Reviewerly/exHarmony huggingface dataset:
Class-wise Metric Scaling for Improved Few-Shot Classification

Few-shot classification aims to generalize basic knowledge to recognize novel categories from a few samples. Recent centroid-based methods achieve promising classification performance with the nearest neighbor rule. However, we consider that those methods intrinsically ignore per-class distribution, as the decision boundaries are biased due to the diversity of intra-class variances. Hence, we propose a class-wise metric scaling (CMS) mechanism, which can be applied to both training and testing stages. Concretely, metric scalars are set as learnable parameters in the training stage, helping to learn a more discriminative and transferable feature representation. As for testing, we construct a convex optimization problem to generate an optimal scalar vector for refining the nearest neighbor decisions. Besides, we also involve a low-ranking bilinear pooling layer for improved representation capacity, which further provides significant performance gains. Extensive experiments are conducted on a series of feature extractor backbones, datasets, and testing modes, which have shown consistent improvements compared to prior SOTA methods, e.g., we achieve accuracies of 66.64 % and 83.63 % for 5-way 1-shot and 5-shot settings on the mini-ImageNet, respectively. Under the semi-supervised inductive mode, results are further up to 78.34 % and 87.53 %, respectively.
Research Review on the Application of Homomorphic Encryption in Database Privacy Protection

With the advent and development of database applications such as big data and data mining, how to ensure the availability of data without revealing sensitive information has been a significant problem for database privacy protection. As a critical technology to solve this problem, homomorphic encryption has become a hot research area in information security at home and abroad in recent years. The paper sorted out, analyzed, and summarized the research progress of homomorphic encryption technology in database privacy protection. Moreover, the application of three different types of homomorphic encryption technology in database privacy protection was introduced respectively, and the rationale and characteristics of each technique were analyzed and explained. Ultimately, this research summarized the challenges and development trends of homomorphic encryption technology in the application of database privacy protection, which provides a reference for future research.
A study on Vietnamese undergraduates’ level of digital skills and the frequency of using digital tools in the EFL context

The current study aims to examine the digital skill level of 369 Vietnamese sophomores in the EFL context. An adapted instrument was translated and revised multiple times before administering the final version of the questionnaire to students. The findings of the research have revealed that students have sufficient digital facilities and they are familiar with using technologies for their language learning. Furthermore, students have achieved an average level of digital skill, and they are most proficient in using social networking services in comparison with other applications. Concerning the frequency of using digital tools, students reported that they do not often utilize digital applications in their language learning. Among applications, students more often use social media than other tools. Besides, the study also explored that there are correlations among digital skill, the frequency of using technology and the ICT facilities and ICT familiarity. It is suggested that there is a necessity in curriculum or teaching methodology changes to create opportunities for students to enhance their digital skills in their language learning in particular and in education in general as they are the digital ‘natives’ of the community.
Pretrained Neural Models for Turkish Text Classification

In the text classification process, which is a sub-task of NLP, the preprocessing and indexing of the text has a direct determining effect on the performance for NLP models. When the studies on pre-trained models are examined, it is seen that the changes made on the models developed for world languages or training the same model with a Turkish text dataset. Word-embedding is considered to be the most critical point of the text processing problem. The two most popular word embedding methods today are Word2Vec and Glove, which embed words into a corpus using multidimensional vectors. BERT, Electra and Fastext models, which have a contextual word representation method and a deep neural network architecture, have been frequently used in the creation of pre-trained models recently. In this study, the use and performance results of pre-trained models on TTC-3600 and TRT-Haber text sets prepared for Turkish text classification NLP task are shown. By using pre-trained models obtained with large corpus, a certain time and hardware cost, the text classification process is performed with less effort and high performance.
ModularHMD: A Reconfigurable Mobile Head-Mounted Display Enabling Ad-hoc Peripheral Interactions with the Real World

We propose ModularHMD, a new mobile head-mounted display concept, which adopts a modular mechanism and allows a user to perform ad-hoc peripheral interaction with real-world devices or people during VR experiences. ModularHMD is comprised of a central HMD and three removable module devices installed in the periphery of the HMD cowl. Each module has four main states: occluding, extended VR view, video see-through (VST), and removed/reused. Among different combinations of module states, a user can quickly setup the necessary HMD forms, functions, and real-world visions for ad-hoc peripheral interactions without removing the headset. For instance, an HMD user can see her surroundings by switching a module into the VST mode. She can also physically remove a module to obtain direct peripheral visions of the real world. The removed module can be reused as an instant interaction device (e.g., touch keyboards) for subsequent peripheral interactions. Users can end the peripheral interaction and revert to a full VR experience by re-mounting the module. We design ModularHMD’s configuration and peripheral interactions with real-world objects and people. We also implement a proof-of-concept prototype of ModularHMD to validate its interactions capabilities through a user study. Results show that ModularHMD is an effective solution that enables both immersive VR and ad-hoc peripheral interactions.
Deformation Mechanisms in Orogenic Gold Systems During Aseismic Periods: Microstructural Evidence from the Central Victorian Gold Deposits, Southeast Australia

