Reviewerly/exHarmony huggingface dataset:
MobileNet-Yolo based wildlife detection model: A case study in Yunnan Tongbiguan Nature Reserve, China

YTNR (Yunnan Tongbiguan Nature Reserve) is located in the westernmost part of China’s tropical regions and is the only area in China with the tropical biota of the Irrawaddy River system. The reserve has abundant tropical flora and fauna resources. In order to realize the real-time detection of wild animals in this area, this paper proposes an improved YOLO (You only look once) network. The original YOLO model can achieve higher detection accuracy, but due to the complex model structure, it cannot achieve a faster detection speed on the CPU detection platform. Therefore, the lightweight network MobileNet is introduced to replace the backbone feature extraction network in YOLO, which realizes real-time detection on the CPU platform. In response to the difficulty in collecting wild animal image data, the research team deployed 50 high-definition cameras in the study area and conducted continuous observations for more than 1,000 hours. In the end, this research uses 1410 images of wildlife collected in the field and 1577 wildlife images from the internet to construct a research data set combined with the manual annotation of domain experts. At the same time, transfer learning is introduced to solve the problem of insufficient training data and the network is difficult to fit. The experimental results show that our model trained on a training set containing 2419 animal images has a mean average precision of 93.6% and an FPS (Frame Per Second) of 3.8 under the CPU. Compared with YOLO, the mean average precision is increased by 7.7%, and the FPS value is increased by 3.
Searching by Generating: Flexible and Efficient One-Shot NAS with Architecture Generator

In one-shot NAS, sub-networks need to be searched from the supernet to meet different hardware constraints. However, the search cost is high and N times of searches are needed for N different constraints. In this work, we propose a novel search strategy called architecture generator to search sub-networks by generating them, so that the search process can be much more efficient and flexible. With the trained architecture generator, given target hardware constraints as the input, N good architectures can be generated for N constraints by just one forward pass without re-searching and supernet retraining. Moreover, we propose a novel single-path supernet, called unified supernet, to further improve search efficiency and reduce GPU memory consumption of the architecture generator. With the architecture generator and the unified supernet, we propose a flexible and efficient one-shot NAS framework, called Searching by Generating NAS (SGNAS). With the pre-trained supernt, the search time of SGNAS for N different hardware constraints is only 5 GPU hours, which is 4N times faster than previous SOTA single-path methods. After training from scratch, the top1-accuracy of SGNAS on ImageNet is 77.1%, which is comparable with the SOTAs. The code is available at: https://github.com/eric8607242/SGNAS.
Effect of game-based, social media, and classroom-based instruction on the learning of phrasal verbs

Mobile-assisted language learning (MALL), which provides access to learning without the constraints of place and time, is worthy of exploration for pedagogical purposes. Given the use of gaming applications and social media as potential instructional methods for MALL, this study investigated the effect of these two methods, along with traditional classroom-based instruction on the learning of English phrasal verbs. To teach phrasal verbs through three different methods (game-based, social media, and classroom-based), 150 learners of English, selected from a language institute in Isfahan, were assigned to game-based, social media, and classroom-based groups. The game-based group received instruction on phrasal verbs via a mobile gaming application called Phrasal Nerds: Phrasal Verbs. The social media group received instruction via a Telegram channel. Finally, the classroom-based group was taught through the traditional teacher-fronted method. A researcher-made test served as the pretest and posttest to measure the contribution of each instructional method to the learners’ phrasal verb achievements. The analysis of data through t-test and analysis of covariance (ANCOVA) indicated improvements in learning English phrasal verbs in the three groups. Although the effects of the three methods were found to be statistically significant, the game-based method was the most effective one for learning English phrasal verbs. The findings highlight the integration of gaming applications into the English teaching curriculum to enhance foreign language lexical learning.
Exploiting Method Names to Improve Code Summarization: A Deliberation Multi-Task Learning Approach

Code summaries are brief natural language descriptions of source code pieces. The main purpose of code summarization is to assist developers in understanding code and to reduce documentation workload. In this paper, we design a novel multi-task learning (MTL) approach for code summarization through mining the relationship between method code summaries and method names. More specifically, since a method's name can be considered as a shorter version of its code summary, we first introduce the tasks of generation and informativeness prediction of method names as two auxiliary training objectives for code summarization. A novel two-pass deliberation mechanism is then incorporated into our MTL architecture to generate more consistent intermediate states fed into a summary decoder, especially when informative method names do not exist. To evaluate our deliberation MTL approach, we carried out a large-scale experiment on two existing datasets for Java and Python. The experiment results show that our technique can be easily applied to many state-of-the-art neural models for code summarization and improve their performance. Meanwhile, our approach shows significant superiority when generating summaries for methods with non-informative names.
Collision-free and dynamically feasible trajectory planning for omnidirectional mobile robots using a novel B-spline based rapidly exploring random tree

