Miracle-dz/UniFAR-FTUs huggingface dataset:
Bringing Computational Thinking to Nonengineering Students through a Capstone Course

Although the concept of computational thinking has flourished, little research has explored how to integrate various elements of computational thinking into an undergraduate classroom setting. Clarifying core concepts of computational thinking and providing empirical cases that apply computational thinking practices into a real-world educational setting is crucial to the success of software engineering education. In this article, we describe the development of a curriculum for a social innovation capstone course, using core concepts and elements of computational thinking. The course was designed for undergraduate students of a liberal arts college at a university in Korea. Students were asked to define a social problem and introduced to the core concepts and processes of computational thinking aided by Arduino and Raspberry Pi programming environments. After building a business model, they implemented a working prototype for their proposed solution. We document class project outcomes and student feedback to demonstrate the effectiveness of the approach.
Code Complexity in Introductory Programming Courses

Instructors of introductory programming courses would benefit from having a metric for evaluating the sophistication of student code. Since introductory programming courses pack a wide spectrum of topics in a short timeframe, student code changes quickly, raising questions of whether existing software complexity metrics effectively reflect student growth as reflected in their code. We investigate code produced by over 800 students in two different Python-based CS1 courses to determine if frequently used code quality and complexity metrics (e.g., cyclomatic and Halstead complexities) or metrics based on length and syntactic complexity are more effective as a heuristic for gauging students’ progress through a course. We conclude that the traditional metrics do not correlate well with time passed in the course. In contrast, metrics based on syntactic complexity and solution size correlate strongly with time in the course, suggesting that they may be more appropriate for evaluating how student code evolves in
Robust Processing-In-Memory With Multibit ReRAM Using Hessian-Driven Mixed-Precision Computation

This article presents an algorithmic approach to design reliable deep neural networks (DNNs) in the presence of stochastic variations in the network parameters induced by process variations in the bit cells in a processing-in-memory (PIM) architecture. We propose and derive a Hessian-based sensitivity metric that can be computed without computing or storing the full Hessian to identify and protect the “important” network parameters while allowing large variations in unprotected parameters. We also show that this metric can be used to aggressively quantize unprotected network parameters in the PIM for improved inference efficiency and compute density. Experiments on modern DNNs like ResNet, MobileNetv2, and DenseNet on CIFAR10 using measured RRAM device data shows the effectiveness of our approach.
Secondary Radio and X-Ray Emissions from Galaxy Mergers

Shocks arising in galaxy mergers could accelerate cosmic-ray (CR) ions to TeV–PeV energies. While propagating in the intergalactic medium, these CRs can produce high-energy neutrinos, electron–positron pairs, and gamma-rays. In the presence of intergalactic magnetic fields, the secondary pairs will radiate observable emissions through synchrotron radiation and inverse Compton scattering. In this paper, we demonstrate that these emissions can explain the radio and X-ray fluxes of merging galaxies such as NGC 660 and NGC 3256. Using our model in combination with the observations, we can constrain the gas mass, shock velocity, magnetic field, and the CR spectral index s of these systems. For NGC 660, a single-zone model with a spectral index 2.1 ≲ s ≲ 2.2 is able to simultaneously reproduce the radio and X-ray observations, while a simple one-zone scenario with s ∼ 2 can describe the radio and a large fraction of X-ray observations of NGC 3256. Our work provides a useful approach for studying the dynamics and physical parameters of galaxy mergers, which can play an important part in future multimessenger studies of similar and related extragalactic sources.
How Digital Nudges Affect Consideration Set Size and Perceived Cognitive Effort in Idea Convergence of Open Innovation Contests

Open innovation initiatives are useful to acquire many ideas, but often face problems when it comes to selecting the best ideas. Idea convergence has been suggested as a first step in idea selection to filter those ideas that are worthy of further consideration. Digital nudges – digital interventions that aim at altering human behavior in a predictable way could support convergence. However, their effects are largely unknown. This study explores how two digital nudges, selection strategy (inclusion/exclusion) and idea subset similarity (similar/random), affect the convergence outcomes consideration set size and perceived cognitive effort. We conducted a laboratory experiment with 88 students and found that guiding individuals towards an inclusion strategy results in smaller consideration sets and higher perceived cognitive effort. Moreover, presenting individuals with subsets of similar ideas resulted in smaller consideration sets. These insights are relevant for the design and use of digital nudges for convergence in open innovation environments.
Gamification of E-Participation: A Literature Review

