Open Task Research Articles

Printed Circuit Board identification using Deep Convolutional Neural Networks to facilitate recycling

In this paper, we have proposed a robust Printed Circuit Board (PCB) classification system based on computer vision and deep learning to assist sorting e-waste for recycling. We have used a public PCB dataset acquired using a conveyor belt, as well as a locally developed PCB dataset that represents challenging practical conditions such as varying lighting, orientation, distance from camera, cast shadows, view-points and different cameras/resolutions. A pre-trained EfficientNet-B3 deep learning model is utilized and retrained for use with our data in PCB classification context. Deep nets are designed for closed set recognition tasks capable of classifying only the images they have been trained for. We have extended the closed set nature of deep nets for use in our open set classification tasks which require identifying unknown PCBs apart from classifying known PCBs. We have achieved an open set average accuracy of 92.4% which is state of the art given the complexities in the datasets we worked with.

Understandings of perimeter and area mobilized with an exploratory approach in a lesson study

Background: Exploratory teaching, a theme increasingly investigated in mathematics education, has been examined as a pedagogical approach underlying the research lesson in lesson studies. Objective: The purpose of this article is to present and discuss the understandings of area and perimeter mobilized by 8th-grade elementary school students based on an exploratory approach, which was the basis for the research lesson in the lesson study. Design: The research was qualitative and interpretive, based on content analysis. Setting and participants: The activities were conducted in two classes, of two hours each, in which the students were invited to resolve an exploratory task about the topic “area and perimeter”, which was prepared by a group of teachers participating in a cycle of a lesson study. The students were asked to explain their strategies, results, and conclusions. Data collection and analysis: The analysis was based on empirical material composed of the materials related to the solutions presented by the students and transcriptions of audio recordings of the students’ discussions as they solved the tasks and of group discussions (the final moments of the research lesson). Results: The analysis presents aspects related to understandings of the topics area and perimeter, mobilized by the exploratory approach, which comprise the following categories: measure, mathematical operation and geometric property. Conclusion: The exploratory approach, underlying the research lesson, favored a deepening of understandings about area and perimeter, because it gave students the opportunity to explore and confront these concepts from different representations mobilized in an open task and to communicate their mathematical ideas and conclusions.

Artificial intelligence in history education. Linguistic content and complexity analyses of student writings in the CAHisT project (Computational assessment of historical thinking)

The use of standardized test formats in the assessment of historical competencies has recently come under severe criticism, especially in the United States, where standardized tests are particularly common. History researchers have argued that open-ended items are more appropriate for assessment. However, providing largescale evaluations of open-ended answers is time consuming and poses challenges regarding the objectivity, validity, and replicability of ratings. To address this issue, we investigated the extent to which computer-based evaluation methods are suitable for evaluating student answers by combining qualitative methods from history education research with quantitative, computer-based linguistic analyses. In two studies, we analyzed data from an intervention study in which 962 students (ninth graders) completed seven open-ended tasks. In Study 1, we investigated the extent to which task complexity could be predicted from the linguistic complexity of the students' answers. In Study 2, we conducted an automatic content assessment by aligning student answers with predefined target answers and compared automatic scores with human ratings that were based on an elaborate evaluation scheme we developed. In our first study, we identified several linguistic features of students’ answers that successfully predicted task complexity. In our second study, the correlations between manual and computational task scores were encouraging. However, our human interrater agreement left room for improvement and demonstrated the challenges of reliably applying explicit evaluation criteria to open tasks. Overall, our findings illustrate that the combination of qualitative methods from history education research and quantitative computational-linguistic analyses may support the large-scale evaluation of open tasks.

Hierarchical classification of multi-corpus acoustic field recordings

Passive acoustic monitoring is one important technique for detecting long-term ecological change. Since audio data contains complex temporal and frequency information, deep learning is an ideal computational framework for automatic processing. In this work, the dataset consists of 64k annotated clips distributed across 23 classes that are derived from 17 000 h of acoustic data consisting of bio-, geo- and anthropophonies recorded at two different sites in the Capital Region of New York State. A hierarchical deep convolutional neural network with one local classifier per parent node provides fine, medium, and coarse-grained label predictions. If a fine-grained label is predicted incorrectly, a correct medium or coarse label still provides valuable taxonomic information. While the flat classifier performs similarly to the hierarchical for single-corpus training scenarios, it achieves higher medium-grained predictions for categories such as avian vocalization. For cross-corpus scenarios, the hierarchical classifier achieves a superior performance overall. These results highlight the importance of neural network architecture customization to suit a particular task and domain; hierarchical structures show potential in open set recognition tasks, where the entire set of possible classes is unknown. [Work supported by NSF Grant No. 1631674 and an RPI HASS Fellowship.]

