Reviewerly/exHarmony huggingface dataset:
Rigid class scheduling and its value for online learning in higher education

The spread of coronavirus infection brings changes to all spheres of activity, including education, which is increasingly moving to a distance learning format. The ultimate goal of this study was to investigate the effectiveness of the recommendations for developing a rigid class scheduling method in the framework of online learning using advanced approaches updating traditional learning mechanisms. Overall, this research was conducted among 226 students and 54 teachers representing Moscow Aviation Institute and Kazan Federal University. Research methodology basis was represented by a sociological survey, which allowed identifying the problems of implementation of online learning from the students’ standpoint. The collected outcomes showed that for 35% of students, one of the main problems in distance learning was the lack of self-discipline, which can be further adjusted by a clearly established schedule of classes (37% of respondents) and tests (29% of respondents). At the same time, the collected data showed that the learning process was most efficient in subgroups 1 (0.5), 2 (0.3), and 5 (0.45). Recommendations provided for further distance education improvement, first of all, related to schedule formation issues. More precisely, the paper emphasized the need for time constraints, even distribution of subjects, use of special applications, designation of dates for knowledge checking, and inclusion of additional learning materials. The findings and results of this work are believed to be of scientific value for the methodological departments of educational institutions responsible for curricula preparation as they are expected to significantly contribute to educational process advancement.
On the effectiveness of testing sentiment analysis systems with metamorphic testing

Metamorphic testing (MT) has been successfully applied to a wide scope of software systems. In these applications, the testing results of MT form the basis for drawing conclusions about the target system’s performance. Therefore, the effectiveness of MT is crucial to the trustfulness of the derived conclusions. However, due to the nature of MT, its effectiveness can be affected by various factors. Despite of MT’s success, it is still important to study its effectiveness under different application contexts. To investigate the effectiveness of MT, we focus on an important aspect, namely, false satisfactions (which are satisfactions of metamorphic relations that involve at least one failing execution), and revisit the application of MT to sentiment analysis (SA) systems. An in-depth analysis of the essence of false satisfactions reveals the situations where they would occur, and how they would affect the effectiveness of MT. Furthermore, 20 metamorphic relations (MRs) are identified for supporting a user-oriented evaluation of SA systems. The occurrence rates of false satisfactions are reported with respect to four SA systems. For the majority of MRs, false satisfactions account for about 20% to 50% of all MR satisfactions, suggesting that false satisfactions occur quite frequently in the evaluation of SA systems. It is also demonstrated that such high occurrence rates of false satisfactions adversely affect the users’ selection of SA systems. Our analysis reveals that without considering the occurrence of false satisfactions, MT may overestimate the system’s conformance to the relevant MR. Furthermore, our experiments empirically show that conclusions derived from MT can be adversely affected when there are many false satisfactions. Our findings will help the MT community to adopt a more fair and reliable way of using the test outcomes of MT, and can also inspire the development of solid foundations for MT.
A cloud Bayesian network approach to situation assessment of scouting underwater targets with fixed‐wing patrol aircraft

Abstract The battlefield situation changes rapidly because underwater targets' are concealment and the sea environment is uncertain. So, a great number of situation information greatly increase, which need to be dealt with in the course of scouting underwater targets. Situation assessment in sea battlefield with a lot of uncertain information is studied, and a new situation assessment method of scouting underwater targets with fixed‐wing patrol aircraft is proposed based on the cloud Bayesian network, which overcomes the deficiency of the single cloud model in reasoning ability and the defect of Bayesian network in knowledge representation. Moreover, in the method, the cloud model knowledge deal with the input data of Bayesian network reasoning, and the advantages in knowledge representation of cloud theory and reasoning of Bayesian network are applied; also, the fuzziness and stochasticity of cloud theory in knowledge expression, the reasoning ability of Bayesian network, are combined. Then, the situation assessment model of scouting underwater targets with fixed‐wing patrol aircraft is established. Hence, the directed acyclic graph of Bayesian network structure is constructed and the assessment index is determined. Next, the cloud model is used to deal with Bayesian network, and the discrete Bayesian network is obtained. Moreover, after CPT of each node and the transformation between certainty degree and probability are accomplished; the final situation level is obtained through a probability synthesis formula. Therefore, the target type and the operational intention of the other side are deduced to form the battlefield situation. Finally, simulations are carried out, and the rationality and validity of the proposed method are testified by simulation results. By this method, the battlefield situation can be gained. And this method has a wider application range, especially for large sample data processing, and it has better practicability.
Speech to Sign Language Translation for Indian Languages