Generating a collision-free and dynamically feasible trajectory with a better clearance in a cluttered environment is still a challenge. We propose two dynamically feasible B-spline based rapidly exploring random tree (RRT) approaches, which are named DB-RRT and FMDB-RRT, to achieve path planning and trajectory planning simultaneously for omnidirectional mobile robots. DB-RRT combines the convex hull property of the B-spline and RRT’s rapid expansion capability to generate a safe and dynamically feasible trajectory. Firstly, we analyze the tree’s sustainable growth ability and put forward the dynamically feasible region. A geometric method is proposed to judge whether finding a dynamically feasible trajectory quickly. Secondly, we design two steer functions to guide the tree’s growth, improve efficiency, and decrease the number of iterations. To further increase the clearance and reduce the randomness of the trajectory, we propose FMDB-RRT, which uses the path of fast marching to guide the rapid growth of DB-RRT. Then, assuming that the number of sampled points is sufficient to represent the dynamically feasible region, the DB-RRT is proved to be probabilistically complete. Finally, by conducting experimental comparisons with other algorithms in different environments and deploying the proposed algorithm to an omnidirectional mobile robot, the effectiveness and good performance of the algorithm have been verified.
ShuffleNet-inspired lightweight neural network design for automatic modulation classification methods in ubiquitous IoT cyber–physical systems

Automatic modulation classification (AMC) is one of the most important technologies of cognitive radios and ubiquitous internet of things (IoT) cyber–physical systems, and it can be adopted to recognize unknown signals. Recently, deep learning (DL) has been applied into AMC for the advanced classification performance. However, DL-based AMC methods generally have high computation complexity and large model sizes, which means that these methods can be rarely implemented into some IoT devices. In this paper, inspired by ShuffleNet, we design a lightweight convolutional neural network (CNN), which is named as ShuffleCNN, and a ShuffleCNN-based AMC (ShffuleAMC) method is proposed for the ubiquitous IoT cyber–physical systems with orthogonal frequency division multiplexing (OFDM). Besides, we also introduce fast Fourier transform (FFT) to pre-process the OFDM signals for the classification performance improvement, and apply ℓ2 regularization to avoid overfitting. It is demonstrated by simulation results that our proposed ShuffleAMC method has little performance loss, when compared with the common CNN-based AMC methods. More importantly, our proposed ShuffleAMC method also has the strengths of low computation complexity and few model sizes.
Polka Lines: Learning Structured Illumination and Reconstruction for Active Stereo

Active stereo cameras that recover depth from structured light captures have become a cornerstone sensor modality for 3D scene reconstruction and understanding tasks across application domains. Active stereo cameras project a pseudo-random dot pattern on object surfaces to extract disparity independently of object texture. Such hand-crafted patterns are designed in isolation from the scene statistics, ambient illumination conditions, and the reconstruction method. In this work, we propose a method to jointly learn structured illumination and reconstruction, parameterized by a diffractive optical element and a neural network, in an end-to-end fashion. To this end, we introduce a differentiable image formation model for active stereo, relying on both wave and geometric optics, and a trinocular reconstruction network. The jointly optimized pattern, which we dub "Polka Lines," together with the reconstruction network, makes accurate active-stereo depth estimates across imaging conditions. We validate the proposed method in simulation and using with an experimental prototype, and we demonstrate several variants of the Polka Lines patterns specialized to the illumination conditions.
Modern Art Education and Teaching Based on Artificial Intelligence

The rapid advancement of artificial intelligence has been intensely employed in art teaching and learning. Including the advancement of smart technologies, there are various difficulties in improving the teaching capability of technical art design courses, including the impact of several variables and the absence of quantitative study, and the imperfection in the index system. The paper proposes the Artificial Intelligence assisted Effective Art Teaching Framework (AIEATF) to expand the ability to adapt to AI-oriented art instruction, develop intelligent teaching styles, and enhance AI-oriented art teaching art knowledge and environment. The potential of improving AI’s effects on major art courses’ teaching effect has been illustrated in detail. On this basis, an assessment model has been developed to consider the enhancing effects. The study’s findings include a valuable guide for educators in art design to strengthen their teaching ability. The experimental results have shown that Modern Painting Perfection Ratio is 87.66%, Computer graphical representation ratio is 88.77%, Photographical Design Percentage ratio is 84.50%, Performance of Carving in Sculpture Ratio is 82.26%, Construction Development Ratio is 93.83%, Expressive Musical Performing Ratio is 92.70%, Energized Dance Performance Ratio is 84.26%, and overall performance ratio is 92.30%.
Depth from Camera Motion and Object Detection