An Evidence-Based Approach to Distractor Generation in Multiple-Choice Language Tests

The purpose of this project is to explore the feasibility of a new approach for producing evidence-based distractor sets. We use Common Wrong Answers (CWAs), and the associated performance data, generated by candidate responses to open gap-fill tasks, to produce distractor sets for multiple-choice gap-fill tasks based on the same texts. We then investigate whether these distractor sets are effective for use in language tests, in terms of empirical and qualitative review, and consider potential impacts on the production process for test material. This project explores a new and innovative method of content development, and raises the possibility of a new approach to item production that can semi-autogenerate test items in shorter periods of time without affecting quality or reliability. Although the approach is specific to one task type, it is hoped that further research will expand on the applications of the approach to deliver a version that may be operationalised for use across different task types in the development of language assessments.

Еstimating the Chézy roughness coefficient as a characteristic of hydraulic resistance to flow in river channels: a general overview, existing challenges, and ways of their overcoming

This paper deals with results of a systemized overview of the Chézy roughness coefficient calculation problem as one most frequently used empirical characteristics of hydraulic resistance. The overview is given in the context of the formation of reliable empirical data needed to support hydro-engineering calculations and mathematical modelling of open flows in river channels. The problem topicality is because of a large number of practical tasks which need such a pre-research. In many cases, the accuracy of determining empirical hydraulic resistance characteristics can largely affect the accuracy of solving tasks relating to designing hydraulic structures and water management regardless of chosen mathematical models and methods.Rivers are characterized by a significant variety of flow conditions; hydraulic resistance to flows in rivers can thus vary widely determining their flow capacity. Considering the variety of river hydro-morphology and hydrology, the Chézy roughness coefficient often appears to be the most complete characteristic of hydraulic resistance to open flows in river channels comparing with other integral empirical characteristics of hydraulic resistance.At present, there are a large number of empirical and semi-empirical formulas to calculate the Chézy roughness coefficient. The main aim of this study was to analyze and systematize them in the context of providing proper support to the open channel hydraulics tasks. To achieve the aim of the study, a literature review regarding the problem of determining the integral hydraulic resistance characteristics to open flow in river channels was performed, as well as formulas used to calculate the Chézy roughness coefficient in practice were explored and systemized. In total, 43 formulas to calculate the Chézy roughness coefficient, as well as 13 formulas that can be used to estimate the Manning roughness coefficient were analyzed and systematized. Based on all these formulas, about 250 empirical equations can be compiled to calculate the Chézy coefficient depending on hydro-morphological peculiarities of rivers and river channels, hydraulic conditions, formulas application limits, and so on.

Computing Equilibria with Two-Player Zero-Sum Continuous Stochastic Games with Switching Controller

Equilibrium computation with continuous games is currently a challenging open task in artificial intelligence. In this paper, we design an iterative algorithm that finds an ε-approximate Markov perfect equilibrium with two-player zero-sum continuous stochastic games with switching controller. When the game is polynomial (i.e., utility and state transitions are polynomial functions), our algorithm converges to ε = 0 by exploiting semidefinite programming. When the game is not polynomial, the algorithm exploits polynomial approximations and converges to an ε value whose upper bound is a function of the maximum approximation error with infinity norm. To our knowledge, this is the first algorithm for equilibrium approximation with arbitrary utility and transition functions providing theoretical guarantees. The algorithm is also empirically evaluated.

Thermographic Stall Detection by Model-Inspired Evaluation of the Dynamic Temperature Behaviour

Model-inspired signal processing approaches with an enhanced detectability of flow separation on thermographic images are presented. Flow separation causes performance loss, structural loads and increasing acoustic emissions on wind turbine rotor blades. However, due to the low thermal contrast between turbulent and separated flow regions, the non-invasive thermographic visualisation of flow separation is currently only possible for wind tunnel measurements, which are characterised by a high thermal contrast and a small measuring distance. The state-of-the-art signal processing approaches evaluate the surface temperature fluctuation of thermographic image series. However, understanding of the signal measurement chain with a distinct consideration of the influences on the dynamic surface temperature is incomplete. Therefore, designing model-inspired signal processing approaches which provide a high interpretability and a maximum contrast is an open task. The proposed signal processing approaches evaluate the surface response selectively, by using the amplitude information of the surface temperature response to an oscillating input signal or gradient-based for a transient input signal. The approaches are applied to wind tunnel measurements on a rotor blade profile at a near thermodynamic steady state and a transient thermodynamic behaviour at Reynolds numbers that are representative for operational wind turbines. The gradient-based evaluation shows an improved contrast for the detection of flow separation, but is only applicable to profiles with transient thermodynamic behaviour. The amplitude evaluation provides a high degree of interpretability of the processed images based on flow-dependent features and enables for an unambiguous identification of flow separation by a global amplitude minimum close to the separation point. Additionally, an increased spatial resolution for surface modifications is shown, while the contrast between flow regions is significantly decreased. Hence, the proposed approaches allow for an improved identifiability of flow separation with regard to future applications on wind turbines in operation.

