Reviewerly/exHarmony huggingface dataset:
Stacked Temporal Attention: Improving First-person Action Recognition by  Emphasizing Discriminative Clips

First-person action recognition is a challenging task in video understanding. Because of strong ego-motion and a limited field of view, many backgrounds or noisy frames in a first-person video can distract an action recognition model during its learning process. To encode more discriminative features, the model needs to have the ability to focus on the most relevant part of the video for action recognition. Previous works explored to address this problem by applying temporal attention but failed to consider the global context of the full video, which is critical for determining the relatively significant parts. In this work, we propose a simple yet effective Stacked Temporal Attention Module (STAM) to compute temporal attention based on the global knowledge across clips for emphasizing the most discriminative features. We achieve this by stacking multiple self-attention layers. Instead of naive stacking, which is experimentally proven to be ineffective, we carefully design the input to each self-attention layer so that both the local and global context of the video is considered during generating the temporal attention weights. Experiments demonstrate that our proposed STAM can be built on top of most existing backbones and boost the performance in various datasets.
Modern problems of ensuring information security in the educational and pedagogical sphere

The article deals with the problems of information security and its provision in relation to society and its educational environment, processes and relations, subjects and objects. The necessity of forming the corresponding subject-information competence of subjects, personal culture of information security is shown. It is noted that the information security of educational subjects is a specific part of the general security of education as its qualitative development in the developing information society in accordance with its properties and requirements. The concept of information security in the field of education is formulated in the context of a general concept and in the aspect of its specificity in education, its educational and educational processes, as well as in the aspect of personal, psychological, age and other properties of subjects of education. This presupposes the presence of a corresponding purposefulness of general education, which also acquires the quality of informational education, and its expression in the content of education and subject-oriented educational training. In particular, this applies to the general education subject “Informatics” as the main resource of information education, in the process of forming the relevant knowledge, competencies and personal qualities. The necessity of mandatory inclusion of the relevant topics in the content of information education in the context of information security and information protection has been substantiated: the state and problems of information security, personal security; goals and objectives of information security; legal and other aspects of information protection, means of protecting electronic data in the field of education.
Biometric Steganography Using MPV Technique

Biometric data is prone to attacks and threats from hackers who are professionals in cyber-crimes. Therefore, securing the data is very essential. Steganographic approach, which is a process of concealing data, is proposed as a solution to this. Biometrics are hidden inside other biometrics for safe storage and secure transmission. Also, it is designed to be robust against attacks, and cannot be detected easily. The intention of this paper is to highlight a method of hiding one image in another image by using mid position value(mpv) technique. Here we have to choose the secret biometric on which Arnold transform will be applied resulting in a scrambled version of the secret biometric. This will be enveloped inside cover image which results in a stego-image. Lastly, hidden secret biometric will be decoded from this stego image, which will first result in a scrambled secret biometric. Inverse Arnold Transform will be applied on this to finally result in the decoded secret biometric. The paper further explains the working and processes in detail.
Question Answering System for low resource language using Transfer Learning

This paper proposes a Question Answering System for Marathi language using Transfer Learning. A well performing Question Answering system leverages the word embeddings used in the system. Producing word embeddings for a language from the scratch is a drawn-out task and requires tremendous dataset and huge computing resources. Utilizing word embeddings created from a limited dataset in NLP tasks prompts average per-formance. Instead utilizing word embeddings from pre-trained models saves a lot of time, and gives great performance, since these models have more learnable parameters and are trained on huge datasets. Our framework uses Multilingual BERT model as pre-trained source model having 110M parameters which leads to effective word representation. We have fine-tuned this BERT model for QAS with the assistance of a small, custom dataset similar to SQuAD, intended for this framework. The system uses Bert-score and F1-score as its evaluation methods. It achieves F1-score of 56.7% and Bert-score of 69.08%. The system being the first of its kind in Marathi language lays the groundwork for future research.
On Longevity and Lost Toys: Sustainable Approaches to Toy Design and Contemporary Play