Hearing-impaired people and mute people face a lot of difficulty in communication while interacting with others in society. It may reduce their self-confidence and might make them feel isolated from others. Sign language acts as a communication medium between deaf people and ordinary people. Many technologies are used to convert the text to American Sign Language. There is a limited amount of research done on Indian Sign Language and is widely used by deaf people in India. This research aims at conversion of speech to Indian sign language for six Indian regional languages Telugu, Hindi, Malayalam, Marathi, Kannada and Tamil. The proposed model takes speech as the input and displays a sequence of corresponding gestures as the output. It involves speech recognition using Wavelet-based MFCC with GMM, text translation using LSTM and mapping the text with the sign language.
Impact of Thread Parallelism on the Soft Error Reliability of Convolution Neural Networks

Convolution neural networks (CNNs) have been incorporated into resource-constrained edge devices to intelligently manage and process local data coming from a variety of sensors. Thread parallelism has been used to boost the performance of neural networks, but only few works address the effect of these parallel modifications on the soft error reliability of underlying models running on edge devices. In this sense, this work aims to assess the soft error reliability of a multi-threaded version of a CNN model developed based on the Arm CMSIS-NN kernels. Results show that the developed threaded CNN model increases performance at the cost of low memory footprint overhead. Promoted multi-threaded CNN model also provides better soft error reliability w.r.t. the original sequential version.
Comparing hand gestures and a gamepad interface for locomotion in virtual environments

Hand gesture is a new and promising interface for locomotion in virtual environments. While several previous studies have proposed different hand gestures for virtual locomotion, little is known about their differences in terms of performance and user preference in virtual locomotion tasks. In the present paper, we presented three different hand gesture interfaces and their algorithms for locomotion, which are called the Finger Distance gesture, the Finger Number gesture and the Finger Tapping gesture. These gestures were inspired by previous studies of gesture-based locomotion interfaces and are typical gestures that people are familiar with in their daily lives. Implementing these hand gesture interfaces in the present study enabled us to systematically compare the differences between these gestures. In addition, to compare the usability of these gestures to locomotion interfaces using gamepads, we also designed and implemented a gamepad interface based on the Xbox One controller. We conducted empirical studies to compare these four interfaces through two virtual locomotion tasks. A desktop setup was used instead of sharing a head-mounted display among participants due to the concern of the Covid-19 situation. Through these tasks, we assessed the performance and user preference of these interfaces on speed control and waypoints navigation. Results showed that user preference and performance of the Finger Distance gesture were close to that of the gamepad interface. The Finger Number gesture also had close performance and user preference to that of the Finger Distance gesture. Our study demonstrates that the Finger Distance gesture and the Finger Number gesture are very promising interfaces for virtual locomotion. We also discuss that the Finger Tapping gesture needs further improvements before it can be used for virtual walking.
Cross-gain modulation-based photonic reservoir computing using low-power-consumption membrane SOA on Si

We demonstrate photonic reservoir computing (RC) utilizing cross-gain modulation (XGM) in a membrane semiconductor optical amplifier (SOA) on a Si platform. The membrane SOA's features of small active volume and strong optical confinement enable low-power nonlinear operation of the reservoir, with 101-mW-scale power consumption and 102-µW-scale optical input power. The power consumption is about an order of magnitude lower than that of conventional SOAs that exhibit saturable nonlinearity. The XGM-based reservoir is configured by injecting a delayed feedback signal into the SOA from a direction opposite to the input signal. This configuration provides robust operation of the feedback circuit because of the phase insensitivity and the elimination of loop oscillation risk. The RC performance is evaluated via the information processing capacity (IPC) and a nonlinear benchmark task. It is revealed that the XGM-based reservoir performs strong nonlinear transformation of input time-series signals. The series of results consistently show that the membrane SOA performs RC-applicable nonlinear operations through XGM at a low power scale.
Gated Tree-based Graph Attention Network (GTGAT) for medical knowledge graph reasoning

Knowledge graph (KG) is a multi-relational data that has proven valuable for many tasks including decision making and semantic search. In this paper, we present GTGAT (Gated Tree-based Graph Attention), a method for tackling the problems of transductive and inductive reasoning in generalized KGs. Based on recent advancement of graph attention network (GAT), we develop a gated tree-based method to distill valuable information in neighborhood via hierarchical-aware and semantic-aware attention mechanism. Our approach not only addresses several key challenges of GAT but is also capable of undertaking multiple downstream tasks. Experimental results have revealed that our proposed GTGAT has matched state-of-the-art approaches across transductive benchmarks on the Cora, Citeseer, and electronic medical record networks (EMRNet). Meanwhile, the inductive experiments on medical knowledge graphs show that GTGAT surpasses the best competing methods for personalized disease diagnosis.
A local reactive steering law for 2D collision avoidance with curvature constraints and constant speed

