Reviewerly/exHarmony huggingface dataset:
A One-Shot Reparameterization Method for Reducing the Loss of Tile Pruning on DNNs

Recently, tile pruning has been widely studied to accelerate the inference of deep neural networks (DNNs). However, we found that the loss due to tile pruning, which can eliminate important elements together with unimportant elements, is large on trained DNNs. In this study, we propose a one-shot reparameterization method, called TileTrans, to reduce the loss of tile pruning. Specifically, we repermute the rows or columns of the weight matrix such that the model architecture can be kept unchanged after reparameterization. This repermutation realizes the reparameterization of the DNN model without any retraining. The proposed reparameterization method combines important elements into the same tile; thus, preserving the important elements after the tile pruning. Furthermore, TileTrans can be seamlessly integrated into existing tile pruning methods because it is a pre-processing method executed before pruning, which is orthogonal to most existing methods. The experimental results demonstrate that our method is essential in reducing the loss of tile pruning on DNNs. Specifically, the accuracy is improved by up to 17% for AlexNet while 5% for ResNet-34, where both models are pre-trained on ImageNet.
Preventing Privacy-Violating Information Flows in JavaScript Applications Using Dynamic Labelling

Web browser-based applications deal with humongous user information using applications of web scripts. In particular, JavaScript applications access information through built-in browser APIs that dynamically load remote scripts and execute with the same privilege as that of the applications – usually referred to as mashup model. Unfortunately, this allows malicious JavaScripts to manipulate the given browser functionalities leading to various web attacks violating users’ privacy. Moreover, with the rapid growth of e-commerce sectors, malicious scripts pose a significant challenge to digital transactions. In this paper, we propose an approach that prevents various web-based attacks such as code injection, cross-site scripting (XSS). The approach adopts a Dynamic Labelling algorithm that generates information flow security policies automatically for local variables in JavaScript based on the given policies for sensitive variables. Unlike existing solutions that are too conservative primarily due to the generic flow policies leading to false-alarms, our solution leads to realizing conditions as to when a script accepts the parameters returned by a dynamic script; thus enabling us to build an automatic platform for preventing information flows to malicious scripts without explicit characterization by programmers or users.
Deep Ensemble Learning Model for Face Recognition using Smart Phone

Mobility necessitates a significant degree of capture condition variability, which is difficult to manage and has a direct impact on extraction of facial characteristics and face detection. The much more intriguing option for automatic facial recognition seems to be deep learning, although these systems heavily rely on the model created during the training phase. The scale of the models also makes it challenging to integrate them into apps smartphone, especially when the model needs to be incorporated. In this study, a deep learning ensemble model was trained to recognize faces on low-resource devices and tested for precision, size, and timing to produce quantitative results. Since the goal of this study is to include so many more scenarios as possible, a variety of databases, including both public and private datasets designed to replicate the complexity of mobile scenarios, were used. In order to conduct a fair comparison, publicly accessible models and conventional methodologies were also included in the study. Additionally, the assessment is supplemented with various classifiers and detectors due to the importance of the template matching and face identification stages. Finally, a Java-based Android application is created and tested in order to gather performance information on the entire system that is built into a mobile device.
International practice of the activity approach in higher pedagogical education

We present the results of a comparative pedagogical study, the purpose of which was to identify and characterize the directions of application of the activity approach (as a key methodological basis) implemented in the world educational practice to the organization of the system of lecturer training. The study was conducted using classical methods for comparative pedagogy: comparative, descriptive, inductive-deductive. The considered in the study trends determine the general patterns of designing educational programs for lecturer training in many countries of the world with high quality education. A detailed description of the essential content of the epistemic spaces of the activity approach is given: paradigms, syntagmas and pragmatics, taking into account the specifics of the university training of teachers in Russia, the USA, the Netherlands, Norway, Sweden, Cyprus, Thailand, Malaysia and other countries. As the key guidelines of modern higher pedagogical education from the perspective of the activity approach the following are designated: a target orientation for continuous professional self-improvement, meaning-forming preparation for solving life tasks, personalization of the educational environment of the university, organization of joint network activities. The application of the activity approach correlates with the educational results, which are normatively fixed in the standards. The principles of the system-modular organization of higher pedagogical education are updated. We consider the specific features of the implementation of the conceptual foundations of the activity approach in higher pedagogical education at the present stage of the development of world educational systems. Based on the generalization and systematization of a number of theoretical and empirical scientific and pedagogical studies, it is concluded that it is necessary to preserve the activity basis in the practice of implementing educational programs of higher pedagogical education, with the introduction of innovations that meet the general trends in the functioning of educational systems around the world.
AN ANALYSIS OF SYNTAX ERRORS IN DESCRIPTIVE ESSAYS

