results of a variety of mutation detection tools to improve the accuracy of the initial mutation set and to solve the detection bias of a single detection tool. At the same time, the high-quality 3GS data was introduced by AssCNV23 to guide the NGS data to assemble, and then detects the CNV after getting enough length data. Finally, to improve the detection efficiency, the tool generates images containing the sequence depth information based on the read depth strategy and uses the convolutional neural network to detect the existing CNVs. The experimental results show that AssCNV23 guarantees a high level of breakpoint accuracy and performs well in identifying large variation. Compared with other tools, the deep learning model has advantages in accuracy and sensitivity, and Matthew correlation coefficient (MCC) performs well in various experiments. This algorithm is relatively reliable.
The architecture of the multy-agent search system of the company

Fast and convenient search of information on a large number of servers simultaneously to a large number of users at enterprises allows for the use of search engines. Since such a system is complex and consists of a large number of applications, it is effective to use the theory of multi-agent systems to create it. For a more systematic search, we suggest using the methods of ranking the results obtained. The article describes the developed mathematical model for determining the page indicator based on ranking factors for determining the position of the page in search results. In the proposed architecture, the simulation results are processed.
Bear &amp; Co

Bear & Co. is a fictitious immersion into the world of being part of an IOT start-up. We invite visitors to join the company, and facilitate their journey through various ethical conundrums, as they become part of the company. First, they must state their values - what they will bring to the company and care most about. Then, we test those values through different scenarios and problems that are unexpected and that do not have easy answers. Finally, we debrief our visitors and invite them to peruse explanations for various ethical approaches presented as maps and diagrams, where they can interrogate their own decisions against three different philosophical viewpoints.
A tool for exploring networks of computer scientists as a graph

In this paper we present GraphDBLP, a tool to query the DBLP bibliography as a graph. The DBLP source data were enriched with semantic similarity relationships computed using word-embeddings. A user can interact with the system either writing queries on the graph-db visual console or using a shell-interface provided with 4 parametric and pre-defined queries.
Discovering Users for Technical Innovations through Systematic Matchmaking

Every year Human-Computer Interaction (HCI) researchers create new technical innovations. Unfortunately, the User-Centered Design (UCD) processes used by most designers in HCI does not help much when the challenge is to find the best users for these innovations. We augmented the matchmaking design method, making it more systematic in considering potential users by using a list of 399 occupation groups and by incorporating the customer discovery interviews from the Lean Startup. We then assessed our new design method by searching for users who might benefit from two different technical innovations: ViBand and PaperID. We found that matchmaking with the list of occupation groups helped surface users we would likely have not considered. In addition, the customer discovery interviews helped to generate better applications and additional target users for the innovations. This paper documents our process, the design method, and insights we gained from using it.
A shortest path problem for tsunami evacuation in Padang City using Floyd-Warshall algorithm

Padang City is a Capital City of West Sumatera Province bordering on the Indian Ocean and active collision zone of two plates namely Indo-Australian plate and the Eurasian plate. This causes Padang City is very vulnerable to earthquake and tsunami. Therefore, determination of a path of evacuation is very important to do. One of the methods is used Floyd-Warshall Algorithm. The Algorithm will determine the shortest path of tsunami evacuation to be searched for all pairs of vertices of the way. In this research, the problem is how to choose the shortest path of tsunami evacuation at Padang Utara Subdistrict. The results are some of the shortest paths of tsunami evacuation that could pass by people in the district of Padang Utara from the beach area to the nearest shelter.
HPanal

Huge page is an attractive technique that can improve performance by reducing the number of TLB (Translation Lookaside Buffer) misses and address translation overhead. However, a lot of memory intensive applications such as Redis, Hadoop and MongoDB recommend to disable this technique due to the performance anomaly. To address this issue, this paper proposes a novel analytic framework, called HPanal, that can evaluate the benefit and cost of the huge page technique quantitatively. The benefit is estimated by three parameters, namely TLB miss, page walk overhead and page fault, while the cost is assessed by the page allocation overhead. These parameters are affected by not only application characteristics such as working set size and access pattern but also system conditions such as available memory and fragmentation degree. HPanal also provides run time capabilities for measuring these parameters while changing application characteristics and system conditions dynamically. Real implementation based experimental results reveal that our framework can explore the tradeoffs in an appropriate way and the fragmentation degree plays a key role on performance of the huge page technique.
Potency of Smart Water Flow Management at Lowland Residential Area

Most of the residential areas in Palembang were located at lowland such as riverside. The problem of flood is inevitable at this area. Flood brings loss to the property. This paper aims to seek the potency of applying smart water flow management to cope the risk of loss caused by flood. To achieve the aims, the study (1) observed how residents manage their house to cope the flood; (2) mapped the function of the buildings at a riverside settlement in Palembang and (3) formulized the strategy for anticipating the risk. Data were collected by interview and field survey. Qualitatively, data were overlapped with the topography map to understand the flow of the runoff water within the settlement. Findings reveal that the distance from the water body determine the strategy on cope the risk of flood
Africa's agricultural R&D funding rollercoaster: an analysis of the elements of funding volatility

Africa's agricultural R&D funding rollercoaster: an analysis of the elements of funding volatility