Reviewerly/exHarmony huggingface dataset:
Robust Image Matching By Dynamic Feature Selection

Estimating dense correspondences between images is a long-standing image under-standing task. Recent works introduce convolutional neural networks (CNNs) to extract high-level feature maps and find correspondences through feature matching. However,high-level feature maps are in low spatial resolution and therefore insufficient to provide accurate and fine-grained features to distinguish intra-class variations for correspondence matching. To address this problem, we generate robust features by dynamically selecting features at different scales. To resolve two critical issues in feature selection,i.e.,how many and which scales of features to be selected, we frame the feature selection process as a sequential Markov decision-making process (MDP) and introduce an optimal selection strategy using reinforcement learning (RL). We define an RL environment for image matching in which each individual action either requires new features or terminates the selection episode by referring a matching score. Deep neural networks are incorporated into our method and trained for decision making. Experimental results show that our method achieves comparable/superior performance with state-of-the-art methods on three benchmarks, demonstrating the effectiveness of our feature selection strategy.
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs

Although 3D Convolutional Neural Networks (CNNs) are essential for most learning based applications involving dense 3D data, their applicability is limited due to excessive memory and computational requirements. Compressing such networks by pruning therefore becomes highly desirable. However, pruning 3D CNNs is largely unexplored possibly because of the complex nature of typical pruning algorithms that embeds pruning into an iterative optimization paradigm. In this work, we introduce a Resource Aware Neuron Pruning (RANP) algorithm that prunes 3D CNNs at initialization to high sparsity levels. Specifically, the core idea is to obtain an importance score for each neuron based on their sensitivity to the loss function. This neuron importance is then reweighted according to the neuron resource consumption related to FLOPs or memory. We demonstrate the effectiveness of our pruning method on 3D semantic segmentation with widely used 3D-UNets on ShapeNet and BraTS'18 as well as on video classification with MobileNetV2 and I3D on UCF101 dataset. In these experiments, our RANP leads to roughly 50%-95% reduction in FLOPs and 35%-80% reduction in memory with negligible loss in accuracy compared to the unpruned networks. This significantly reduces the computational resources required to train 3D CNNs. The pruned network obtained by our algorithm can also be easily scaled up and transferred to another dataset for training.
On the differential privacy of dynamic location obfuscation with personalized error bounds

Geo-indistinguishability and expected inference error are two complementary notions for location privacy. The joint guarantee of differential privacy (indistinguishability) and distortion privacy (inference error) limits the information leakage. In this paper, we analyze the differential privacy of PIVE, a dynamic location obfuscation mechanism proposed by Yu, Liu and Pu (NDSS 2017), and show that PIVE fails to offer either of the privacy guarantees on adaptive Protection Location Sets (PLSs) as claimed. Specifically, we demonstrate that different PLSs could intersect with one another due to the defined search algorithm, and then different apriori locations in the same PLS could have different protection diameters. As a result, we can show that the proof of local differential privacy for PIVE is problematic. Besides, the condition introduced in PIVE is confirmed to be not sufficient for bounding expected inference errors in general, which makes the user-defined inference error threshold invalid. To address these issues, we propose a couple of correction approaches, analyze theoretically their satisfied privacy characteristics and detail their respective merits and demerits.
The Effect of the Major Satisfaction, Self-Efficacy and Career Preparation Behavior of University Students Majoring in Tourism

This study analyzed to identify the influence relationship between major satisfaction, self-efficacy, and career preparation behavior of university students majoring in tourism, and whether self-efficacy has a mediating effect. The subjects of this study were 235 university students majoring in tourism in the O city, and data were collected online from November 18 to November 27, 2020. As a result of the analysis, the four factors of major satisfaction (class satisfaction, relationship satisfaction, perceived satisfaction, subject satisfaction) had a significant effect on both self-efficacy and career preparation behavior was the highest. In the relationship between major satisfaction and career preparation behavior, it was confirmed that self-efficacy plays a partial mediating role in career preparation behavior. As the self-efficacy increases, major satisfaction plays a stronger role in career preparation behavior. Therefore, strategies to strengthen active career preparation behavior along with opening and operating programs to strengthen major-related competencies, and effective class management will be needed.
Task Calibration for Distributional Uncertainty in Few-Shot Classification

As numerous meta-learning algorithms improve performance when solving few-shot classification problems for practical applications, accurate prediction of uncertainty, though challenging, has been considered essential. In this study, we contemplate modeling uncertainty in a few-shot classification framework and propose a straightforward method that appropriately predicts task uncertainty. We suppose that the random sampling of tasks can generate those in which it may be hard for the model to infer the queries from the support examples. Specifically, measuring the distributional mismatch between support and query sets via class-wise similarities, we propose novel meta-training that lets the model predict with careful confidence. Moreover, our method is algorithm-agnostic and readily expanded to include a range of meta-learning models. Through extensive experiments including dataset shift, we present that our training strategy helps the model avoid being indiscriminately confident, and thereby, produce calibrated classification results without the loss of accuracy.
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection

