Reviewerly/exHarmony huggingface dataset:
Development of Measurement and Control Learning Material to Promote Computational Thinking for Elementary School Students Considering Developmental Stage

In the new education guidelines to be implemented from 2020, the programming education is introduced from the elementary school stage. The programming education at elementary schools is aimed at nurturing "Computational Thinking". So, we develop teaching materials using "Measurements and Controls via Programmings" to promote the computational thinking for elementary school students. And we held a workshop using the Learning materials. In this paper, we propose the learning materials and learning contents, moreover consider the utility of learning materials and learning contents from survey of the workshop.
Experimental Analysis of Performance Ratio of Solar Rooftop Photovoltaic System (SRTPV) for Various Roof Orientation and Tilt

It is observed that Solar Roof Top PV systems has to be installed under verity of roof having different orientations, tilts etc. under different geographical conditions, this variation is likely to affect the performance of the SRTPV system. One of the ideal condition to have maximum generation is that the radiation should strike perpendicular to the solar panels surface fixed on roof. It is necessary to evaluate Performance of the SRTPV system and a methodology is to be set for maximizing the generation under these varied condition. This research is concentrated on evaluating SRTPV system for different orientations and tilt, finds its effect on system performance and develop a methodology to optimize design conditions for different roofs for optimal best performance under field situations created by at laboratory by forming different roof orientations and tilts with sun locations. Performance ratio is considered as evaluation criteria for optimizing results. The experiments model is designed for varying roof orientations from 0 to 180 degree and tilt for each orientations 0 to 90 degree. The Sun varies from Oto 180 degree. For the designing model "Taguchi Method" is used for controlling the measured parameters for evaluation. The analysis carried out show that the performance ratio for a given location should have an optima roof and tilt to maximize generation and get optimal PR. This gives the guide lines how roof can be treated if roof does not have proper angle of orientations and tilt. If treatment is not done one losses money by value with less investment on returns. For treated roof the PR found is above 80% which can be well compared to estimated generations simulation software. Further a design methodology is developed for guidelines for designers under the situation.
Attention-Based Hierarchical Recurrent Neural Network for Phenotype Classification

This paper focuses on labeling phenotypes of patients in Intensive Care Unit given their records from admission to discharge. Recent works mainly rely on recurrent neural networks to process such temporal data. However, such prevalent practice, which leverages the last hidden state in the network for sequence representation, falls short when dealing with long sequences. Moreover, the memorizing strategy inside the recurrent units does not necessarily identify the key health records for each specific class. In this paper, we propose an attention-based hierarchical recurrent neural network (AHRNN) for phenotype classification. Our intuition is to remember all the past records by a hierarchical structure and make predictions based on crucial information in the label's perspective. To the best of our knowledge, it is the first work of applying attention-based hierarchical neural networks to clinical time series prediction. Experimental results show that our model outperforms the state-of-the-arts in accuracy, time efficiency and model interpretability.
To be or not to be (embodied): That is not the question

Two articles in a recent special issue on Information and the Body published in the journal Library Trends stand out because of the way they are identifying, albeit indirectly, a formidable challenge to library information science (LIS). In her contribution, Bates warns that understanding information behavior demands recognizing and studying “any one important element of the ecology [in which humans are embedded].” Hartel, on the other hand, suggests that LIS would not lose much but would have lots to gain by focusing on core LIS themes instead of embodied information, since the latter may be unproductive, as LIS scholars are “latecomer[s] to a mature research domain.” I would argue that LIS as a discipline cannot avoid dealing with those pesky mammals aka patrons or users; like the cognate discipline and “community of communities” human computer interaction (HCI), LIS needs the interdisciplinarity to succeed. LIS researchers are uniquely positioned to help bring together LIS's deep understanding of “information” and embodiment perspectives that may or may not have been developed in other disciplines. LIS researchers need to be more explicit about what their original contribution is, though, and what may have been appropriated from other disciplines.
Supporting and Understanding Reflection on Persuasive Technology Through a Reflection Schema

Literature on persuasive technology acknowledges the importance of promoting reflection within design research and practice. This paper takes up a reflection framework suggested in previous research as assisting the reasoning of researchers, designers or other stakeholders concerning values, goals, actions, and their consequences in a project. It contributes to this research by demonstrating additional evidence for the applicability of the reflection framework by applying it to a published case. This work can guide researchers and practitioners by means of issues to be considered while reflecting on as well as communicating value-related aspects in a project, so that significant value choices and the rationale for actions taken to promote them are clear.
From Inbox Reception to Compliance: A Field Experiment Examining the Effects of E-mail Address and Subject Line on Response and Compliance Rates in Initial E-mail Encounters