This chapter summarizes and synthesizes key developments within the industries of play and contemporary toy design that address sustainability in toys from two perspectives: first, from the design and production perspective, and second, from the use or play perspective. By drawing on insights from earlier work in the fields of toy and play research, a summary of key points regarding sustainable approaches to toy design and contemporary play is suggested. Good toy design aims at producing play value derived from raw material, aesthetics, mechanics, relations to storytelling and so on, but as a vague and subjective measure, play value can present different things for designers and players of different ages, depending on their ideas on the life cycles of toys and their qualifications for sustainability. Instead, relevant and more tangible aspects of play value are the physical durability of toys, which may refer to the ecological dimensions of the raw material used for toys (such as textile, wood or plastic), their functionality (afforded, versatile and enduring functions for toys such as mechanics) and the thematic durability of toys (such as intriguing backstories that invite the player to long-term play). When the toys’ play value is considered thoroughly from these perspectives, the affective responses to the plaything are strengthened. In the chapter, the concepts of longevity and becoming lost are discussed as issues worth of deliberation not only within the toy industry, but in terms of player actions with toys.
A systematic review of the application of metaverse in language education: Prominent themes, research methods, impacts, and future challenges

In the era of human-computer symbiosis, extensive research has examined the influence of educational technologies on education. The nascent field, metaverse, driven by advancements in 5G, VR, and AR, has emerged as a new frontier. Although some scholars have explored the application of the metaverse in education, there is a lack of research specifically focusing on its implications for language education. This systematic literature review aims to comprehensively examine empirical studies on the use of metaverse in language education, including, research topics, methodologies, common tools, impacts, and challenges. Through searching authoritative databases (i.e., Web of Science and Scopus) following the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines (i.e., PRISMA), 12 relevant articles were selected. Statistical analysis showed that South Korea and China are active contributors in this field, primarily investigating university students. Platforms like Ifland, Gather Town, and Metaverse Studio were commonly used for metaverse construction, along with tools such as VR. Quantitative and mixed-method approaches were favored, with research mostly focused on student and teacher perspectives and language proficiency outcomes. Overall, findings suggest positive attitudes towards metaverse implementation and its beneficial impact on learning outcomes, but caution against considering it a complete solution for language learning. Despite valuable insights, the existing literature on metaverse in language education remains limited compared to other technological domains. Further research is necessary to enrich the understanding of research directions, sample choice, experimental findings and so on.
INVESTIGATING STUDENTS ERRORS IN DESCRIPTIVE WRITING AT SECOND SEMESTER STUDENTS AT STIKES YARSI MATARAM

Analyzing the errors is one of the appropriate ways to use to show the true proficiency level of the target language that students learn at a particular time. By conducting analysis on grammatical errors, the teacher can conclude the most frequent of errors which are often made by students then, teachers can arrange the more effective and interesting strategies in teaching grammatical rules. This research was aimed to elaborate on kinds of students errors and possible causes of in writing a descriptive text. The instruments used for this research are writing tasks and questioner. This research was a descriptive qualitative method because the data of this research is in the form of sentences and paragraphs. The data gotten were then analyzed by using surface taxonomy strategy. The result of this research, It was found four types of errors in the students writing. Those are omission error, addition error, misformation error, and misordering error. Omission and misformation error is the most frequent of errors found in the text. Whereas, the sources of error found in this research are interlingual transfer, intralingual transfer, the context of learning and communication strategies. Based on the analysis researcher found that interlingual and context learning is the dominant cause of the error. The researcher can deliver suggestion especially to the teacher, they should give the clear explanation about the different rules both Indonesian and English, the teacher should emphasize the concepts of the verb tenses because the most frequent of errors relate to use subject-verb agreement.
Power Generation Prediction in Solar PV system by Machine Learning Approach

Solar energy is becoming more and more incorporated into the global power grid. As a result, enhancing the accuracy of solar energy projections is crucial for effective power grid planning, control, and operations. A fast, accurate and advanced estimation method is desperately needed to prevent PV's detrimental consequences on electricity and energy networks. For the optimum integration of solar technology into existing power systems, which benefits both grids and station operators, accurate prediction of solar production is crucial. The purpose of this research is to test the effectiveness of the machine learning model for projecting PV solar output. Using ANN in this research, weather parameters with the Power Generation for the next day appear to have been predicted. The evaluation findings suggest that the models' accuracy is sufficient to be employed with existing works and their approaches. Machine learning was shown to be capable of accurately predicting power while removing the difficulties associated with predicted solar irradiance data in this study.
A Reusable Machine-Calculus for Automated Resource Analyses