Gamification is one of the most commonly employed approaches for motivating individuals to participate in several types of activities. One of its largest application areas has been e-participation (i.e. citizen engagement in policy-making). Even though the required ICT infrastructure to facilitate e-participation mostly exists today, the focus of the problem has shifted towards humans; citizens are not motivated enough to participate. Gamification is a potential approach to increase motivation towards eparticipation. However, currently there is a dearth in our understanding of how gamification is being applied and researched as well as what kinds of result there exist from gamification. The aim of this paper is to synthesize research and findings on gamified eparticipation, providing directions for future research in this area.
Evolved embodied phase coordination enables robust quadruped robot locomotion

Overcoming robotics challenges in the real world requires resilient control systems capable of handling a multitude of environments and unforeseen events. Evolutionary optimization using simulations is a promising way to automatically design such control systems, however, if the disparity between simulation and the real world becomes too large, the optimization process may result in dysfunctional real-world behaviors. In this paper, we address this challenge by considering embodied phase coordination in the evolutionary optimization of a quadruped robot controller based on central pattern generators. With this method, leg phases, and indirectly also inter-leg coordination, are influenced by sensor feedback. By comparing two very similar control systems we gain insight into how the sensory feedback approach affects the evolved parameters of the control system, and how the performances differ in simulation, in transferal to the real world, and to different real-world environments. We show that evolution enables the design of a control system with embodied phase coordination which is more complex than previously seen approaches, and that this system is capable of controlling a real-world multi-jointed quadruped robot. The approach reduces the performance discrepancy between simulation and the real world, and displays robustness towards new environments.
Hunting for brown dwarfs in the globular cluster M4: second epoch HST NIR observations

We present an analysis of the second epoch Hubble Space TelescopeWide Field Camera 3 F110W near-infrared (NIR) imaging data of the globular cluster M 4. The new data set suggests that one of the previously suggested four brown dwarf candidates in this cluster is indeed a high-probability cluster member. The position of this object in the NIR colour–magnitude diagrams (CMDs) is in the white dwarf/brown dwarf area. The source is too faint to be a low-mass main-sequence (MS) star, but, according to theoretical considerations, also most likely somewhat too bright to be a bona-fide brown dwarf. Since we know that the source is a cluster member, we determined a new optical magnitude estimate at the position the source should have in the optical image. This new estimate places the source closer to the white dwarf sequence in the optical–NIR CMD and suggests that it might be a very cool (Teff ≤ 4500 K) white dwarf at the bottom of the white dwarf cooling sequence in M 4, or a white dwarf/brown dwarf binary. We cannot entirely exclude the possibility that the source is a very massive, bright brown dwarf, or a very low-mass MS star, however, we conclude that we still have not convincingly detected a brown dwarf in a globular cluster, but we expect to be very close to the start of the brown dwarf cooling sequence in this cluster. We also note that the MS ends at F110W ≈ 22.5 mag in the proper-motion cleaned CMDs, where completeness is still high.
Visual Localization Using Sparse Semantic 3D Map

Accurate and robust visual localization under a wide range of viewing condition variations including season and illumination changes, as well as weather and day-night variations, is the key component for many computer vision and robotics applications. Under these conditions, most traditional methods would fail to locate the camera. In this paper we present a visual localization algorithm that combines structure-based method and image-based method with semantic information. Given semantic information about the query and database images, the retrieved images are scored according to the semantic consistency of the 3D model and the query image. Then the semantic matching score is used as weight for RANSAC’s sampling and the pose is solved by a standard PnP solver. Experiments on the challenging long-term visual localization benchmark dataset demonstrate that our method has significant improvement compared with the state-of-the-arts.
Analytical Modeling of High-Torque-Density Spoke-Type Permanent Magnet In-Wheel Motor Accounting for Rotor Slot and Eccentric Magnetic Pole

In this article, a hybrid analytical model combining the subdomain method and the lumped-parameter magnetic circuit method (LMCM) is presented for the analysis of a high-torque-density spoke-type permanent magnet in-wheel motor considering rotor slot and eccentric magnetic pole. In the proposed model, the air-gap field distribution is derived using the subdomain method, while the saturation and the axial flux leakage of the iron core are considered by LMCM. The rectangular-shaped spoke magnets, eccentric magnetic poles, and rotor slots are approximated by multiple layers of arc-shaped subdomains. The magnetic equivalent circuit (MEC) of the stator and rotor is established, and the nonlinearity of the iron core is taken into account. Based on the developed hybrid analytical model, electromagnetic performance, such as cogging torque, electromagnetic torque, flux linkage, and back electromotive force, are obtained. The analytical results are verified by the finite-element method, and its application in the optimization process is investigated. Finally, a 1800-Nm high-torque prototype machine is manufactured and tested, and the results validate the accuracy and efficiency of the analytical method.
Functional Geometry of Human Connectome and Robustness of Gender Differences