This research was conducted to analyze the syntax error. The object of this research is a descriptive essay for English education students batch 2019 which was made during the second semester. The author's aim in this study is to analyze the existence of syntax errors and the types of errors in writing descriptive essays. In this study, the author uses a qualitative descriptive method. The results of the research in the findings and discussion section stated that the descriptive essays of English education students in class 2019 stated that there were a number of syntax errors. In the finding section, the writer found eleven types of syntax error sentences in three student descriptive essays. From eleven example sentences, the writer finds the location of errors in the structure of phrases, clauses, and sentences. And from the three error syntax areas, two types of syntax errors were found by students, there are omission and addition. The most syntax errors are phrase structure errors in omission and the smallest is clause errors in the ‘Wh’ question. Many sentence errors in omission because incorrect because there is no verb in the clause. Each clause needs a subject and a verb. In some cases, the best verb to use is a simple is or was (or some other form of the verb to be). Keywords: error analysis, syntax error, descriptive essay.
Data Leakage Attack via Backdoor Misclassification Triggers of Deep Learning Models

In recent years, deep neural networks (DNNs) have been successfully applied in various tasks, and various third-party models are available to data holders. However, data holders who blindly use third-party models to train on their data may lead to data leakage, resulting in serious data privacy problems. The Capacity Abuse Attack (CAA) is the state-of-the-art black-box attack method which uses the labels of the augmented malicious dataset to encode the information of the training data. However, the expanded malicious dataset in CAA are artificially synthesized, not natural images, and significantly different from the original training data. Thus these malicious images are easy to be detected. In our attack, we use a technique similar to generating poisoned datasets in backdoor attacks, make malicious data generated similar to real and natural images, and make our attack more concealed. Extensive experiments are conducted, and the results demonstrate that our attack can effectively obtain the private training data of data holders without significantly impacting the model's original task.
Cross-Domain Character Recognition through Latent Space Alignment

Deep neural networks have proved its capability in many machine learning tasks. The effectiveness of deep neural networks in real-world applications, however, is greatly affected by the distribution discrepancy between the training and testing data. To address the issue, domain adaptation methods have been studied. In this work, we propose a novel unsupervised domain adaptation method which combines the feature learning and the distribution estimation into one learning framework, enabling automatic update of feature representations through fine-tuning parameterized distributions. As such, our model can produce an unified distribution to represent both source and target samples. Furthermore, two new regularizers are integrated into the optimization objective to minimize the divergence of the unified distribution from those of source and target domains. Experiments on character reconstruction show that our method demonstrates much better learning ability compared to the existing variational autoencoder. More importantly, our method improves recognition accuracy by more than 5% from that of the state-of-the-art methods in domain adaptation tasks built upon popular character datasets.
Integrating whole person education in physics asynchronous learning about rainbow

In the past years, the calling of whole-person education that encourages a holistic approach to teaching and learning speaks louder. This study was conducted in a freshmen class to address the challenges of advocating whole-person education to combine academic quality with a humanistic approach to student development. This study aims to design an asynchronous physics learning about the rainbow that integrates whole-person education through an English for Science course. The benefit of this study is to give an example of conducting asynchronous physics learning that integrates whole-person education. This research is qualitative research using the narrative inquiry technique. The respondents were 10 freshmen who took the English for Science course. The learning is held in virtual asynchronous mode, except the evaluation is held in virtual synchronous mode. This study performs a design an asynchronous physics learning about the rainbow that integrates whole-person education through English for Science course. The learning steps effectively enables achieving the learning objective and integrating whole-person education in asynchronous physics learning. The teacher successfully develops the whole person – intellectually, spiritually, and ethically, connecting academics (physics concept) to values (spiritual values). It is an example to advocate whole-person education in combining academic quality with a humanistic approach to student development.
Haze Removal for Remote Sensing Images Using Residual Attentive Atmospheric Scattering Network

Due to the complexity of remote sensing image scene, it is a great challenge to obtain high-quality haze-free remote sensing images. In this paper, a haze removal network for remote sensing images called residual attentive atmospheric scattering network (RAASNet) is proposed. The network first predicts transmission map and atmospheric light value combined with neural network, and calculates preliminary haze removal result through the atmospheric scattering model. Then a haze-free image is obtained through the enhancement model. In this network, residual attentive block is added into the transmission map prediction network to enhance accuracy of the predicted image, and adjustment loss function makes the output closer to real haze-free image. Through qualitative and quantitative analysis of experimental results compared with other state-of-the-art methods, our method has good performance in haze removal for remote sensing images with different landscape and different resolution. And outputs of our network have high color fidelity and rich image details.
Trends and Research on the Teaching and Learning of Mathematics in Higher Education Institutions Through Mobile Learning