Can Communities Produce Complex Technology? Looking Into Space for Insight

This article examines a community producing complex space technology. We attempt to highlight which aspects of the community’s activities can help democratize high-tech development while providing a context for similar cases involved in developing and manufacturing nonhigh-technological artefacts. We discuss how this has been made possible by using a technology-determined organizational approach based on the CubeSat open platform infrastructure, blending formal and hands-on education, open communication, specific recruitment and working practices, and a genuine passion for technology. We identify as critical enablers for community-based collaborative development of space technology the open-source architecture standard called CubeSat Design Specifications, the modularization of work in subsystems and between different organizations, and the open and participatory approach work tasks distribution and decision making. Moreover, we argue that the digital/informational aspect of this technology allows the community to implement organizational practices that resemble how open-source movements over the internet produce complex digital artefacts like Wikipedia or Linux. ESTCube can shed light on community-driven complex technology development, providing lessons on what a democratized version of high technology would resemble and how open and digitalized technology can help develop the capacities of a community.

Research on Redundant Design Technique for USV Autonomous Control System

In the field of USV autonomous control system design, redundant design technology is the important method to improve system reliability. In order to solve the problem of insufficient reliability when USV carrying out long endurance and open sea task, this paper analyzes the critical design elements of autonomous control system redundant configuration, presents a high reliable hybrid redundant structure for USV autonomous control system, and realizes redundant strategy and fault-tolerant control. This paper forms the USV autonomous control system redundant configuration scheme comply with the fault-tolerant capability and redundant rank of USV autonomous control system. Redundancy management strategies and algorithms are used to implement USV autonomous control based on hybrid redundant design. The reliability analysis and computation validate task reliability of the autonomous control system based on hybrid redundant structure.

Lisen&Curate: A platform to facilitate gathering textual evidence for curation of regulation of transcription initiation in bacteria

The number of published papers in biomedical research makes it rather impossible for a researcher to keep up to date. This is where manually curated databases contribute facilitating the access to knowledge. However, the structure required by databases strongly limits the type of valuable information that can be incorporated. Here, we present Lisen&Curate, a curation system that facilitates linking sentences or part of sentences (both considered sources) in articles with their corresponding curated objects, so that rich additional information of these objects is easily available to users. These sources are going to be offered both within RegulonDB and a new database, L-Regulon. To show the relevance of our work, two senior curators performed a curation of 31 articles on the regulation of transcription initiation of E. coli using Lisen&Curate. As a result, 194 objects were curated and 781 sources were recorded. We also found that these sources are useful to develop automatic approaches to detect objects in articles by observing word frequency patterns and by carrying out an open information extraction task. Sources may help to elaborate a controlled vocabulary of experimental methods. Finally, we discuss our ecosystem of interconnected applications, RegulonDB, L-Regulon, and Lisen&Curate, to facilitate the access to knowledge on regulation of transcription initiation in bacteria. We see our proposal as the starting point to change the way experimentalists connect a piece of knowledge with its evidence using RegulonDB.

William Tolly and His Canal: Navigating Calcutta in the Lake-Eighteenth Century

This article studies the making of one particular canal in the port-city of Calcutta during the early years of English East India Company rule in Bengal. Major Tolly, a Company-servant, proposed to undertake the arduous task of opening up a navigable route connecting Calcutta with the eastern districts of the province for better trade and communication facilities. In the process, he was hopeful of making a good fortune for himself as well. But the sailing was not smooth. Tolly had to enter into various negotiations with the Company government regarding land, the right to hold property in Calcutta, and the role of the Company in defining those rights. He also faced difficulties with the local zamindars regarding collection of tolls, and the issue of maintenance of the canal. The Company administrators were also not unanimous in their opinions regarding these issues, which sometimes compounded the problem for Tolly. Through a discussion of the material history of this canal, this article proposes to look at the ways in which a mercantile power sought to create and consolidate its hold over a coastal enclave in a largely riverine province, negotiating and redefining a maze of seemingly incomprehensible political-economic considerations.