Abstract In many orogenic gold deposits, gold is located in quartz veins. Understanding vein development at the microstructural scale may therefore provide insights into processes influencing the distribution of gold, its morphology, and its relationship to faulting. We present evidence that deformation processes during aseismic periods produce characteristic quartz microstructures and crystallographic preferred orientations, which are observed across multiple deposits and orogenic events. Quartz veins comprise a matrix of coarse, subidiomorphic, and columnar grains overprinted by finer-grained quartz seams subparallel to the fault trace, which suggests an initial stage of cataclastic deformation. The fine-grained quartz domains are characterized by well-oriented quartz c-axis clusters and girdles oriented parallel to the maximum extension direction, which reveals that fluid-enhanced pressure solution occurred subsequent to grain refinement. Coarser anhedral gold is associated with primary quartz, whereas fine-grained, “dusty” gold trails are found within the fine-grained quartz seams, revealing a link between aseismic deformation and gold morphology. These distinct quartz and gold morphologies, observed at both micro- and macroscale, suggest that both seismic fault-valving and aseismic deformation processes are both important controls on gold distribution.
The Pandemic Job Market from the Perspective of Female Languages and Translation College Graduates in Saudi Arabia: Crisis or Opportunity

The COVID-19 outbreak early 2020 has had a considerable impact on the global labor market. Many businesses closed down, constrained their working hours, reduced their staff, and/or limited their new recruitments. Many employees have been obliged to work remotely and interact digitally. Due to the Pandemic, the unemployment rates have gone up to unprecedented rates in all countries. This study investigates the status of unemployment in Saudi Arabia in May 2021 among languages and translation graduates and whether Saudi universities have adapted their curricula to equip languages and translation graduating seniors with the necessary skills needed for the Pandemic and Post-pandemic job market. Most participating graduates, instructors and administrators in the sample reported lack of jobs for language and translation graduates and that universities have not adapted their curricula nor offered any training courses or seminars in job search techniques and digital and interpersonal skills necessary for the new Pandemic job environment. The study recommends that Saudi universities offer online workshops and seminars to train graduating seniors in preparing a CV, writing a letter of purpose and cover letter, filling a job application form, answering online vocational interest surveys, where to look for a job online, joining online business communities, getting ready for job interviews, raising students’ awareness of recruitment and job advertisement websites/pages, and training students in effective communication, interpersonal, and digital skills needed for a remote job environment. Students are also advised to do volunteer work and take training courses while waiting to be recruited.
Efficient Leakage-Resilient MACs Without Idealized Assumptions

The security proofs of leakage-resilient MACs based on symmetric building blocks currently rely on idealized assumptions that hardly translate into interpretable guidelines for the cryptographic engineers implementing these schemes. In this paper, we first present a leakage-resilient MAC that is both efficient and secure under standard and easily interpretable black box and physical assumptions. It only requires a collision resistant hash function and a single call per message authentication to a Tweakable Block Cipher (\(\mathsf {TBC}\)) that is unpredictable with leakage. This construction leverages two design twists: large tweaks for the \(\mathsf {TBC}\) and a verification process that checks the inverse \(\mathsf {TBC}\) against a constant. It enjoys beyond birthday security bounds. We then discuss the cost of getting rid of these design twists. We show that security can be proven without them as well. Yet, a construction without large tweaks requires stronger (non idealized) assumptions and may incur performance overheads if specialized \(\mathsf {TBC}\)s with large tweaks can be exploited, and a construction without twisted verification requires even stronger assumptions (still non idealized) and leads to more involved bounds. The combination of these results makes a case for our first pragmatic construction and suggests the design of \(\mathsf {TBC}\)s with large tweaks and good properties for side-channel countermeasures as an interesting challenge.
Robust &amp; Asymptotically Locally Optimal UAV-Trajectory Generation Based on Spline Subdivision

Generating locally optimal UAV-trajectories is challenging due to the non-convex constraints of collision avoidance and actuation limits. We present the first local, optimization-based UAV-trajectory generator that simultane-ously guarantees validity and asymptotic optimality for known environments. Validity: Given a feasible initial guess, our algo-rithm guarantees the satisfaction of all constraints throughout the process of optimization. Asymptotic Optimality: We use an asymptotic exact piecewise approximation of the trajectory with an automatically adjustable resolution of its discretization. The trajectory converges under refinement to the first-order stationary point of the exact non-convex programming problem. Our method has additional practical advantages including joint optimality in terms of trajectory and time-allocation, and robustness to challenging environments as demonstrated in our experiments.
Time-Optimal Multi-Quadrotor Trajectory Planning for Pesticide Spraying