Mapping the brain imaging data to networks, where each node represents a specific area of the brain, has enabled an objective graph-theoretic analysis of human connectome. However, the latent structure on higher-order connections remains unexplored, where many brain regions acting in synergy perform complex functions. Here we analyse this hidden structure using the simplicial complexes parametrisation where the shared faces of simplexes encode higher-order relationships between groups of nodes and emerging hyperbolic geometry. Based on data collected within the Human Connectome Project, we perform a systematic analysis of consensus networks of 100 female (F-connectome) and 100 male (M-connectome) subjects by varying the number of fibres launched. Our analysis reveals that the functional geometry of the common F\&M-connectome coincides with the M-connectome and is characterized by a complex architecture of simplexes to the 14th order, which is built in six anatomical communities, and short cycles among them. Furthermore, the F-connectome has additional connections that involve different brain regions, thereby increasing the size of simplexes and introducing new cycles. By providing new insights into the internal organisation of anatomical brain modules as well as into the links between them that are essential to dynamics, these results also highlight the functional gender-related differences
A log-barrier Newton-CG method for bound constrained optimization with complexity guarantees

We describe an algorithm based on a logarithmic barrier function, Newton’s method and linear conjugate gradients that seeks an approximate minimizer of a smooth function over the non-negative orthant. We develop a bound on the complexity of the approach, stated in terms of the required accuracy and the cost of a single gradient evaluation of the objective function and/or a matrix-vector multiplication involving the Hessian of the objective. The approach can be implemented without explicit calculation or storage of the Hessian.
In vitro cultivation and acclimatization of Miltonia flavescens (Lindl.) Lindl. with ora-pro-nobis extract (Pereskia aculeata Mill.)

The aim of this study was to evaluate the effect of aqueous ora-pro-nobis (Pereskia aculeata Mill.) extract on in vitro growth, survival rate, and acclimatization of Miltonia flavescens. Crude ora-pro-nobis extract was produced by infusing 10 g of dry leaves fractionated in 100 mL of distilled water. The treatments consisted of the crude extract (100%) and dilutions of 75%, 50%, and 25% added to Murashige and Skoog 1⁄2 culture medium (control treatment: absence of extract) where seedlings germinated in vitro were cultivated. After 270 days of cultivation, growth data were collected, and the plants acclimated. The survival rate was evaluated every 30 days, and the growth of the aerial part and root system were measured after 120 days of acclimatization. The data were submitted to analysis of variance and a test of the separation of means (Skott-Knott). For the number of leaves, the 100% treatment had the highest average value, differing statistically from the other treatments. For the length of the root system and fresh mass, the highest averages occurred in the control and 100% treatments. For the number of roots and shoots, there were no differences between treatments. After 120 days of acclimatization, plants from the 100% treatment had the lowest survival percentage, and plants from the 25% and 50% treatments had the greatest increase in the growth of the aerial part and root systems, demonstrating these treatments would be beneficial for the cultivation of this species.
Time-contingent Impact of Inconsistent Action-based Information Cues on Social Commerce Purchases: An Experimental Investigation

Social commerce websites predominantly display two types of action-based online social information: product’s past purchases and bookmarks (e.g. wishlists). The impact of inconsistency between these two information cues on consumer decision making is uncertain and is expected to be dependent on the purchase context. In this paper we investigate the effect of (action-based) online social information inconsistency on consumers’ likelihood of purchasing a product for temporally proximal and distant purchases. Using a controlled experimental set-up with Latinsquare design and linear mixed model analysis we find significant interaction effect of information inconsistency type and temporal distance of purchase on purchase likelihood of product, establishing the purchase timing dependent impact of information inconsistency. The paper offers several academic implications, and valuable insights for website managers to elicit favorable consumer responses even under information inconsistency and effectively design their product recommendation strategies.
VAE-based regularization for deep speaker embedding

Deep speaker embedding has achieved state-of-the-art performance in speaker recognition. A potential problem of these embedded vectors (called `x-vectors') are not Gaussian, causing performance degradation with the famous PLDA back-end scoring. In this paper, we propose a regularization approach based on Variational Auto-Encoder (VAE). This model transforms x-vectors to a latent space where mapped latent codes are more Gaussian, hence more suitable for PLDA scoring.
Few-Shot Learning via Saliency-Guided Hallucination of Samples