A novel radar target recognition method for open and imbalanced high-resolution range profile

High-resolution range profile (HRRP) data often has imbalanced and open-ended distribution in realistic radar target recognition task, requiring the recognition system classify targets among majority and minority classes and detect unseen targets accurately. In this paper, we propose a novel algorithm for open and imbalanced HRPP recognition tasks, by learning from realistic data distribution and optimizing the accuracy over the majority, minority and open classes. This method maps the HRRP data to the latent feature space, enhances the feature by sharing knowledge from majority to minority class, and provides a scalar indicating the familiarity to known classes. Dual-attention is developed to provide strong discriminative feature representation. An angular penalty is employed in the loss function to optimize the intra-class similarity and inter-class variability. Experiments on measured data prove that the proposed algorithm outperforms other existing methods under both close and open settings with accuracy increased significantly, and more discriminative representation. This study provides a promising and effective approach for open and imbalanced HRRP target recognition.

Structural Dynamics of Knowledge Networks

We investigate the structural patterns of the appearance and disappearance of links in dynamic knowledge networks. Human knowledge is nowadays increasingly created and curated online, in a collaborative and highly dynamic fashion. The knowledge thus created is interlinked in nature, and an important open task is to understand its temporal evolution. In this paper, we study the underlying mechanisms of changes in knowledge networks which are of structural nature, i.e., which are a direct result of a knowledge network's structure. Concretely, we ask whether the appearance and disappearance of interconnections between concepts (items of a knowledge base) can be predicted using information about the network formed by these interconnections. In contrast to related work on this problem, we take into account the disappearance of links in our study, to account for the fact that the evolution of collaborative knowledge bases includes a high proportion of removals and reverts. We perform an empirical study on the best-known and largest collaborative knowledge base, Wikipedia, and show that traditional indicators of structural change used in the link analysis literature can be classified into four classes, which we show to indicate growth, decay, stability and instability of links. We finally use these methods to identify the underlying reasons for individual additions and removals of knowledge links.

BRAIN ACTIVITY OF INDUSTRIAL DESIGNERS IN CONSTRAINED AND OPEN DESIGN: THE EFFECT OF GENDER ON FREQUENCY BANDS

AbstractIn this paper, we present results from an experiment using EEG to measure brain activity and explore EEG frequency power associated with gender differences of professional industrial designers while performing two prototypical stages of constrained and open design tasks, problem-solving and design sketching. Results indicate no main effect of gender. However, among other main effects, a consistent main effect of hemisphere for the six frequency bands under analysis was found. In the problem-solving stage, male designers show higher alpha and beta bands in channels of the prefrontal cortices and female designers in the right occipitotemporal cortex and secondary visual cortices. In the design sketching stage, male designers show higher alpha and beta bands in the right prefrontal cortex, and female designers in the right temporal cortex and left prefrontal cortex, where higher theta is also found. Prioritising different cognitive functions seem to play a role in each gender's approach to constrained and open design tasks. Results can be useful to design professionals, students and design educators, and for the development of methodological approaches in design research and education.

Potential of nafimidone derivatives against co-morbidities of epilepsy: In vitro, in vivo, and in silico investigations.

Nafimidone is known for its clinical antiepileptic effects and alcohol derivatives of nafimidone were reported be potent anticonvulsants. These compounds are structurally similar to miconazole, which is known to inhibit cholinesterases, protect neurons, and ameliorate cognitive decline. Herein, we aimed to reveal the potential of three nafimidone alcohol esters (5g, 5i, and 5k), which were previously reported for their anticonvulsant effects, against co-morbidities of epilepsy such as inflammatory and neuropathic pain, cognitive and behavioral deficits, and neuron death, and understand their roles in related pathways such as γ-butyric acid type A (GABAA ) receptor and cholinesterases using in vitro, in vivo and in silico methods. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) test was used for cytotoxicity evaluation, hippocampal slice culture assay for neuroprotection, formalin test for acute and inflammatory pain, sciatic ligation for neuropathic pain, Morris water maze and open field locomotor tasks for cognitive and behavioral deficits, radioligand binding for GABAA receptor affinity, spectrophotometric methods for cholinesterase inhibition in vitro, and molecular docking in silico. The compounds were non-toxic to fibroblast cells. 5 k was neuroprotective against kainic acid-induced neuron death. 5i reduced pain response of mice in both the acute and the inflammatory phases. 5i improved survival upon status epilepticus. The compounds showed no affinity to GABAA receptor but inhibited acetylcholinesterase, 5k also inhibited butyrylcholinesterase. The compounds were predicted to interact mainly with the peripheric anionic site of cholinesterase enzymes. The title compounds showed neuroprotective, analgesic, and cholinesterase inhibitory effects, thus they bear promise against certain co-morbidities of epilepsy with neurological insults.