This paper addresses the problem of learning to estimate the depth of detected objects given some measurement of camera motion (e.g., from robot kinematics or vehicle odometry). We achieve this by 1) designing a recurrent neural network (DBox) that estimates the depth of objects using a generalized representation of bounding boxes and uncalibrated camera movement and 2) introducing the Object Depth via Motion and Detection Dataset (ODMD). ODMD training data are extensible and configurable, and the ODMD benchmark includes 21,600 examples across four validation and test sets. These sets include mobile robot experiments using an end-effector camera to locate objects from the YCB dataset and examples with perturbations added to camera motion or bounding box data. In addition to the ODMD benchmark, we evaluate DBox in other monocular application domains, achieving state-of-the-art results on existing driving and robotics benchmarks and estimating the depth of objects using a camera phone.
Sarcasm Detection using Cognitive Features of Visual Data by Learning Model

The objective of the paper is to detect sarcasm in human communication. The methodology uses basic cognitive features of human utterances by capturing three modes of data viz., voice, text, and temporal facial features. The captured data is unstructured as it consists of parameters of feelings and emotions to generate sarcasm which affects expressions through glottal and facial organs. The data capturing method is equally challenging as compared to the method of data processing to acquire features. The significant work is aligned to make natural decisions in the prediction processes using cognitive information in the data lineage. Sarcasm detection in natural human communication is a challenging process. The Linguistic features of natural language processing (NLP) methods help identify sentiment as negative and positive sentences based on polarity using the pre-labelled samples. The multiclass neural network model is used as a soft cognition method for the detection of sarcasm under cloud resources. Identified cognitive features have information like voice cues and eye movements, they tend to influence the decision of detecting sarcasm. The visual data are found to be quite interesting and can establish a strong platform in the area of NLP for further research work.
“Alexa, let me ask you something different” Children's adaptive information search with voice assistants

In this study, we investigated how children interact with voice assistants, particularly focusing on what kinds of questions they ask and how they react to the responses obtained. We recorded 3- to 10-year-old children's (N = 43) spontaneous interactions with Amazon Alexa, and analyzed the questions they asked, as well as how they adjusted their information search based on the responses received. Our results confirm previous work in showing that children's questions are mostly information-seeking, yet the type of questions children ask also depends on their age and familiarity with voice assistants. For example, children who are younger and less familiar with voice assistants are more likely to ask questions about themselves and their environment (e.g., “What is my sister's name?”). We also show for the first time that, even though all children are sensitive to the relevance and accuracy of voice assistants' responses to a certain extent, older children are more likely to change the topic and type of the questions asked upon receiving irrelevant or uninformative responses. This study shows that, with age and familiarity, children become more sensitive to the behavior, informativeness, and constraints of artificial agents, growing into adaptive and sophisticated technology users.
Top-rank convolutional neural network and its application to medical image-based diagnosis

Top-rank learning identifies a real-valued ranking function that will provide more absolute top samples. These are highly reliable positive samples that are ranked higher than the highest-ranked negative samples. Therefore, top-rank learning is useful for tasks that require reliable decisions. Additionally, it inherits the merits of the ranking functions, such as robustness to the unbalanced condition. However, conventional top-rank learning tasks are formulated as linear or kernel-based problems and are thus limited in coping with complicated tasks. In this study, we propose a Top-rank convolutional neural network (TopRank CNN) to realize top-rank learning with representation learning for complicated tasks. Given that the original objective function of top-rank learning suffers from overfitting, we employ the p-norm relaxation of the original loss function in the proposed method. We prove the usefulness of TopRank CNN experimentally with medical diagnosis tasks that require reliable decisions and robustness to the unbalanced condition.
An entirely reverse-engineered finite element model of a classical guitar in comparison with experimental data