Learning new concepts from a few of samples is a standard challenge in computer vision. The main directions to improve the learning ability of few-shot training models include (i) a robust similarity learning and (ii) generating or hallucinating additional data from the limited existing samples. In this paper, we follow the latter direction and present a novel data hallucination model. Currently, most datapoint generators contain a specialized network (i.e., GAN) tasked with hallucinating new datapoints, thus requiring large numbers of annotated data for their training in the first place. In this paper, we propose a novel less-costly hallucination method for few-shot learning which utilizes saliency maps. To this end, we employ a saliency network to obtain the foregrounds and backgrounds of available image samples and feed the resulting maps into a two-stream network to hallucinate datapoints directly in the feature space from viable foreground-background combinations. To the best of our knowledge, we are the first to leverage saliency maps for such a task and we demonstrate their usefulness in hallucinating additional datapoints for few-shot learning. Our proposed network achieves the state of the art on publicly available datasets.
Graph Pattern Entity Ranking Model for Knowledge Graph Completion

Knowledge graphs have been developed rapidly in recent years and shown their usefulness for many artificial intelligence tasks. However, knowledge graphs often have lots of missing facts. To solve this problem, many knowledge graph embedding models to populate knowledge graphs have been developed and have shown outstanding performance these days. However, knowledge graph embedding models are so called-black box. Hence, we actually does not know how information of a knowledge graph is processed and the models are hard to interpret. In this paper, we utilize graph patterns in a knowledge graph to overcome such problems. Our proposed model, graph pattern entity ranking Model (GRank), constructs an entity ranking system for each graph pattern and evaluate them using a measure for a ranking system. By doing so, we can find helpful graph patterns for predicting facts. Then we conduct the link prediction tasks on standard data sets to evaluate GRank. We show our approach outperforms other state-of-the-art approaches such as ComplEx and TorusE on standard metrics such as HITS@n and MRR. Moreover, This model is easily interpretable because output facts are described by graph patterns.
Improving Dialogue State Tracking by Discerning the Relevant Context

A typical conversation comprises of multiple turns between participants where they go back and forth between different topics. At each user turn, dialogue state tracking (DST) aims to estimate user’s goal by processing the current utterance. However, in many turns, users implicitly refer to the previous goal, necessitating the use of relevant dialogue history. Nonetheless, distinguishing relevant history is challenging and a popular method of using dialogue recency for that is inefficient. We, therefore, propose a novel framework for DST that identifies relevant historical context by referring to the past utterances where a particular slot-value changes and uses that together with weighted system utterance to identify the relevant context. Specifically, we use the current user utterance and the most recent system utterance to determine the relevance of a system utterance. Empirical analyses show that our method improves joint goal accuracy by 2.75% and 2.36% on WoZ 2.0 and Multi-WoZ restaurant domain datasets respectively over the previous state-of-the-art GLAD model.
Trust and risky technologies: Aligning and coping with Tesla Autopilot

Products are increasingly digitized, and they incorporate digital components, smart features and partial automation. Modern cars are a prime example of consumer-oriented automation; they sense the environment and perform specific driving tasks on the driver’s behalf. The driving assistance and safety features provided by automation are under constant development, and as these features evolve, drivers experience and learn about their capabilities as they use them and develop their trust in automation in the light of new experiences and information. In this paper, we present a study on how trust in car automation unfolds as users gain experiences and information that conflicts with their expectations concerning the level of automation. We use Tesla Model S car as our case technology and explore how its users develop their trust and cope with issues with the novel automation technology. Our findings suggest important directions for future research of consumeroriented automation and digitized products.
VayuAnukulani: Adaptive Memory Networks for Air Pollution Forecasting

Air pollution is the leading environmental health hazard globally due to various sources which include factory emissions, car exhaust, and cooking stoves. As a precautionary measure, air pollution forecast serves as the basis for taking effective pollution control measures, and accurate air pollution forecasting has become an important task. In this paper, we forecast fine-grained ambient air quality information for 5 prominent locations in Delhi based on the historical and real-time ambient air quality and meteorological data reported by Central Pollution Control board. We present VayuAnukulani system, a novel end-to-end solution to predict air quality for next 24 hours by estimating the concentration and level of different air pollutants including nitrogen dioxide (NO2), particulate matter (PM2.5 and PM10) for Delhi. Extensive experiments on data sources obtained in Delhi demonstrate that the proposed adaptive attention based Bidirectional LSTM Network outperforms several baselines for classification and regression models. The accuracy of the proposed adaptive system is ~15–20% better than the same offline trained model. We compare the proposed methodology on several competing baselines, and show that the network outperforms conventional methods by ~7–18%. 1