In the last decade, the use of mobile devices has been intensified at all educational levels. They have recently been included in the design of strategies and methodologies that contribute to mathematics education, especially in the resolution of mathematical problems. In this research, a systematic review is carried out on the use of mobile devices in the teaching and learning of mathematics in higher education institutions, with the purpose of identifying advantages, limitations, effectiveness, trends, and characteristics that have been presented in the last 10 years. Thirty articles were selected between 2011 and 2021 in 15 indexed journals with three specialized on mobile learning. The insights found allow us to see the current state of the use of mobile learning in the teaching and learning of mathematics in higher education institutions and evolution in new research in mathematics educational scenarios.
Flow Synthesis Based Visual Servoing Frameworks for Monocular Obstacle Avoidance Amidst High-Rises

We propose a novel flow synthesis based visual servoing framework enabling long-range obstacle avoidance for Micro Air Vehicles (MAV) flying amongst tall skyscrapers. Recent deep learning based frameworks use optical flow to do high-precision visual servoing. In this paper, we explore the question: can we design a surrogate flow for these high-precision visual-servoing methods, which leads to obstacle avoidance? We revisit the concept of saliency for identifying high-rise structures in/close to the line of attack amongst other competing skyscrapers and buildings as a collision obstacle. A synthesised flow is used to displace the salient object segmentation mask. This flow is so computed that the visual servoing controller maneuvers the MAV safely around the obstacle. In this approach, we use a multi-step Cross-Entropy Method (CEM) based servo control to achieve flow convergence, resulting in obstacle avoidance. We use this novel pipeline to successfully and persistently maneuver high-rises and reach the goal in simulated and photo-realistic real-world scenes. We conduct extensive experimentation and compare our approach with optical flow and short-range depth-based obstacle avoidance methods to demonstrate the proposed framework’s merit. Additional Visualisation can be found at https://sites.google.com/view/monocular-obstacle/home
Analysis of Tuberculosis Disease Spreading Pattern in Muara Enim District using KNN Algorithm

Tuberculosis (TB) is a type of infectious disease caused by Mycobacterium tuberculosis, which not only attacks the lungs, but can also attack the bones, intestines, or glands. During the Covid-19 pandemic, TB cases in Indonesia also increased. TB and Covid-19 had the similar symptoms such as cough, fever, and breathing difficulty, so that TB sufferers must be given serious treatment to avoid Covid-19. In predicting a disease, it is important for health workers to make decisions, thus it is necessary to do an early diagnosis in order to reduce the transmission of TB in the community. There are many algorithm methods used in conducting data analysis, for this study the authors use K-Nearest Neighbor (K-NN) algorithm and Logistic Regression as comparison. Experimental results using available dataset collected from health centers in Muara Enim District of South Sumatra Province show that the K-NN algorithm provides the best accuracy of 89% on dataset with training to testing data ratio of 80%:20%, while the Logistic Regression provides the best accuracy of 96% on 70%:30% ratio. The analysis mechanism discussed in this paper may be considered as tool for the authority to predict and take necessary actions to prevent the TB spreading.
GeoGTI: Towards a General, Transferable and Interpretable Site Recommendation

Lack of data and weak interpretability are the main problems faced by store site recommendations. This paper presents a unified site recommendation system called GeoGTI (General,Transferable and Interpretable), which applies to different brands in various industries. Different from the existing single-dimensional transfer methods, we adopt multi-layer knowledge transfer, leveraging knowledge from industries, competitive brands, upper administrative districts, and other cities to alleviate the problems of data scarcity and cold-start. Besides, to fill in the gap of weak interpretability, we score the candidate locations into a five-dimension radar chart from population, business, living, working, and transportation aspects, making the recommended result more convincing and instructive. Extensive experiments are conducted on real-world datasets from various industries, demonstrating GeoGTI’s practicability and effectiveness on store site recommendations.
Traitor Tracing in Broadcast Encryption using Vector Keys

