Reviewerly/exHarmony huggingface dataset:
Students’ Cohesion Used by Eleventh Graders in Writing Report Text

Abstract One of the hardest skills in English is writing. Students need time for being able to write well. Because writing is a composition with many principles in it. There are some components in writing in order to make a well composition. One of the components is cohesion and it needs cohesive devices to fulfil. Cohesive devices have four components, they are conjunctions, definite article, personal pronouns, and demonstrative pronouns. The aim of this research is to know how the students of eleventh grade use cohesive devices in their writing report text. This research uses qualitative as the research design and explains descriptively. The research takes place in SMAN 1 Kediri and the students of eleventh grade as the subject of the study. The researcher does document analysis by analysing students’ writing by looking at the cohesive devices that are used. The data are analysed by reducing data, displaying data, and making conclusion. The result is the students of eleventh grade used cohesive devices n their writing report text quite well. Most of them used all cohesive devices to make their writing looks good. Although, one component of cohesive devices was used a little by the students, their writing still consists one or two of it. Finally, the students use cohesive devices in their writing report text although the teacher did not explain about it.
Human Machine Interaction and Meanings of Machine Learning - A Case of Hand Posture Recognition from Wrist-Worn Camera

Trung-Hieu Le, Nguyen Thuy Dung, Dinh Tran Ngoc Huy et al (2021) stated that Hand Gestures Recognition can be supported with IoTs Solution System Design.Moreover, Hand gestures have been shown to be an efficient way for human-machine interaction. Authors presents a new way to capture hand gestures using the wrist-worn camera. The wrist-worn device is designed as a watch with an integrated camera that is much easier and comfortable to wear in daily life context. Hieu, L.T et al (2019) specified that Hand gestures for human machine interaction using wearable sensors have more potentiality than ambient sensing thanks to its low-cost, light weight and mostly scalablity every where at anytime. Despite the fact of existing works on human hand gestures using wearable sensors, each focuses on a specific application and difficult to be generalized.. Experimental results show that with limited camera angles, the postures are highly distinctive and easily discriminated with the highest performance of 98.85% and 97.40% in terms of precision and recall, which motivates a wide range of applications and new research directions for human-machine interaction, wearables, the Internet of Things (IoT) and so on.
Analysis Of Human Acitivities Identification Based On Combined Image Carrier Applications In Education - And Ho Chi Minh Views On Science Technology For National Education

During covid 19 time, it is better to research on technology applications in improving quality of teaching in education sector in developing countries such as Vietnam.Nam, V.Q., Tinh, D.T., Huy, D.T.N, Hieu, L.T., & Huong, L.T.T (2021) stated Not only Internet of things (IoTs), but Artificial intelligence (AI) also has some certain influence on several sectors such as education, industries, etc. In emerging markets such as Vietnam. They also affect risk management information system of both IT firms and other industrial companies.Hence, This paper find out technology applications such as video camera or webcams can be used to observe learners’ behaviors and human activities during online teaching in order to monitor quality of teaching and adjust teaching programs if needed to attract learners. Moreover, TQM or ISO standards and requirements can be applied into education and e -learning process, for instance: ISO required: Derived from the customer's request , Solve client complaints and  Satisfy customer demand.Last but not least, from Ho Chi Minh views and ideologies, science technology has vital roles for education in order to serve for economic development. Development of science technology need to go together with education, socio-economic development and national defense.
A Privacy-Preserving Energy Consumption Data Sharing Framework for Smart Grids

In the era of big data, smart grid utilities are seeking new opportunities to create profits by making use of in-hand power big data. However, these data, such as energy consumption data, power grid measurement data, etc., are often sensitive digital assets that cannot be directly shared to third parties or individual users. To meet this gap, in this paper we propose a Privacy-Preserving energy consumption Data Sharing framework (PPDS) for smart grids. In this framework, two Paillier-based algorithms are designed respectively to achieve privacy-preserving data acquisition and secure computation of the mean for energy consumption data in a specific period or regions. Numerical results demonstrate that the proposed PPDS framework is efficient in privacy-preserving energy consumption data sharing.
VLGrammar: Grounded Grammar Induction of Vision and Language