We investigate the adversarial robustness of CNNs from the perspective of channel-wise activations. By comparing \textit{non-robust} (normally trained) and \textit{robustified} (adversarially trained) models, we observe that adversarial training (AT) robustifies CNNs by aligning the channel-wise activations of adversarial data with those of their natural counterparts. However, the channels that are \textit{negatively-relevant} (NR) to predictions are still over-activated when processing adversarial data. Besides, we also observe that AT does not result in similar robustness for all classes. For the robust classes, channels with larger activation magnitudes are usually more \textit{positively-relevant} (PR) to predictions, but this alignment does not hold for the non-robust classes. Given these observations, we hypothesize that suppressing NR channels and aligning PR ones with their relevances further enhances the robustness of CNNs under AT. To examine this hypothesis, we introduce a novel mechanism, i.e., \underline{C}hannel-wise \underline{I}mportance-based \underline{F}eature \underline{S}election (CIFS). The CIFS manipulates channels' activations of certain layers by generating non-negative multipliers to these channels based on their relevances to predictions. Extensive experiments on benchmark datasets including CIFAR10 and SVHN clearly verify the hypothesis and CIFS's effectiveness of robustifying CNNs. \url{https://github.com/HanshuYAN/CIFS}
20 Years of VR Surgical Simulator and its Positioning Today

It has been about 20 years since the commercial use virtual reality (VR) surgical simulator was launched.
Rancang Bangun Peak Flow Meter dengan Output Suara Berbasis Android

Peak Flow Meter (PFM) is a tool to measure the amount of air flow in the airway (PFR) and to detect asthma. The output value of PFR can be influenced by several factors, such as age, respiratory muscle strength, height and gender. In this research, airway measurements are used to measure the condition of patients suffering from asthma. The author aims to make this tool so that it can find out how to design and make a peak flow meter output sound tool, measure the peak current and can know how the MPXV7002DP sensor works in regulating output in the form of sound. The method used by the author is to design or make a tool peak flow meter output sound. This MPXV7002DP sensor works when the sensor receives air blows from the flow sensor which automatically reads the highest air pressure from the breath. The test results using the VT Mobile Medical Gas Flow Analyzer prove that the largest percentage error is 2.4%, with the blowing rate on the Peak Flow Meter is 64.0 lpm and the blowing rate on VT mobile is 62.50 lpm. Therefore, this tool can be said to be very certain to detect asthma. Then it can be concluded that the peak flow meter is feasible and meets the specified requirements.Peak Flow Meter (PFM) is a tool to measure the amount of air flow in the airway (PFR) and to detect asthma. The value of PFR can be influenced by several factors such as age, respiratory muscle strength, height and gender. Airway measurements are used to measure the condition of patients suffering from asthma. The author aims to make this tool so that it can find out how to design and make a peak flow meter output sound tool, measure the peak current and can know how the MPXV7002DP sensor works in regulating output in the form of sound. The method used by the author is to design or make a tool peak flow meter output sound. This MPXV7002DP sensor works when the sensor receives air blows from the flow sensor which automatically reads the highest air pressure from the breath. The test results using the VT Mobile Medical Gas Flow Analyzer prove that the largest percentage error is 2.4%, with the blowing rate on the Peak Flow Meter is 64.0 lpm and the blowing rate on VT mobile is 62.50 lpm, so this tool can be said to be very certain to detect asthma. Then it can be concluded that the peak flow meter is feasible and meets the specified requirements.
THE EFFECT OF GALLERY WALK STRATEGY ON STUDENTS’ SPEAKING SKILL

This research aimed to find out the effect of gallery walk strategy on students’ speaking skill at eleventh-grade students MA Darul Masholeh. The study took 24 students as samples from two classes, that was XI IPA as an experimental class, and XI IPS as control class. It used quantitative analysis which is designed as quasi-experimental. The technique of collecting data is testing that is pre-test and post-test. The result showed that: 1). Students’ speaking skill without applying gallery walk strategy still under the standardized score of the school, that is 64.00. 2). Student's speaking skill by applied gallery walk strategy is improved, and most of them succeed to pass the standardized score of the school, that is 84.00. 3). The effect of gallery walk strategy on students’ speaking skill is a significant influence. The gain between the pre-test and post-test score is 15.64 points. Because the t-test result is t account (9.541)&gt; t table at (df) 22 (2.07387)showed that the significance level (Sig. (2-tailed)) was 0.00 which less than the significance level of 0.05. ), so, Ho is rejected, and Ha is accepted. It means that there is a significant effect of using gallery walk strategy on students’ speaking skill at MA Darul Masholeh in advertisement text at eleventh-grade students of MA Darul Masholeh.&#x0D;
New Engineering Construction of Internet of Things With Prominent Safety Features