We introduce an automated resource analysis technique is introduced, targeting a Call-By-Push-Value abstract machine, with memory prediction as a practical goal. The machine has a polymorphic and linear type system enhanced with a first-order logical fragment, which encodes both low-level operational semantics of resource manipulations and high-level synthesis of algorithmic complexity. Resource analysis must involve a diversity of static analysis, for escape, aliasing, algorithmic invariants, and more. Knowing this, we implement the Automated Amortized Resource Analysis framework (AARA) from scratch in our generic system. In this setting, access to resources is a state-passing effect which produces a compile-time approximation of runtime resource usage. We implemented type inference constraint generation for our calculus, accompanied with an elaboration of bounds for iterators on algebraic datatypes, for minimal ML-style programming languages with Call-by-Value and Call-By-Push-Value semantics. The closed-formed bounds are derived as multivariate polynomials over the integers. This now serves as a base for the development of an experimental toolkit for automated memory analysis of functional languages.
Light Field Rectification Based on Relative Pose Estimation

Hand-held light field (LF) cameras have unique advantages in computer vision such as 3D scene reconstruction and depth estimation. However, the related applications are limited by the ultra-small baseline, leading to the extremely low depth resolution in reconstruction. To solve this problem, we propose to rectify LF to obtain a large baseline. Specifically, the proposed method aligns two LFs captured by two hand-held LF cameras with a random relative pose, and extracts the corresponding row-aligned sub-aperture images (SAIs) to obtain an LF with a large baseline. For an accurate rectification, a method for pose estimation is also proposed, where the relative rotation and translation between the two LF cameras are estimated. The proposed pose estimation minimizes the degree of freedom (DoF) in the LF-point-LF-point correspondence model and explicitly solves this model in a linear way. The proposed pose estimation outperforms the state-of-the-art algorithms by providing more accurate results to support rectification. The row-aligned SAIs and significantly improved depth resolution in 3D reconstruction demonstrate the effectiveness of the proposed LF rectification.
RNNP: A Robust Few-Shot Learning Approach.

Learning from a few examples is an important practical aspect of training classifiers. Various works have examined this aspect quite well. However, all existing approaches assume that the few examples provided are always correctly labeled. This is a strong assumption, especially if one considers the current techniques for labeling using crowd-based labeling services. We address this issue by proposing a novel robust few-shot learning approach. Our method relies on generating robust prototypes from a set of few examples. Specifically, our method refines the class prototypes by producing hybrid features from the support examples of each class. The refined prototypes help to classify the query images better. Our method can replace the evaluation phase of any few-shot learning method that uses a nearest neighbor prototype-based evaluation procedure to make them robust. We evaluate our method on standard mini-ImageNet and tiered-ImageNet datasets. We perform experiments with various label corruption rates in the support examples of the few-shot classes. We obtain significant improvement over widely used few-shot learning methods that suffer significant performance degeneration in the presence of label noise. We finally provide extensive ablation experiments to validate our method.
PENGEMBANGAN MEDIA PEMBELAJARAN E-MODUL INTERAKTIF BERBASIS SMARTPHONE PADA MATA KULIAH TEKNIK MENGHIAS KAIN JURUSAN PKK FT UNM

This study aims to discover: 1) the results of the development of Smartphone-based interactive e-module in Fabric Decorating Techniques Courses to improve the learning outcomes in PKK Department FT UNM, 2) whether the feasibility of the smartphone-based interactive e-module learning media developed meets valid criteria, 3) whether the feasibility of smartphone-based interactive e-module learning media developed meets practical criteria, 4) whether the feasibility of the smartphone-based interactive e-module learning media developed meets effective criteria. The method used in this study is Research and Development (R&amp;D). The research subjects were 32 students and 2 lecturers in PKK Department FT UNM. Data collection techniques used were tests, questionnaires, and e-module validation. The module development process employed the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) e-development model. The analysis phase includes an analysis of the needs for learning resources, curriculum, and materials. The design stage includes creating the e-module framework, determining the layout and reference book, and preparing the e-module assessment instrument. The development stage includes e-module development, expert validation and e-module revision. The implementation stage is the trial stage of the e-module, which had been validated and revised for students. The evaluation stage is the stage of analyzing the strengths and weaknesses of the e-module that had been tested. The quality of the module is determined by three criteria: validity, practicality, and effectiveness. This e-module is declared as valid based on an assessment from the material expert with an average of 87% (very valid) and an assessment from the media expert with an average of 91% (very valid). Moreover, the e-module is declared as practical for use by students based on the results of the students’ response questionnaire with an average of 83% (Very practical). Furthermore, based on the test results before and after using the e-module, it is declared as effective for use by students based on the results of the pretest and posttest with an average score of 71% (effective).
Entanglement-Assisted Entanglement Purification