Cognitive grammar suggests that the acquisition of language grammar is grounded within visual structures. While grammar is an essential representation of natural language, it also exists ubiquitously in vision to represent the hierarchical part-whole structure. In this work, we study grounded grammar induction of vision and language in a joint learning framework. Specifically, we present VLGrammar, a method that uses compound probabilistic context-free grammars (compound PCFGs) to induce the language grammar and the image grammar simultaneously. We propose a novel contrastive learning framework to guide the joint learning of both modules. To provide a benchmark for the grounded grammar induction task, we collect a large-scale dataset, \textsc{PartIt}, which contains human-written sentences that describe part-level semantics for 3D objects. Experiments on the \textsc{PartIt} dataset show that VLGrammar outperforms all baselines in image grammar induction and language grammar induction. The learned VLGrammar naturally benefits related downstream tasks. Specifically, it improves the image unsupervised clustering accuracy by 30\%, and performs well in image retrieval and text retrieval. Notably, the induced grammar shows superior generalizability by easily generalizing to unseen categories.
Efficient Visual Cryptography Technique for Image Security

In today’s generations information acts as crucial role in every organization. Most of the organizations spending lots of amount just to protect the data due to increasing of cyber threats. We must provide the security to these confidential data before transmitting into network and while storing data in cloud storages. To provide the security to visual information from unauthorized persons we need to convert original information to unreadable format. So, to get this unreadable format we have a technique called as visual cryptography. Visual cryptography is a powerful technique for dividing a secret image into two or more number of shares also called as encryption and these shares are decrypting using the human visual system (HVS) without any computational method. In our proposed model first, we will apply visual cryptography to input secret image for generating shares (dotted image). We have generated 10 shares for secret image after that implemented Secured ECC cryptography algorithm for securing shares. These shares will send to receiver through transmitting carrier. Also, our model was analysed with evaluation parameters which is (Peak Signal to Noise Ratio(PSNR) and Mean Square Error (MSE).
Literasi Baca Tulis dalam Menyimak Lagu Bagi Pemelajar Bahasa Indonesia bagi Penutur Asing

Song and music are an inseparable unity and if discussed will be an interesting part. Related to songs in the Indonesian language learning process for foreign speakers is one of the teaching strategies in an effective, fun, and efficient learning process. The problem related to the literacy of a lesson is the lack of innovation from the teacher. Therefore, using songs as a medium or means of learning can be an alternative for teaching Indonesian to foreign speakers. Especially the song called Manusia Kuat by Tulus. In the song, it is illustrated that every human being has a dream that must be achieved, do not never give up, and always be enthusiastic. The song can be used in learning to complete overlapping sentences by listening to songs without visuals and observing visuals without audio by mentioning what objects are in the visual. With that, students will get a friendly, fun atmosphere and increase students' interest in learning about Indonesian. Keywords : Literacy, Song, Manusia Kuat, BIPA
Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement

Real-world low-light images suffer from two main degradations, namely, inevitable noise and poor visibility. Since the noise exhibits different levels, its estimation has been implemented in recent works when enhancing low-light images from raw Bayer space. When it comes to sRGB color space, the noise estimation becomes more complicated due to the effect of the image processing pipeline. Nevertheless, most existing enhancing algorithms in sRGB space only focus on the low visibility problem or suppress the noise under a hypothetical noise level, leading them impractical due to the lack of robustness. To address this issue,we propose an adaptive unfolding total variation network (UTVNet), which approximates the noise level from the real sRGB low-light image by learning the balancing parameter in the model-based denoising method with total variation regularization. Meanwhile, we learn the noise level map by unrolling the corresponding minimization process for providing the inferences of smoothness and fidelity constraints. Guided by the noise level map, our UTVNet can recover finer details and is more capable to suppress noise in real captured low-light scenes. Extensive experiments on real-world low-light images clearly demonstrate the superior performance of UTVNet over state-of-the-art methods.
SCOUTER: Slot Attention-based Classifier for Explainable Image Recognition

Explainable artificial intelligence has been gaining attention in the past few years. However, most existing methods are based on gradients or intermediate features, which are not directly involved in the decision-making process of the classifier. In this paper, we propose a slot attention-based classifier called SCOUTER for transparent yet accurate classification. Two major differences from other attention-based methods include: (a) SCOUTER's explanation is involved in the final confidence for each category, offering more intuitive interpretation, and (b) all the categories have their corresponding positive or negative explanation, which tells "why the image is of a certain category" or "why the image is not of a certain category." We design a new loss tailored for SCOUTER that controls the model's behavior to switch between positive and negative explanations, as well as the size of explanatory regions. Experimental results show that SCOUTER can give better visual explanations in terms of various metrics while keeping good accuracy on small and medium-sized datasets.
A global convergence theory for deep ReLU implicit networks via over-parameterization