Global multivariate model learning from hierarchically correlated data

Inverse statistical physics aims at inferring models compatible with a set of empirical averages estimated from a high-dimensional dataset of independently distributed equilibrium configurations of a given system. However, in several applications, such as biology, data result from stochastic evolutionary processes, and configurations are related through a hierarchical structure, typically represented by a tree, and therefore not independent. In turn, empirical averages of observables superpose intrinsic signals related to the equilibrium distribution of the studied system, and spurious historical (or phylogenetic) signals resulting from the structure underlying the data-generating process. The naive application of inverse statistical physics techniques therefore leads to systematic biases and an effective reduction of the sample size. To advance on the currently open task of extracting intrinsic signals from correlated data, we study a system described by a multivariate Ornstein–Uhlenbeck process defined on a finite tree. Using a Bayesian framework, we can disentangle covariances in the data corresponding to their multivariate Gaussian equilibrium distribution from those resulting from the historical correlations. Our approach leads to a clear gain in accuracy in the inferred equilibrium distribution, which corresponds to an effective two to fourfold increase in sample size.

Opening tasks, opening minds – a rediscovery of the open-ended approach

Open-ended tasks are designed such that they may have more than one correct solution or may be solved in more than one way. Although such tasks constitute an irreplaceable tool for prompting young learners to be actively and creatively involved in mathematical discourse, their implementation poses a challenge. Primary school students in Poland are usually offered algorithmic and rote teaching methods and are thus very often deprived of important elements of successful mathematics learning. The ubiquitous teacher-centred approach dedicates little time to any contribution from learners. The aim of this design research was to implement a change in early childhood mathematics education. The change comprised students creating and/or solving open-ended tasks in groups, thus promoting dialogic teaching. The results confirmed that students who are challenged with openended tasks through dialogic teaching not only genuinely engage in their activities, develop a better number sense and flexibility of thinking, but also help each other gain a deeper understanding of new concepts. Captured in this research were the synergistic images of the beauty of children’s reasoning and the beauty of mathematics as an open subject – an incentive for others to begin their journey with freedom of speech for young mathematicians.

The Effects of Urban and Economic Development on Coastal Zone Management

The land transformation process in the last decades produced the urbanization growth in flat and coastal areas all over the world. The combination of natural phenomena and human pressure is likely one of the main factors that enhance coastal dynamics. These factors lead to an increase in coastal risk (considered as the product of hazard, exposure, and vulnerability) also in view of future climate change scenarios. Although each of these factors has been intensively studied separately, a comprehensive analysis of the mutual relationship of these elements is an open task. Therefore, this work aims to assess the possible mutual interaction of land transformation and coastal management zones, studying the possible impact on local coastal communities. The idea is to merge the techniques coming from urban planning with data and methodology coming from the coastal engineering within the frame of a holistic approach. The main idea is to relate urban and land changes to coastal management. Then, the study aims to identify if stakeholders’ pressure motivated the deployment of rigid structures instead of shoreline variations related to energetic and sedimentary balances. The influence of coastal protection (described by an indicator taking into account the linear density of the rigid protection extension alongshore) measures have been considered. Finally, the economical performances in the period just after the land transformation have been studied by means of a dimensionless index based on the concept of the local unit. The method has been applied to the case study of the Abruzzo Region. Results reveal a large urbanization growth and a generalized over-protection of the coast with a resulting spatial alternation of shoreline retreat and advance. On the other hand, the analysis of the local economy reveals that the presence of tourism activities does not necessarily lead to an increase in the local economy. Indeed, the most important improvement in local economies in the region can be associated with coastal stretches characterized by a high environmental value.

OPEN PRACTICE-ORIENTED TASKS AS A TOOL FOR THE DEVELOPMENT OF INFORMATION LITERACY OF PRIMARY SCHOOL CHILDREN

The article deals with the actual problem of organizing the educational process aimed at developing information literacy in schoolchildren when studying mathematics at one of the most difficult stages of learning - at the stage of transition from primary to basic school. This paper describes some areas of information literacy development in primary school students; the analysis of the main groups of actions, with the help of which the ability of younger students to work with mathematical information can be formed; describes the results of the performance by younger students of a set of tasks aimed at the formation of information literacy based on the work of students with open practice-oriented tasks. The empirical experience of using the proposed methodology and the analysis of the results obtained made it possible to obtain data on the level of mastery of information literacy by younger students.