Within text-based computer-mediated communication, individuals use available cues to form and guide initial encounters. The goal of this study was to examine how e-mail cues influence the effectiveness of initial e-mail communication in a professional context. A field experiment was conducted to examine how two readily available inbox-level cues— e-mail address (professional vs. nonprofessional) and subject line (clear vs. unclear)—influence the likelihood that an e-mail will be opened and the likelihood of the receiver complying with the e-mail’s request to complete an online survey. Analysis of e-mails sent to professors ( N = 2,021) from 24 academic institutions across the United States revealed that e-mails sent from a professional e-mail address coupled with an unclear subject line resulted in the greatest likelihood that an e-mail would be opened. Analysis found robust support for e-mail address professionalism to increase the likelihood that an embedded survey link will be clicked on and the subsequent survey completed. Theoretical and practical implications along with directions for future research are discussed.
Measuring Monetary Policy Surprises Using Text Mining: The Case of Korea

We propose a novel approach to measure monetary policy shocks using sentiment analy-\sis, which is relatively free from specification errors compared to VAR-identified shocks and allows time for a wider circle of market participants to digest information com-pared to shocks identified through intraday Fed futures data. We quantify the tones of news articles around 152 dates of Monetary Policy Board (MPB) meetings of the Bank of Korea (BOK) from 2005 to 2017 and then measure monetary policy surprises us-ing the changes of those tones following monetary policy announcements. We estimate its impact on asset prices and find that it better explains changes in long-term rates, while changes in the Bank of Korea’s base rate and VAR-identified monetary shocks are more closely associated with changes in short-term rates. Our result strongly suggests that a text mining approach to measure monetary policy surprises can be an useful complement to extract market expectations on future monetary policy.
Efficiently mining cohesion-based patterns and rules in event sequences

Discovering patterns in long event sequences is an important data mining task. Traditionally, research focused on frequency-based quality measures that allow algorithms to use the anti-monotonicity property to prune the search space and efficiently discover the most frequent patterns. In this work, we step away from such measures, and evaluate patterns using cohesion — a measure of how close to each other the items making up the pattern appear in the sequence on average. We tackle the fact that cohesion is not an anti-monotonic measure by developing an upper bound on cohesion in order to prune the search space. By doing so, we are able to efficiently unearth rare, but strongly cohesive, patterns that existing methods often fail to discover. Furthermore, having found the occurrences of cohesive itemsets in the input sequence, we use them to discover the representative sequential patterns and the dominant partially ordered episodes, without going through the computationally expensive candidate generation procedures typically associated with sequential pattern and episode mining. Experiments show that our method efficiently discovers important patterns that existing state-of-the-art methods fail to discover.
OpenCL vs

Digital audio synthesis has become an important component of modern music production with techniques that can produce realistic simulations of real instruments. Physical modelling sound synthesis is a category of audio synthesis that uses mathematical models to emulate the physical phenomena of acoustic musical instruments including drum membranes, air columns and strings. The synthesis of physical phenomena can be expressed as discrete variants of Newton's laws of motion, using, for example, the Finite-Difference Time-Domain method or FDTD.
A new robust formulation for optical-flow/material identification problems

The characterization of material properties from image sequences is of paramount importance in assessing structural integrity. A particular application can be found in the field of biomedical engineering, where anatomical structures, whose material properties are frequently sought, can be observed in-vivo through time. A traditional approach for such task, involves the estimation of the displacement field across the image sequence – usually by means of optical flow techniques – and the subsequent estimation of the material properties through the formulation of an inverse mechanical problem. The decoupled nature of this approach leads to cumulative errors across these stages, caused by the lack of consistence between the recovered optical flow and the displacement field delivered by the underlying mechanical model yet to be identified. In recent years, new approaches were proposed to integrate the optical flow and the material identification problems to render a compatible characterization of the displacement field in the sense that they correspond to a mechanical model whose material properties also undergo an identification process. In the present work, a new numerical method for the simultaneous identification of the optical flow field and the material properties of a mechanical model from a sequence of images is presented. Several numerical examples with manufactured solution are solved. Different sources of errors are considered, such as randomized pixel errors and interpolation errors, to analyze the robustness of the approach. The results obtained allow us to conclude that the proposed method performs robustly even in the presence of noise, and is also efficient in terms of the number of iterations required to achieve convergence.
Antecedents of Internet of Things Adoption in Oil and Gas Industry