Implicit deep learning has received increasing attention recently due to the fact that it generalizes the recursive prediction rules of many commonly used neural network architectures. Its prediction rule is provided implicitly based on the solution of an equilibrium equation. Although a line of recent empirical studies has demonstrated its superior performances, the theoretical understanding of implicit neural networks is limited. In general, the equilibrium equation may not be well-posed during the training. As a result, there is no guarantee that a vanilla (stochastic) gradient descent (SGD) training nonlinear implicit neural networks can converge. This paper fills the gap by analyzing the gradient flow of Rectified Linear Unit (ReLU) activated implicit neural networks. For an $m$-width implicit neural network with ReLU activation and $n$ training samples, we show that a randomly initialized gradient descent converges to a global minimum at a linear rate for the square loss function if the implicit neural network is \textit{over-parameterized}. It is worth noting that, unlike existing works on the convergence of (S)GD on finite-layer over-parameterized neural networks, our convergence results hold for implicit neural networks, where the number of layers is \textit{infinite}.
Magic Pyramid: Accelerating Inference with Early Exiting and Token Pruning

Pre-training and then fine-tuning large language models is commonly used to achieve state-of-the-art performance in natural language processing (NLP) tasks. However, most pre-trained models suffer from low inference speed. Deploying such large models to applications with latency constraints is challenging. In this work, we focus on accelerating the inference via conditional computations. To achieve this, we propose a novel idea, Magic Pyramid (MP), to reduce both width-wise and depth-wise computation via token pruning and early exiting for Transformer-based models, particularly BERT. The former manages to save the computation via removing non-salient tokens, while the latter can fulfill the computation reduction by terminating the inference early before reaching the final layer, if the exiting condition is met. Our empirical studies demonstrate that compared to previous state of arts, MP is not only able to achieve a speed-adjustable inference but also to surpass token pruning and early exiting by reducing up to 70% giga floating point operations (GFLOPs) with less than 0.5% accuracy drop. Token pruning and early exiting express distinctive preferences to sequences with different lengths. However, MP is capable of achieving an average of 8.06x speedup on two popular text classification tasks, regardless of the sizes of the inputs.
Does enforcing fairness mitigate biases caused by subpopulation shift?

Many instances of algorithmic bias are caused by subpopulation shifts. For example, ML models often perform worse on demographic groups that are underrepresented in the training data. In this paper, we study whether enforcing algorithmic fairness during training improves the performance of the trained model in the \emph{target domain}. On one hand, we conceive scenarios in which enforcing fairness does not improve performance in the target domain. In fact, it may even harm performance. On the other hand, we derive necessary and sufficient conditions under which enforcing algorithmic fairness leads to the Bayes model in the target domain. We also illustrate the practical implications of our theoretical results in simulations and on real data.
Dual Progressive Prototype Network for Generalized Zero-Shot Learning

Generalized Zero-Shot Learning (GZSL) aims to recognize new categories with auxiliary semantic information,e.g., category attributes. In this paper, we handle the critical issue of domain shift problem, i.e., confusion between seen and unseen categories, by progressively improving cross-domain transferability and category discriminability of visual representations. Our approach, named Dual Progressive Prototype Network (DPPN), constructs two types of prototypes that record prototypical visual patterns for attributes and categories, respectively. With attribute prototypes, DPPN alternately searches attribute-related local regions and updates corresponding attribute prototypes to progressively explore accurate attribute-region correspondence. This enables DPPN to produce visual representations with accurate attribute localization ability, which benefits the semantic-visual alignment and representation transferability. Besides, along with progressive attribute localization, DPPN further projects category prototypes into multiple spaces to progressively repel visual representations from different categories, which boosts category discriminability. Both attribute and category prototypes are collaboratively learned in a unified framework, which makes visual representations of DPPN transferable and distinctive. Experiments on four benchmarks prove that DPPN effectively alleviates the domain shift problem in GZSL.
ZeBRA: Precisely Destroying Neural Networks with Zero-Data Based Repeated Bit Flip Attack

In this paper, we present Zero-data Based Repeated bit flip Attack (ZeBRA) that precisely destroys deep neural networks (DNNs) by synthesizing its own attack datasets. Many prior works on adversarial weight attack require not only the weight parameters, but also the training or test dataset in searching vulnerable bits to be attacked. We propose to synthesize the attack dataset, named distilled target data, by utilizing the statistics of batch normalization layers in the victim DNN model. Equipped with the distilled target data, our ZeBRA algorithm can search vulnerable bits in the model without accessing training or test dataset. Thus, our approach makes the adversarial weight attack more fatal to the security of DNNs. Our experimental results show that 2.0x (CIFAR-10) and 1.6x (ImageNet) less number of bit flips are required on average to destroy DNNs compared to the previous attack method. Our code is available at https://github. com/pdh930105/ZeBRA.
Catering Students Acceptance Towards the Use of Smartphone Application in Food Presentation