New industry 4.0 needs the cooperation of new engineering education to cultivate new talents with multiple core abilities.The Internet of things project is an important specialty of new engineering and the next 10 billion industry.However, its personnel training does not meet the requirements of the new engineering, and the homogenization is serious.In view of these problems, combining with the advantages and characteristics of the college, this major explores the Internet of things engineering curriculum system, talent training mode, school enterprise cooperation, industry university research cooperation mode, which meets the requirements of new engineering.Preliminary results have been achieved, the employment rate has increased steadily, and the students and employers are satisfied with the professional training.
Bayesian Reasoning and Artificial Intelligence

The present article studies the importance of Bayesian Reasoning in everyday lifesituations and for the whole science. Examples are also given to illustrate our results.
Blockchain Technologies in Electric Energy Systems

Demand response (DR) programs development have been currently capturing significant interest by the utilities with the intention to match generation and demand in a more efficient way. The participation of DR programs in electricity markets has increased and has benefitted not only individual consumers but also the market as a whole. In addition, Blockchain and artificial intelligence are emerging technologies that have attracted significant attention from the industries and the academic community. Due to the impact of both technologies on energy systems, applications of blockchain technology in the energy field is explored, in this paper, because of its characteristics and the development needs of the energy Internet realm. An overview of demand response and blockchain technology that covers their types, enabling technologies, worldwide experiences, benefits and challenges are presented. Furthermore, techniques used in blockchain realization are also discussed. Finally, the paper winds up presenting a conceptual framework of blockchained-based proxy demand resources.
Video Encoding for Wireless Multimedia Sensor Networks: A Review

Wireless multimedia sensor networks (WMSNs) make possible diverse and demanding monitoring and surveillance applications. The WMSNs have to operate with significant energy, memory and processing power constraints. These constraints make video encoding vital but challenging to accomplish. Many approaches have been attempted but each one comes with its shortcomings. In this paper, a framework was developed to compare and contrast different methods for video encoding in WMSNs. This allows both researchers and practitioners to evaluate the different trade-offs that current techniques entail and what research gaps are not being addressed.
Hindsight Planner

Goal-oriented reinforcement learning capacitates agents to accomplish variant goals, which is crucial for robotic tasks. However, the sparse-reward setting of these tasks aggravates sample inefficiency. Hindsight Experience Replay (HER) was introduced as a technique to elevate sample efficiency by imaging hindsight virtual goals for unsuccessful trajectories, which mitigates long-term domination of negative rewards. Nevertheless, there is still a gap between the distribution of hindsight goals and desired goals of the tasks, which was narrowed by lots of aimless exploration in HER. In this paper, we propose Hindsight Planner(HP) to generate several subgoals guiding the agent to explore towards the desired goal step by step, which allows the agent to exploit its local knowledge learned from achieved goals. The planner uses history trajectories to learn the structure of feasible goal space, then generalizes its knowledge to unseen goals. We have extensively evaluated our framework on a number of robotic tasks and show substantial improvements over the original HER in terms of sample efficiency and converged performance.
Uniform Object Rearrangement: From Complete Monotone Primitives to Efficient Non-Monotone Informed Search

Object rearrangement is a widely-applicable and challenging task for robots. Geometric constraints must be carefully examined to avoid collisions and combinatorial issues arise as the number of objects increases. This work studies the algorithmic structure of rearranging uniform objects, where robot-object collisions do not occur but object-object collisions have to be avoided. The objective is minimizing the number of object transfers under the assumption that the robot can manipulate one object at a time. An efficiently computable decomposition of the configuration space is used to create a "region graph", which classifies all continuous paths of equivalent collision possibilities. Based on this compact but rich representation, a complete dynamic programming primitive DFSDP performs a recursive depth first search to solve monotone problems quickly, i.e., those instances that do not require objects to be moved first to an intermediate buffer. DFSDP is extended to solve single-buffer, non-monotone instances, given a choice of an object and a buffer. This work utilizes these primitives as local planners in an informed search framework for more general, non-monotone instances. The search utilizes partial solutions from the primitives to identify the most promising choice of objects and buffers. Experiments demonstrate that the proposed solution returns near-optimal paths with higher success rate, even for challenging non-monotone instances, than other leading alternatives.
Grundzüge der sizilianischen Prosodie

