Reviewerly/exHarmony huggingface dataset:
THE EFFECT OF A ONE-ON-ONE DIALOGUE-BASED MATHEMATICAL INTELLIGENT TUTORING SYSTEM FOR LEARNING EQUIVALENT FRACTION

The purposes of this study are to develop a one-on-one dialogue-based mathematical intelligent tutoring system (ITS) for learning equivalent fraction in the 4th grade math, and evaluate its learning effect. The system used the course content and dialogue script designed by the math teaching experts in advance, and a computer agent teacher asked questions based on the course script. After the student answered, the system was able to identify the error pattern and misconception according to the student's response, then provided each student with adaptive teaching guidance or feedback. Students could construct correct equivalent fraction concepts through a series of interactive dialogues between students and the computer agent teacher step by step. In order to evaluate the effectiveness of this ITS, a quasi-experiment design was conducted. The pretest and post-test were parallel tests involving equivalent fraction. The participants of the study were 76 students in the fourth grade of two elementary schools chosen from midland of Taiwan. They were divided into the experimental group of 39 and the control group of 37. The experimental group used the "one-on-one dialogue-based mathematical intelligent tutoring system" for teaching. The control group used traditional classroom instruction by a human teacher. The learning content and time were controlled to be the same. Finally, the learning effectiveness and learning interest were assessed by comparing the pre-test and post-test performance of students. The results of the study showed that both teaching methods can significantly improve the students’ learning achievements of equivalent fraction, and the learning effectiveness of "one-on-one dialogue-based mathematical intelligent tutoring system" was significantly better than that of traditional classroom instruction. In the "one-on-one dialogue-based mathematical intelligent tutoring system" group, the learning improvement of students with different genders and different ability levels were also reaching a significant level. It indicated that this system benefited the learning achievements of students with different genders and different abilities. Furthermore, from the response data of the learning interest questionnaire, both teaching methods could significantly improve the learning interest of students. But there was no significant difference between the two teaching methods. By interviewing students, the probable causes included that low learning interest students of the experimental group also lack interest to familiar system operation, and some students think this ITS is not interesting enough because of lacking learning games.
Evaluation of IT Governance at Office X using the COBIT 5 Framework

The IT governance audit aims to determine the level of importance (maturity level) of a company or government agency. IT governance audit is essential because if governance is not implemented correctly, the harmony between IT is beneficial to support business processes, and standard procedures will not be achieved. One of the agencies that require the implementation of good IT governance is Office X in Badung Regency. Office X in Badung Regency has a function in developing ICT infrastructure through application development, public service content, and ICT networks to improve public services. Conditions that occur in the field are frequent errors caused by internal and external parties that negatively impact the data management of Office X. The audit process begins with observations in the agency environment related to activity data and implementation, then mapping with business goals according to COBIT 5, followed by mapping business goals with IT Goals to obtain IT processes. The obtained IT processes are then selected to obtain critical IT processes according to the Executive Office. The audit results show that four IT processes need to be given recommendations for improvement, namely MEA 01, EDM 01, APO 07, DSS02.
Local-enhanced Multi-resolution Representation Learning for Vehicle Re-identification

In real traffic scenarios, the changes of vehicle resolution that the camera captures tend to be relatively obvious considering the distances to the vehicle, different directions, and height of the camera. When the resolution difference exists between the probe and the gallery vehicle, the resolution mismatch will occur, which will seriously influence the performance of the vehicle re-identification (Re-ID). This problem is also known as multi-resolution vehicle Re-ID. An effective strategy is equivalent to utilize image super-resolution to handle the resolution gap. However, existing methods conduct super-resolution on global images instead of local representation of each image, leading to much more noisy information generated from the background and illumination variations. In our work, a local-enhanced multi-resolution representation learning (LMRL) is therefore proposed to address these problems by combining the training of local-enhanced super-resolution (LSR) module and local-guided contrastive learning (LCL) module. Specifically, we use a parsing network to parse a vehicle into four different parts to extract local-enhanced vehicle representation. And then, the LSR module, which consists of two auto-encoders that share parameters, transforms low-resolution images into high-resolution in both global and local branches. LCL module can learn discriminative vehicle representation by contrasting local representation between the high-resolution reconstructed image and the ground truth. We evaluate our approach on two public datasets that contain vehicle images at a wide range of resolutions, in which our approach shows significant superiority to the existing solution.
Novel Space Efficient Indices for Kannada Text: VKTPY Trie Family