The efficient generation of high-fidelity entangled states is the key element for long-distance quantum communication, quantum computation and other quantum technologies, and at the same time the most resource-consuming part in many schemes. We present a new class of entanglement-assisted entanglement purification protocols that can generate high-fidelity entanglement from noisy, finite-size ensembles with improved yield and fidelity as compared to previous approaches. The scheme utilizes high-dimensional auxiliary entanglement to perform entangling non-local measurements and determine the number and positions of errors in an ensemble in a controlled and efficient way, without disturbing the entanglement of good pairs. Our protocols can deal with arbitrary errors, but are best suited for few errors, and work particularly well for decay noise. Our methods are applicable to moderate sized ensembles, as will be important for near term quantum devices.
Language-Guided Navigation via Cross-Modal Grounding and Alternate Adversarial Learning

The emerging vision-and-language navigation (VLN) problem aims at learning to navigate an agent to the target location in unseen photo-realistic environments according to the given language instruction.The main challenges of VLN arise mainly from two aspects: first, the agent needs to attend to the meaningful paragraphs of the language instruction corresponding to the dynamically-varying visual environments; second, during the training process, the agent usually imitate the expert demonstrations,i.e., the shortest-path to the target location specified by associated language instructions.Due to the discrepancy of action selection between training and inference, the agent solely on the basis of imitation learning does not perform well.Existing VLN approaches address this issue by sampling the next action from its predicted probability distribution during the training process.This allows the agent to explore diverse routes from the environments, yielding higher success rates.Nevertheless, without being presented with the golden shortest navigation paths during the training process, the agent may arrive at the target location through an unexpected longer route.To overcome these challenges, we design a cross-modal grounding module, which is composed of two complementary attention mechanisms, to equip the agent with a better ability to track the correspondence between the textual and visual modalities.We then propose to recursively alternate the learning schemes of imitation and exploration to narrow the discrepancy between training and inference.We further exploit the advantages of both these two learning schemes via adversarial learning.Extensive experimental results on the Room-to-Room (R2R) benchmark dataset demonstrate that the proposed learning scheme is generalized and complementary to prior arts.Our method performs well against state-of-the-art approaches in terms of effectiveness and efficiency.
Quantum optimal control of multilevel dissipative quantum systems with reinforcement learning

Manipulate and control of the complex quantum system with high precision are essential for achieving universal fault tolerant quantum computing. For a physical system with restricted control resources, it is a challenge to control the dynamics of the target system efficiently and precisely under disturbances. Here we propose a multi-level dissipative quantum control framework and show that deep reinforcement learning provides an efficient way to identify the optimal strategies with restricted control parameters of the complex quantum system. This framework can be generalized to be applied to other quantum control models. Compared with the traditional optimal control method, this deep reinforcement learning algorithm can realize efficient and precise control for multi-level quantum systems with different types of disturbances.
Learning From Synthetic Shadows for Shadow Detection and Removal

Shadow removal is an essential task in computer vision and computer graphics. Recent shadow removal approaches all train convolutional neural networks (CNN) on real paired shadow/shadow-free or shadow/shadow-free/mask image datasets. However, obtaining a large-scale, diverse, and accurate dataset has been a big challenge, and it limits the performance of the learned models on shadow images with unseen shapes/intensities. To overcome this challenge, we present SynShadow, a novel large-scale synthetic shadow/shadow-free/matte image triplets dataset and a pipeline to synthesize it. We extend a physically-grounded shadow illumination model and synthesize a shadow image given an arbitrary combination of a shadow-free image, a matte image, and shadow attenuation parameters. Owing to the diversity, quantity, and quality of SynShadow, we demonstrate that shadow removal models trained on SynShadow perform well in removing shadows with diverse shapes and intensities on some challenging benchmarks. Furthermore, we show that merely fine-tuning from a SynShadow-pre-trained model improves existing shadow detection and removal models. Codes are publicly available at https://github.com/naoto0804/SynShadow.
KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision

Modern models of event causality detection (ECD) are mainly based on supervised learning from small hand-labeled corpora.However, hand-labeled training data is expensive to produce, low coverage of causal expressions and limited in size, which makes supervised methods hard to detect causal relations between events.To solve this data lacking problem, we investigate a data augmentation framework for ECD, dubbed as Knowledge Enhanced Distant Data Augmentation (KnowDis).Experimental results on two benchmark datasets EventStoryLine corpus and Causal-TimeBank show that 1) KnowDis can augment available training data assisted with the lexical and causal commonsense knowledge for ECD via distant supervision, and 2) our method outperforms previous methods by a large margin assisted with automatically labeled training data.