Motion planning algorithms for dynamic environments that explicitly take into account actuator constraints require a lot of computational effort due to replanning or optimizing trajectories. This makes them limited in use, especially for autonomous reactive behaviors that need to be computed on-board. Motivated by this need, we present a new real-time method for reactive collision avoidance for systems with bounded curvature in static and dynamic environments. Our approach relies on the implementation of a local steering law that satisfies a predefined bound on path curvature. The steering law depends on a user-defined parametric function that determines the transition between obstacle-free motion and collision avoidance by enforcing an obstacle impenetrability constraint. As such, we propose a systematic procedure which modulates the velocity vector to enforce curvature constraints in complex 2D environments characterized by static and moving obstacles. We provide theoretical guarantees for collision avoidance and we demonstrate this methodology through simulations.
AX-Unet: A Deep Learning Framework for Image Segmentation to Assist Pancreatic Tumor Diagnosis

Image segmentation plays an essential role in medical imaging analysis such as tumor boundary extraction. Recently, deep learning techniques have dramatically improved performance for image segmentation. However, an important factor preventing deep neural networks from going further is the information loss during the information propagation process. In this article, we present AX-Unet, a deep learning framework incorporating a modified atrous spatial pyramid pooling module to learn the location information and to extract multi-level contextual information to reduce information loss during downsampling. We also introduce a special group convolution operation on the feature map at each level to achieve information decoupling between channels. In addition, we propose an explicit boundary-aware loss function to tackle the blurry boundary problem. We evaluate our model on two public Pancreas-CT datasets, NIH Pancreas-CT dataset, and the pancreas part in medical segmentation decathlon (MSD) medical dataset. The experimental results validate that our model can outperform the state-of-the-art methods in pancreas CT image segmentation. By comparing the extracted feature output of our model, we find that the pancreatic region of normal people and patients with pancreatic tumors shows significant differences. This could provide a promising and reliable way to assist physicians for the screening of pancreatic tumors.
Theorizing Technological Mediation in the Outdoor Classroom

Abstract Situated within the context of the changing nature of teaching and learning in a postdigital context, this paper aims to theorize the mediating impacts of mobile technologies on outdoor learning experiences. Technological mediation is arguably a vital, yet often neglected, aspect of pedagogical practices. Today, the increasing employment of mobile technologies is not only changing the practices of outdoor education, but also challenging the traditional values of the field. This paper calls the predominant view that technology places a barrier between learners and the environment into question and offers a novel theoretical perspective. Inspired by postphenomenological mediation theory, the paper proposes a tri-polar technological mediation and outdoor learning framework. The framework offers a deeper understanding of the different dimensions of the mediating impacts of mobile devices on the relations between learners, their peers, and the natural environment in the outdoor classroom.
Explainability for Medical Image Captioning

Medical image captioning is the process of generating clinically significant descriptions to medical images, which has many applications among which medical report generation is the most frequent one. In general, automatic captioning of medical images is of great interest for medical experts since it offers assistance in diagnosis, disease treatment and automating the workflow of the health practitioners. Recently, many efforts have been put forward to obtain accurate descriptions but medical image captioning still provides weak and incorrect descriptions. To alleviate this issue, it is important to explain why the model produced a particular caption based on some specific features. This is performed through Artificial Intelligence Explainability (XAI), which aims to unfold the ‘black-box’ feature of deep-learning based models. We present in this paper an explainable module for medical image captioning that provides a sound interpretation of our attention-based encoder-decoder model by explaining the correspondence between visual features and semantic features. We exploit for that, self-attention to compute word importance of semantic features and visual attention to compute relevant regions of the image that correspond to each generated word of the caption in addition to visualization of visual features extracted at each layer of the Convolutional Neural Network (CNN) encoder. We finally evaluate our model using the ImageCLEF medical captioning dataset.
Rethinking Stateful Stream Processing with RDMA

Remote Direct Memory Access (RDMA) hardware has bridged the gap between network and main memory speed and thus invalidated the common assumption that network is often the bottleneck in distributed data processing systems. However, high-speed networks do not provide "plug-and-play" performance (e.g., using IP-over- InfiniBand) and require a careful co-design of system and application logic. As a result, system designers need to rethink the architecture of their data management systems to benefit from RDMA acceleration. In this paper, we focus on the acceleration of stream processing engines, which is challenged by real-time constraints and state consistency guarantees. To this end, we propose Slash, a novel stream processing engine that uses high-speed networks and RDMA to efficiently execute distributed streaming computations. Slash embraces a processing model suited for RDMA acceleration and scales out by omitting the expensive data re-partitioning demands of scale-out SPEs. While scale-out SPEs rely on data re-partitioning to execute a query over many nodes, Slash uses RDMA to share mutable state among nodes. Overall, Slash achieves a throughput improvement up to two orders of magnitude over existing systems deployed on an InfiniBand network. Furthermore, it is up to a factor of 22 faster than a self-developed solution that relies on RDMA-based data re-partitioning to scale out query processing.
Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process