Secured data transmission between one to many authorized users is achieved through Broadcast Encryption (BE). In BE, the source transmits encrypted data to multiple registered users who already have their decrypting keys. The Untrustworthy users, known as Traitors, can give out their secret keys to a hacker to form a pirate decoding system to decrypt the original message on the sly. The process of detecting the traitors is known as Traitor Tracing in cryptography. This paper presents a new Black Box Tracing method that is fully collusion resistant and it is designated as Traitor Tracing in Broadcast Encryption using Vector Keys (TTBE-VK). The proposed method uses integer vectors in the finite field Zp as encryption/decryption/tracing keys, reducing the computational cost compared to the existing methods.
A Real-time Semantic Segmentation Method Based on Multi-level Feature Fusion

The performance improvement for real-time segmentation networks is generally to accelerate the segmentation speed of the model at the cost of computational cost, ignoring the problem of semantic inconsistency of neighborhood features, which causes the accuracy of segmented images to decrease. Therefore, it is crucial to take into account the segmentation efficiency while ensuring the accuracy of model segmentation. In this paper, a lightweight model based on Multi-level Feature Fusion Semantic Segmentation Network (MLFFNet) is proposed, and the network as a whole adopts a two-branch structure to differentiate different types of features. The model obtained 81.4 FPS forward inference speed and 71.3% segmentation accuracy on the Cityscapes dataset, which is capable of real-time semantic segmentation tasks and proposes a new idea for the semantic segmentation problem in a complex context.
Independent German Learning Through the Mobile Learning Funeasylearn Application for the 10th Graders of Lintas Minat SMAN 9 Malang

This article aims to describe the independent learning process through FunEasyLearn application and student responses to the use of application. The research method in this study was descriptive qualitative. Sources of data were 36 students of 10th grade IPS 2 SMAN 9 Malang, while data were obtained through two different techniques, namely observations and interviews. The results showed that the independent learning process through the FunEasyLearn application went well. This also helps students to build their independent learning skills. During the learning process, students were responsible for completing the exercises in the apps independently. Furthermore, they had no difficulty in operating the apps. They were also interested and motivated to learn German with the help of FunEasyLearn because it has colorful and illustrated features as well and various exercises so that easier for them to understand the material. Overall, students showed positive responses to the use of the apps independently. They stated that their vocabs mastery increased after using the apps. Therefore, the FunEasyLearn apps can be used continuously as an alterative media for students to enlarge German vocabulary and build their independent learning both in and outside of the classroom.
Graph Coloring Applications in Scheduling Courses using Welch-Powell Algorithm - A Case Study

Subject scheduling is a routine activity carried out by each school before the start of the new school year, so the subject schedule is arranged in such a way, so that there are no identical or conflicting schedules, and no teachers teach at the same time and class. The preparation of the first-semester subject schedule for the 2021/2022 academic year at SMPN 2 Sungai Rumbai, Dharmasraya Regency is one of the problems in graph coloring, this problem will be solved using the Welch-Powell algorithm. This arrangement is done by representing the data in the form of a graph, so that neighboring vertices are obtained. Next, apply the Welch-Powell algorithm to the coloring of graph vertices to obtain chromatic numbers, which means that vertices with the same color can be scheduled together so that the arrangement of the subject schedule can be scheduled on the same day and class.
The Statistics of Interpretable Machine Learning

Statisticians, especially those who do applied work in the natural and social sciences, have long been interested in understanding model parameters and predictions. However, the distinct subfield of interpretable machine learning (iML), with its focus on general purpose explanations at varying degrees of resolution, is a much more recent development. In this chapter, I go beyond mere taxonomies and survey some of the most influential iML proposals at length. My goal in so doing is not to undertake the Sisyphean task of constructing a comprehensive review – numerous articles and at least one monograph have attempted this feat, only to be rendered obsolete within a few months – but rather to catalogue the assumptions and methods that characterise the most influential directions of research in iML. This overview is an essential step to ground future analysis, introducing a range of technical concepts and notation designed to enable broader discussion across disciplines. The examples from this chapter give a sense of the methodological breadth of iML techniques, which draw on literature in statistics, computer science, and game theory in the effort to make ML models and predictions more interpretable.
A Blockchain-based Framework for the Supply Chain Finance in The Electric-power Industry

In order to solve the issue of information asymmetry between commercial banks as well as small- and medium-sized enterprises in supply chain finance, and further reduce their financing cost, this paper proposes to use blockchain as a tool to promote data sharing for the supply chain finance in the electric-power industry. Under the scenario of power grid industry and the specific business of order financing, we design a theoretical model of financial data sharing in power grid industry chain based on multi-chain and consortium chain, combined with encryption algorithm, distributed database and policy-based access control method.