Today's technological advancements have brought many changes, especially the use of the Internet in education. Learning to use a smartphone app is a learning approach that addresses knowledge and skills acquisition through mobile technology. The purpose of this study is to measure (1) student acceptance factors in the application of learning (2) the impact of student acceptance on application usage and (3) the level of student acceptance in application usage for the study of the art of food presentation. This study was conducted on a sample of 60 undergraduate students. The students were from Faculty of Technical and Vocational, University Tun Hussein Onn Malaysia (UTHM). A set of questionnaire was used as an instrument. The data was descriptive analysis using Statistical Package for Social Science (SPSS) version 18. Percentage, mean and standard deviation were obtained from theanalysis process. In this research finding, it showed that the factors of student acceptance in the application of learning, the impact of student acceptance in application usage and the level of student acceptance in the application of learning for the presentation of the art of food presentation are respectively high with mean scores of 4.08, 3.81 and 4.00. According to the results, some suggestions for further research to enable application learning are also highlighted.
An Online Riemannian PCA for Stochastic Canonical Correlation Analysis

We present an efficient stochastic algorithm (RSG+) for canonical correlation analysis (CCA) using a reparametrization of the projection matrices. We show how this reparametrization (into structured matrices), simple in hindsight, directly presents an opportunity to repurpose/adjust mature techniques for numerical optimization on Riemannian manifolds. Our developments nicely complement existing methods for this problem which either require $O(d^3)$ time complexity per iteration with $O(\frac{1}{\sqrt{t}})$ convergence rate (where $d$ is the dimensionality) or only extract the top $1$ component with $O(\frac{1}{t})$ convergence rate. In contrast, our algorithm offers a strict improvement for this classical problem: it achieves $O(d^2k)$ runtime complexity per iteration for extracting the top $k$ canonical components with $O(\frac{1}{t})$ convergence rate. While the paper primarily focuses on the formulation and technical analysis of its properties, our experiments show that the empirical behavior on common datasets is quite promising. We also explore a potential application in training fair models where the label of protected attribute is missing or otherwise unavailable.
Multi-Person 3D Motion Prediction with Multi-Range Transformers

We propose a novel framework for multi-person 3D motion trajectory prediction. Our key observation is that a human's action and behaviors may highly depend on the other persons around. Thus, instead of predicting each human pose trajectory in isolation, we introduce a Multi-Range Transformers model which contains of a local-range encoder for individual motion and a global-range encoder for social interactions. The Transformer decoder then performs prediction for each person by taking a corresponding pose as a query which attends to both local and global-range encoder features. Our model not only outperforms state-of-the-art methods on long-term 3D motion prediction, but also generates diverse social interactions. More interestingly, our model can even predict 15-person motion simultaneously by automatically dividing the persons into different interaction groups. Project page with code is available at https://jiashunwang.github.io/MRT/.
Cross-Modal Object Tracking: Modality-Aware Representations and a Unified Benchmark

In many visual systems, visual tracking often bases on RGB image sequences, in which some targets are invalid in low-light conditions, and tracking performance is thus affected significantly. Introducing other modalities such as depth and infrared data is an effective way to handle imaging limitations of individual sources, but multi-modal imaging platforms usually require elaborate designs and cannot be applied in many real-world applications at present. Near-infrared (NIR) imaging becomes an essential part of many surveillance cameras, whose imaging is switchable between RGB and NIR based on the light intensity. These two modalities are heterogeneous with very different visual properties and thus bring big challenges for visual tracking. However, existing works have not studied this challenging problem. In this work, we address the cross-modal object tracking problem and contribute a new video dataset, including 654 cross-modal image sequences with over 481K frames in total, and the average video length is more than 735 frames. To promote the research and development of cross-modal object tracking, we propose a new algorithm, which learns the modality-aware target representation to mitigate the appearance gap between RGB and NIR modalities in the tracking process. It is plug-and-play and could thus be flexibly embedded into different tracking frameworks. Extensive experiments on the dataset are conducted, and we demonstrate the effectiveness of the proposed algorithm in two representative tracking frameworks against 19 state-of-the-art tracking methods. Dataset, code, model and results are available at https://github.com/mmic-lcl/source-code.