Internet of Things (IoT) is one of the top digital topics recently emerging within the oil and gas industry. According to several definitions of IoT, everything should be interconnected and linked to each other. For IoT to emerge from the conceptual level and become a reality, organizations must be ready to invest and adopt this new technology. Several studies have showed the potentials that IoT holds for the industrial sector. However, IoT is still not properly adopted across various organizations in various industries, including the oil and gas. We conducted an extensive literature review on several databases to identify the antecedents of IoT adoption in the oil and gas industry. This study aims to identify the antecedents of IoT adoption in oil and gas industry from different literatures and present the research model. This paper will help the organizations in oil and gas industry to understand the antecedents of IoT adoption; and spur the research community to delve into more research on IoT adoption.
Programming Agents by Their Social Relationships: A Commitment-Based Approach

Multiagent systems can be seen as an approach to software engineering for the design and development of complex, distributed software. Generally speaking, multiagent systems provide two main abstractions for modularizing the software: the agents and the environment where agents operate. In this paper, we argue that also the social relationships among the agents should be expressed explicitly and become first-class objects both at design- and at development-time. In particular, we propose to represent social relationships as commitments that are reified as resources in the agents’ environment and can be directly manipulated by the agents via standard operations. We demonstrate that this view induces an agent programming schema that is substantially independent of the actual agent platform, provided that commitments are available as explained. The paper exemplifies the schema on two agent platforms, JADE and JaCaMo, where commitments are made available via the 2COMM library.
Negligence and AI's Human Users

Negligence law is often asked to adapt to new technologies. So it is with artificial intelligence (AI). But AI is different. Drawing on examples in medicine, financial advice, data security, and driving in semi-autonomous vehicles, this Article argues that AI poses serious challenges for negligence law. By inserting a layer of inscrutable, unintuitive, and statistically-derived code in between a human decisionmaker and the consequences of that decision, AI disrupts our typical understanding of responsibility for choices gone wrong. The Article argues that AI’s unique nature introduces four complications into negligence: 1) unforeseeability of specific errors that AI will make; 2) capacity limitations when humans interact with AI; 3) introducing AI-specific software vulnerabilities into decisions not previously mediated by software; and 4) distributional concerns based on AI’s statistical nature and potential for bias.Tort scholars have mostly overlooked these challenges. This is understandable because they have been focused on autonomous robots, especially autonomous vehicles, which can easily kill, maim, or injure people. But this focus has neglected to consider the full range of what AI is. Outside of robots, AI technologies are not autonomous. Rather, they are primarily decision-assistance tools that aim to improve on the inefficiency, arbitrariness, and bias of human decisions. By focusing on a technology that eliminates users, tort scholars have concerned themselves with product liability and innovation, and as a result, have missed the implications for negligence law, the governing regime when harm comes from users of AI.The Article also situates these observations in broader themes of negligence law: the relationship between bounded rationality and foreseeability, the need to update reasonableness conceptions based on new technology, and the difficulties of merging statistical facts with individual determinations, such as fault. This analysis suggests that though there might be a way to create systems of regulatory support to allow negligence law to operate as intended, an approach to oversight that it not based in individual fault is likely to be a more fruitful approach.
A GPU-Based jDE Algorithm Applied to Continuous Unconstrained Optimization

Population-based search algorithms, such as the Differential Evolution approach, evolve a pool of candidate solutions during the optimization process and are suitable for massively parallel architectures promoted by the use of GPUs. Hence, this paper proposes a GPU-based self-adaptive Differential Evolution employing the jDE mechanism to control its parameters, named cujDE. Two CUDA structures are employed to model the kernel functions in cujDE. The proposed algorithm is compared with another GPU-based self-adaptive DE (cuSaDE) in four continuous unconstrained benchmark functions. Also, the speedup between the CPU-based and the GPU-based jDE is measured. Results obtained suggest that the proposed approach is well suited to and competitive for continuous optimization.
Academic Libraries in 2018: A Comparison of Makerspaces within Academic Research Libraries

This chapter reports the results of a survey deployed to 113 of the 124 Association of Research Libraries (ARL) members on the current role makerspaces play in academic libraries. Nearly one-quarter of ARL institutions (n = 26; 23%) indicated they have a makerspace. This research analyzes ARL institutions who have established makerspaces within their physical library spaces. This chapter describes the physical aspects of makerspaces, programs and marketing, and demographic details (Bagley, 2014). According to the respondents, what constitutes a makerspace depends on the patrons ARL institutions serve.
Multi-level Supervised Network for Person Re-identification