In this paper, we investigate the problem of generating time-optimal trajectories automatically for spraying pesticides to infected regions with varying degree of infections in an agricultural field using a collection of quad-rotors that have limited pesticide carrying capacity but are capable of refilling from the pesticide tanks stationed across the field. We consider the time of traversal between points, time for changing the direction of traversal, and time for spraying and refilling, explicitly in the problem formulation, and generate trajectories for the multiple quad-rotors to execute in parallel such that the total energy (time) for completion of the task is minimized. We present two methods namely, multiple traveling salesman based time-optimal trajectory generation, and clustering based de-compositional time-optimal trajectory generation, respectively. In the first method, our approach consists of reducing the time-optimal trajectory generation problem for the multiple quad-rotors to a version of the multiple traveling salesman problem on a weighted graph, and then encoding the problem into a mixed integer linear programming problem. In the second method, our approach consists of first decomposing the infected regions into k clusters corresponding to the k robots, placing each quad-rotor at the center of the corresponding clusters, and finally, independently solving time-optimal trajectory generation problem for each quad-rotor using the first method. We have implemented both the approaches and performed experimental comparison. We observe that the first method provides an optimal strategy, while the second method provides a sub-optimal strategy. However, the second method is computationally more efficient, and the loss of optimality is minimal, hence, it provides a better trade-off between the computational complexity and the optimality constraints.
Pose-guided Generative Adversarial Net for Novel View Action Synthesis

We focus on the problem of novel-view human action synthesis. Given an action video, the goal is to generate the same action from an unseen viewpoint. Naturally, novel view video synthesis is more challenging than image synthesis. It requires the synthesis of a sequence of realistic frames with temporal coherency. Besides, transferring different actions to a novel target view requires awareness of action category and viewpoint change simultaneously. To address these challenges we propose a novel framework named Pose-guided Action Separable Generative Adversarial Net (PAS-GAN), which utilizes pose to alleviate the difficulty of this task. First, we propose a recurrent pose-transformation module which transforms actions from the source view to the target view and generates novel view pose sequence in 2D coordinate space. Second, a well-transformed pose sequence enables us to separate the action and background in the target view. We employ a novel local-global spatial transformation module to effectively generate sequential video features in the target view using these action and background features. Finally, the generated video features are used to synthesize human action with the help of a 3D decoder. Moreover, to focus on dynamic action in the video, we propose a novel multi-scale action-separable loss which further improves the video quality. We conduct extensive experiments on two large-scale multi-view human action datasets, NTU-RGBD and PKU-MMD, demonstrating the effectiveness of PAS-GAN which outperforms existing approaches. The codes and models will be available on https://github.com/xhl-video/PAS-GAN.
JAM: Joint attention model for next event recommendation in event-based social networks

In recent years, event-based social networks (EBSNs) have emerged as popular applications for people selecting social events such as concerts, hikes, and technical talks, and how to precisely promote events to specific users has become an essential topic in both academia and industry. However, the next event recommendation in EBSNs is facing new challenges compared to general recommender systems — events are usually new and have a short life cycle, and user preferences may change over time. This study proposes a next-event recommendation method called the joint attention model (JAM), where rich contextual information is integrated for event representation, and attention mechanism is employed to handle the dynamic preferences of users. In particular, the participants are also considered as context of the events. To capture the dynamic changing preferences of users, this study develops a signed multihead attention mechanism, which assigns positive and negative weights to historical events visited by users in the past and uses higher-order attention to simulate the weights of positive and negative effects. Empirical experiments are conducted with different datasets from Meetup, and the results show that the proposed model achieves better performance than the state-of-the-art methods.
A study of the use of virtual reality headsets in Chinese adolescents with intellectual disability

Background: Virtual reality technologies (VRTs) present many characteristics that can facilitate learning, especially in individuals with intellectual disabilities (ID). The VRT head-mounted display (HMD) has recently shown significant technological improvement. This study aims to prove the suitability of the newer commercially available VRT HMDs used in interventions for adolescents with ID in China. To this end, we explored the acceptance, immersion, and negative effects for adolescents with ID in a series of virtual environments. Methods: Forty-nine adolescents with ID (33 boys and 16 girls, IQ < 70), aged 11 to 21, from three special schools participated in this study. Questionnaires, observations, and interviews were used to investigate their views while using a VR HMD. Results: Our analysis found that most participants narrated a pleasant experience using HMD and a series of VR experiences and experienced a high level of immersion accompanied by low negative effects. In addition, our study found that the gender variable was independent of the acceptance of HMDs and the immersion experience. Conclusions: Overall, this study suggests that the HMD is accepted by most Chinese adolescents with ID; thus, its application has great potential in China.
<i>i</i> -QER: An Intelligent Approach Towards Quantum Error Reduction