Abstract This article is concerned with basic intonational features of Sicilian, a Romance variety spoken in Sicily in Southern Italy. While there has been some research on the intonational features of the regional variety of Italian, Sicilian intonation remains undescribed. The first part of the article provides a historic overview of the dialectal configuration of Sicilian and the Sicilian-Italian diglossia. In the subsequent section we perform an intonational analysis on a Sicilian corpus containing acoustic data of 432 utterances (216 broad focus statements and 216 information-seeking yes-no questions). Once the basic intonational features of Sicilian are described and analyzed we use the informatic tool ProDis (Elvira-García et al. 2018) for a quantitative cluster analysis in order to define geoprosodic groups within Siciliy. Finally, we carry out another cluster analysis with the aim of modelling prosodic distances between Romance varieties spoken in different areas in Italy.
An Active Shape Model Parametric Approach for Facial Landmark Detection

Facial landmark detection is crucial on subsequent tasks related to the face recognition, three-dimensional face reconstruction, and so on. In this work, we present an active shape model parametric approach for facial landmark detection. Concretely, we propose to encode the landmark locations by the active shape model (ASM) parameters. Then, we leverage the power of cascade regression to estimate the ASM parameters. Final landmark locations can be decoded by the ASM parameters. Through this parametric way of predicting the landmark locations in another domain, it is more compact and effective compared with existing similar work. Experiments are conducted to validate the effectiveness of our method.
El asombro: una emoción para el acceso a la sabiduría

Awe is a classic concept with an important place in the history of philosophy, since it is regarded as the beginning of the development of thought, as we know it today. However, until now it has historically received little attention in the field of education, although in recent years it has been the subject of renewed attention for a variety of reasons. These include a growing discomfort with curriculum designs that reflect a frenetic culture with a clear tendency towards activism and instrumentalism. This work explores the concept of awe, considering some current approaches from the philosophy of education, at the same as providing new perspectives, considering the links between awe and wisdom as well as conditions that make awe possible, such as humility, gratitude, contemplation of the environment, and appreciation of the intrinsic value of what is observed. Moreover, it suggests three areas for fostering awe in schools. These include: creating opportunities for contact with truth, beauty, and goodness; promoting a greater immersion in natural environments; and reducing the pace of educational activity.
Verification Of Independent Study Assessment Using Blockchain Technology

Current technological developments greatly impact the assessment verification system. To find out the student benchmarks in the results of teaching and learning activities during the learning system assessment process is very important in the scope of higher education. With the existence of blockchain technology widely applied in the world of Education, having the advantage of a decentralized system and strong cryptography can help universities in building infrastructure. Universitas Raharja is one of the educational institutions that has implemented an online assessment system (PEN +), which will use blockchain technology to verify the assessment of independent studies. which provides services to Raharja University lecturers in verifying student grades that can be accessed anywhere and at any time. However, currently the verification process for the independent study assessment that has been carried out is still done manually which results in verification not with very strong security. The existence of an independent study assessment verification uses blockchain technology to produce strong data security that did not occur before. In the PEN + lecturer assessment system for the independent study assessment verification process, it cannot yet enter the value of Independent study (IS) in real time. Therefore, there is a need for development in this blockchain technology for the verification process of independent study assessment. In this study there were 10 (ten) literary studies on verification of valuation. Thus there are several benefits that lecturers need not hesitate to verify the assessment, the process by using blockchain technology produces very strong security.
Conditional Generative Modeling via Learning the Latent Space

Although deep learning has achieved appealing results on several machine learning tasks, most of the models are deterministic at inference, limiting their application to single-modal settings. We propose a novel general-purpose framework for conditional generation in multimodal spaces, that uses latent variables to model generalizable learning patterns while minimizing a family of regression cost functions. At inference, the latent variables are optimized to find optimal solutions corresponding to multiple output modes. Compared to existing generative solutions, our approach demonstrates faster and stable convergence, and can learn better representations for downstream tasks. Importantly, it provides a simple generic model that can beat highly engineered pipelines tailored using domain expertise on a variety of tasks, while generating diverse outputs. Our codes will be released.
SOCIOLOGICAL ANALYSIS OF THE USE OF AUGMENTED REALITY TECHNOLOGY IN EDUCATION

Augmented reality is increasingly used in education and its further spread is predicted as one of the technological trends. The benefits of using augmented reality are contributing to the increasing use of augmented reality in education. The purpose of the research is to study the use of augmented reality technology in teaching. The article provides the overview of the development of augmented reality and presents the results of a survey of teachers who attended the advanced training course. According to the results of the survey, the readiness of teachers to use this technology and the feasibility of its use in various methods and forms of training organization were determined. A special feature of the use of augmented reality in education is its accessibility and ease of use of ready-made mobile applications and services.