The classical guitar is a popular string instrument in which the sound results from a coupled mechanical process. The oscillation of the plucked strings is transferred through the bridge to the body, which acts as an amplifier to radiate the sound. In this contribution, a procedure to create a numerical finite element (FE) model of a classical guitar with the help of experimental data is presented. The geometry of the guitar is reverse-engineered from computed tomography scans to a very high level of detail, and care is taken in including all necessary physical influences. All of the five different types of wood used in the guitar are modeled with their corresponding orthotropic material characteristics, and the fluid-structure interaction between the guitar body and the enclosed air is taken into account by discretizing the air volume inside the guitar with FEs in addition to the discretization of the structural parts. Besides the numerical model, an experimental setup is proposed to identify the modal parameters of a guitar. The procedure concludes with determining reasonable material properties for the numerical model using experimental data. The quality of the resulting model is demonstrated by comparing the numerically calculated and experimentally identified modal parameters.
Machine learning-based analysis of operator pupillary response to assess cognitive workload in clinical ultrasound imaging

Pupillometry, the measurement of eye pupil diameter, is a well-established and objective modality correlated with cognitive workload. In this paper, we analyse the pupillary response of ultrasound imaging operators to assess their cognitive workload, captured while they undertake routine fetal ultrasound examinations. Our experiments and analysis are performed on real-world datasets obtained using remote eye-tracking under natural clinical environmental conditions. Our analysis pipeline involves careful temporal sequence (time-series) extraction by retrospectively matching the pupil diameter data with tasks captured in the corresponding ultrasound scan video in a multi-modal data acquisition setup. This is followed by the pupil diameter pre-processing and the calculation of pupillary response sequences. Exploratory statistical analysis of the operator pupillary responses and comparisons of the distributions between ultrasonographic tasks (fetal heart versus fetal brain) and operator expertise (newly-qualified versus experienced operators) are performed. Machine learning is explored to automatically classify the temporal sequences into the corresponding ultrasonographic tasks and operator experience using temporal, spectral, and time-frequency features with classical (shallow) models, and convolutional neural networks as deep learning models. Preliminary statistical analysis of the extracted pupillary response shows a significant variation for different ultrasonographic tasks and operator expertise, suggesting different extents of cognitive workload in each case, as measured by pupillometry. The best-performing machine learning models achieve receiver operating characteristic (ROC) area under curve (AUC) values of 0.98 and 0.80, for ultrasonographic task classification and operator experience classification, respectively. We conclude that we can successfully assess cognitive workload from pupil diameter changes measured while ultrasound operators perform routine scans. The machine learning allows the discrimination of the undertaken ultrasonographic tasks and scanning expertise using the pupillary response sequences as an index of the operators' cognitive workload. A high cognitive workload can reduce operator efficiency and constrain their decision-making, hence, the ability to objectively assess cognitive workload is a first step towards understanding these effects on operator performance in biomedical applications such as medical imaging.
Blockchain on stool land acquisition: Lessons from Ghana for strengthening land tenure security other than titling

The paper develops a blockchain-enabled framework for stool land acquisition in Ghana that makes it possible for parties with interest in lands to object to their sale if the transaction is being conducted on their blind side. The framework also makes it possible for potential land buyers to authenticate in real-time the ownership status of the land they want to acquire–whether the property is registered with the Lands Commission or has been deregistered from the Commission’s records. The framework is a response to Ghana’s current opaque and weakly coordinated land administration systems flourishing the phenomenon of multiple sale of same lands and other land tenure security problems in the country. Further to providing a blockchain-enabled stool lands acquisition framework that enhances transparency, accountability, and better land records keeping in Ghana, the paper takes issues with the view widely held in international development, government, judicial, and some academic circles that titling or lease registration is the only or primary viable means of securing land rights. Our findings from Ghana show that in addition to creating predatory economies for powerful land sector actors (e.g., chiefs and land administrators) to extort outrageous sums of money from land buyers, the heavy emphasis on land titling or lease registration undermines efforts at strengthening other less cumbersome, less costly and less corruption-laden aspects of land administration to improve property rights in the land sector.
Fair Feature Distillation for Visual Recognition

Fairness is becoming an increasingly crucial issue for computer vision, especially in the human-related decision systems. However, achieving algorithmic fairness, which makes a model produce indiscriminative outcomes against protected groups, is still an unresolved problem. In this paper, we devise a systematic approach which reduces algorithmic biases via feature distillation for visual recognition tasks, dubbed as MMD-based Fair Distillation (MFD). While the distillation technique has been widely used in general to improve the prediction accuracy, to the best of our knowledge, there has been no explicit work that also tries to improve fairness via distillation. Furthermore, We give a theoretical justification of our MFD on the effect of knowledge distillation and fairness. Throughout the extensive experiments, we show our MFD significantly mitigates the bias against specific minorities without any loss of the accuracy on both synthetic and real-world face datasets.