Deep Learning (DL) has achieved great success in many real applications. Despite its success, there are some main problems when deploying advanced DL models in database systems, such as hyper-parameters tuning, the risk of overfitting, and lack of prediction uncertainty. In this paper, we study a lightweight and accurate cardinality estimation for SQL queries, which is also uncertainty-aware. By lightweight, we mean that we can train a DL model in a few seconds. With uncertainty ensured,it becomes possible to update the estimator to improve its prediction in areas with high uncertainty.The approach we explore is different from the direction of deploying sophisticated DL models as cardinality estimators in database systems. We employ Bayesian deep learning (BDL), which serves as a bridge between Bayesian inference and deep learning. The prediction distribution by BDL provides principled uncertainty calibration for the prediction. In addition, when the network width of a BDL model goes to infinity, the model performs equivalent to Gaussian Process (GP). This special class of BDL, known as Neural Network Gaussian Process (NNGP), inherits the advantages of Bayesian approach while keeping universal approximation of neural networks, and can utilize a much larger model space to model distribution-free data as a nonparametric model. We show our NNGP estimator achieves high accuracy, is built fast, and is robust to query workload shift, in our extensive performance studies by comparing with existing learned estimators. We also confirm the effectiveness of NNGP by integrating it into PostgreSQL.
Research on Sentiment Analysis Model of Short Text Based on Deep Learning

With the wide application of the Internet and the rapid development of network technology, microblogs and online shopping platforms are playing an increasingly important role in people’s daily life, learning, and communication. The length of these information texts is usually relatively short, and the grammatical structure is not standardized, but it contains rich emotional tendencies of users. The features used by custumal machinery schooling methods are too sparse on the vector space model and lack the semantic information of short texts, which cannot well identify the semantic features and potential emotional features of short texts. In response to the above problems, this paper proposes a bidirectional long-term and short-term memory network model based on emotional multichannel, combining the attention mechanism and convolutional neural network features in deep learning and learning the short text by combining shallow learning and deep learning. The semantic information and potential emotional information of the short text can be improved to promote the effective expression of short-text emotional features and improve the short-text emotional classification effect. Finally, this paper compares the above models on multidomain classification data sets such as NLPIR and NLPCC2014. The accuracy and F1 value of the model proposed in this paper have achieved good improvement in the field of short-text sentiment analysis.
Controllable energy angular spectrum method

A controllable energy method, which considers the undersampling issue of the transfer function and valid spectral energy of a source signal, is proposed to implement angular spectrum diffraction calculation in near and far fields. The proposed method provides an optimized frequency boundary $f_{CE}$ within which it always keeps controllable energy to be diffracted. The controllable energy angular spectrum method significantly reduces the number of samples while having the same accuracy as previous angular spectrum methods, implying a higher calculation efficiency. The new perspective of analyzing spectral energy is shown to improve the performance of relevant diffraction calculations.
Causal factorization machine for robust recommendation

Factorization Machines (FMs) are widely used for the collaborative recommendation because of their effectiveness and flexibility in feature interaction modeling. Previous FM-based works have claimed the importance of selecting useful features since incorporating unnecessary features will introduce noise and reduce the recommendation performance. However, previous feature selection algorithms for FMs are proposed based on the i.i.d. hypothesis and select features according to their importance to the predictive accuracy on training data. However, the i.i.d. assumption is often violated in real-world applications, and shifts between training and testing sets may exist. In this paper, we consider achieving causal feature selection in FMs so as to enhance the robustness of recommendation when the distributions of training data and testing data are different. What's more, different from other machine learning tasks like image classification, which usually select a global set of causal features for a predictive model, we emphasize the importance of considering personalized causal feature selection in recommendation scenarios since the causal features for different users may be different. To achieve our goal, we propose a personalized feature selection method for FMs and refer to the confounder balancing approach to balance the confounders for every treatment feature. We conduct experiments on three real-world datasets and compare our method with both representative shallow and deep FM-based baselines to show the effectiveness of our method in enhancing the robustness of recommendations and improving the recommendation accuracy.