Most existing methods for person re-identification (re-id) only utilize last layer features of deep convolution neural networks (CNNs), which may lose the useful lower level information and decreases the performance. To alleviate this problem, we propose a novel multi-level feature learning framework, Multi-Level Supervised Network (MLSN), for re-id. Specifically, MLSN is consist of a backbone deep CNN and several part-wise sub-networks. The backbone CNN extracts multi-level semantic feature maps at each middle layer and the part-wise sub-networks transform the feature maps to part-based pedestrian descriptors through a designed supervised learning. By fusing these descriptors, MLSN can utilize both low level information and high level information for re-id. Moreover, an effective optimizing strategy is presented to further improve the performance. Experimental results on three large scale datasets Market-1501, DukeMTMC-reid and CUHK03 show that our proposed approach outperforms several state-of-the-art methods.
On checking skeptical and ideal admissibility in abstract argumentation frameworks

Abstract argumentation frameworks (afs) are directed graphs with vertices being abstract arguments and edges denoting the attacks between them. Within the context of afs, we implement and evaluate an algorithm for two essential computational problems: checking skeptical and ideal admissibility. We evaluate the implemented algorithms using a widely-known benchmark. In terms of the number of solved problem instances and the average running time, our implementation outperforms two prominent systems.
Multilingual Text Categorization of Indo-Aryan Languages

This paper explores a generalized document categorization technique on both Bengali and Nepali documents. The motivation for the research is to develop a common Natural Language Processing (NLP) algorithm for the languages which are originated from the same language family tree. In this regard, several machine learning (ML) approaches; specifically Linear SVC, Chi-squared SVC, Bernoulli Naïve Bayes, Multinomial Naïve Bayes algorithms are used to automatically classify Bengali and Nepali documents into predefined categories. We analyze the efficiency of the algorithms and validate the hypothesis by showing more than 90% accuracy of all the algorithms. We also, observe interesting analogy among top features of Bengali and Nepali categories.
Information Security Governance, Technology, Processes and People: Compliance and Organisational Readiness

Compliance and an increasing level of cyber maturity form crucial part of corporate defence systems and are the basis of any well-functioning cyber security programme. As the scope of compliance widens with the maturity of the organisation, the human element needs to be addressed as well. Cyber maturity assessments, red teaming and capture the flag exercises help simulate the threat vectors tactics, tools and procedures, give defenders an insight into the enemy motives and help mitigate technical exposure. However, most of these exercises are being delivered with an exclusive aim to achieve technical learning and address incidents on a technical vulnerability level. This chapter looks at how can organisations – having achieved the necessary compliance and governance standards – understand and address human behaviour as cyber security threat. These can be team member’s individual blind-spots and glitches in high-risk team dynamics, which are, if ineffective, are considered system vulnerabilities. When these risk behaviours have been identified and addressed with targeted interventions and training, organisations will be able to mitigate the human risk directly, just as they would patch their on-line systems or scan their networks.
Investigating students' attitude and achievement in organic chemistry using interactive application

The "fourth wave" transformed our society into "mobile society" which focuses on cyber-physical systems. It also redirected higher education practices from the traditional classroom setting to e-learning system. The purpose of this action research is to investigate the impact on the use of an interactive application on students' performance and attitude towards chemistry in an Organic Chemistry class. The study was conducted on and confined to first-year BS Food Technology students of the Mariano Marcos State University, Ilocos Norte, Philippines. The research problem was identified through the experience of the researcher supported by the literature. An intervention was carried out with the use of an interactive mobile application - Chirality 2, a game-based application designed to teach fundamental concepts of organic chemistry. Stereochemistry Concept Inventory (SCI) and Attitude toward the Subject Chemistry Inventory (ASCIv2) were utilized in the conduct of pre- and post-strategy tests. Students' scores were described using means and standard deviation. Paired t-test was used to determine significant differences between the students' pre-test and post-test scores, both in their examination performance and attitudes. After the intervention, the post-test results revealed a significant improvement in the test scores of students. There was also a significant change in the attitude of the students, both on affective and cognitive sub-dimensions. The significant difference between the pre-test and post-test suggests that interactive mobile application enhances the performance of students and develops students' attitude. Results of the Pearson's correlation showed that there was no significant association between the attitude towards chemistry and achievement of students.