Quantum computing has become a promising computing approach because of its capability to solve certain problems, exponentially faster than classical computers. A n -qubit quantum system is capable of providing 2 n computational space to a quantum algorithm. However, quantum computers are prone to errors. Quantum circuits that can reliably run on today’s Noisy Intermediate-Scale Quantum (NISQ) devices are not only limited by their qubit counts but also by their noisy gate operations. In this article, we have introduced i -QER, a scalable machine learning-based approach to evaluate errors in a quantum circuit and reduce these without using any additional quantum resources. The i -QER predicts possible errors in a given quantum circuit using supervised learning models. If the predicted error is above a pre-specified threshold, it cuts the large quantum circuit into two smaller sub-circuits using an error-influenced fragmentation strategy for the first time to the best of our knowledge. The proposed fragmentation process is iterated until the predicted error reaches below the threshold for each sub-circuit. The sub-circuits are then executed on a quantum device. Classical reconstruction of the outputs obtained from the sub-circuits can generate the output of the complete circuit. Thus, i -QER also provides classical control over a scalable hybrid computing approach, which is a combination of quantum and classical computers. The i -QER tool is available at https://github.com/SaikatBasu90/i-QER .
Siamese tracking combing frequency channel attention with adaptive template

Siamese network based the tracker is a hot topic in the field of visual object tracking. However, Siamese trackers still have a robustness gap compared with state-of-the-art algorithms. Therefore, focusing on the issue, this letter adds Frequency Channel Attention (FCA) and adaptive template feature map to the framework of Siamese neural network. FCA can enhance feature representation of effective channels and improve feature discrimination by modeling the correlation between each channel of the image. In this algorithm, by theoretical analysis and experimental validation, restriction is broken through a simple yet effective FCA network sampling strategy and a Siamese-FCA tracker with significant performance gain is successfully trained. Meanwhile, in order to better adjust the proportion between target and background, the tracker selects suitable size of the target feature map. Moreover, extensive ablation studies are conducted to demonstrate the effectiveness of the proposed tracker. Fairly, the experimental results of five test benchmarks, including OTB2013, OTB2015, VOT2016, VOT2018 and UAV123 datasets, shows that the proposed algorithm performs outstanding. In particular, the issue of similarity and small target tracking failure is overcome. The average running frame rate reaches 86 frames per second, which can meet the real-time requirements.
Fast Path Computation using Lattices in the Sensor-Space for Forest Navigation

Fast autonomous motion in cluttered and unknown environments, such as forests, is highly dependent on low-latency obstacle avoidance strategies. In this context, this paper presents a motion planning strategy that relies on lattices for the fast computation of local paths that both avoid obstacles and follow a vector field that encodes the global robot task. Lattices are constructed in the sensor space and represent a set of search trees that can be quickly pruned in function of the detected obstacles. The remaining lattice trees are used to optimize a vector field-dependent functional, thus generating the best free local path that tracks the field. To illustrate the proposed approach, we present simulation and real-world experiments of a planar robot moving in a cluttered, forest-like environment.
Lexical, Pragmatic and Linguistic Feature Based Two-Level Sarcasm Detection Using Machine Learning Techniques

Sarcasm refers to the use of words that mean different from what a person really wants to say, especially in order to show irritation, or to insult someone or just to be funny. The detection of sarcasm is of great importance to many natural language processing applications such as sentiment analysis, social media handling, advertising and opinion mining. To understand sarcasm, people usually rely on the speaker's facial expression, tone of voice, writing style, and how the speaker might feel about it. Despite various facial cues, sarcasm detection could be a difficult process and some people struggle to understand sarcasm. The detection of sarcasm becomes twofold difficult in textual context as compared with verbal communication. Along with the presence of sarcasm in text, there are many situations where a person needs to know what kind of sarcasm has been used. Therefore, this work aims to automate the process of the multi-classification of the sarcastic tweets into four different categories—positive, negative, self-deprecating and miscellaneous using three types of features, i.e., lexical, pragmatic and linguistic. For multiclass classification, four classifiers have been used—Logistic Regression, Naive Bayes, Random Forest and Support Vector Machine. The experimental results illustrate that, up to 89 and 39%, accuracy has been achieved with respect to binary and multiclass sarcasm detection, respectively.