V-KTPY Trie family is a group of space efficient indices designed to store and search Kannada text. The existing text searching and document fetching methods use different kinds of indices such as indices based on hashing, lexicographical indices and clustering technique based indexing. Each of these indexing methods have their own advantages including the optimal time complexity. However these indices are not space efficient. In this paper we are proposing a family of novel space efficient indices called V-KTPY Tries, which have the features of both lexicographical and hash based indexing. V-KTPY Tries are congruence of V-KTPY Rule (“Vistruta Katapayadi sutra”) and Prefix trees (Trie) , where the text labels of the Trie are encrypted by V-KTPY Rule. This powerful rule is an extension of an ancient “Katapayadi Sutra” (KTPY Rule) which can convert characters of Brahmi/Devanagari scripts to numbers. In this paper V-KTPY Tries are indexing V-KTPY encrypted Kannada text due to which compression is possible. The experiments are conducted on the family of V-KTPY Tries and their corresponding Tries with unicode Kannada. And the results show that the simple V-KTPY Trie gives 35% space efficiency; V-KTPY 10Ary Trie gives 65% space efficiency over simple unicode Trie with almost the same time complexity. The Prefix Hashed Trie is a fully compressed V-KTPY Trie which gives 20% space efficiency when compared to fully compressed unicode Trie. V-KTPY Tries can be used where Tries are applicable. The VKTPY prefix hashed Tries are used in Kannada feature selection. V-KTPY Tries can be extended to index many (120+) Indian languages which follow Brahmi or Devanagari script.
Imitating Emergencies: Generating Thermal Surveillance Fall Data Using Low-Cost Human-like Dolls

Outdoor fall detection, in the context of accidents, such as falling from heights or in water, is a research area that has not received as much attention as other automated surveillance areas. Gathering sufficient data for developing deep-learning models for such applications has also proven to be not a straight-forward task. Normally, footage of volunteer people falling is used for providing data, but that can be a complicated and dangerous process. In this paper, we propose an application for thermal images of a low-cost rubber doll falling in a harbor, for simulating real emergencies. We achieve thermal signatures similar to a human on different parts of the doll's body. The change of these thermal signatures over time is measured, and its stability is verified. We demonstrate that, even with the size and weight differences of the doll, the produced videos of falls have a similar motion and appearance to what is expected from real people. We show that the captured thermal doll data can be used for the real-world application of pedestrian detection by running the captured data through a state-of-the-art object detector trained on real people. An average confidence score of 0.730 is achieved, compared to a confidence score of 0.761 when using footage of real people falling. The captured fall sequences using the doll can be used as a substitute to sequences of people.
Design and Build a Facial Recognition Based Motor Vehicle Security System

The background of this research is to build a system that will be able to help reduce crime, especially not the crime of motor vehicle theft by using the concept of facial recognition on every motorized vehicle. Therefore, the owner will be recognized by the vehicle directly. The method used in this study is to use the method of literature study or literature review by using the basis of journals with the same research because this will find differences between journals and other journals so as to find novelty in journal writing. The problem raised in this research is how to reduce the rate of motor vehicle theft by using the system method or the internet of think method, therefore by making this system it will be able to help the police in reducing the crime of motor vehicle theft. The purpose of making this paper is how to create a system that can reduce motor vehicle crime, especially motor vehicle theft so that it can make motorists trust in leaving their motorized vehicle anywhere. Therefore, by making a facial recognition system on vehicles it will be able to reduce crime rates and can make motor vehicle drivers believe.
A Remote Attestation Mechanism Using a Threshold Ring Signature for a Perception Layer of Distributed Networking

Due to the emergence of various new technologies, Internet of things (IoT) is gradually becoming one of the most valued technologies at present. The IoT makes our life more and more convenient through the interconnection of everything, but the IoT brings many advantages and also raises some security issues, such as the IoT perception layer as the main means of sensing data delivery, due to the limited resources of sensing nodes and their vulnerability, the diversity of sensing data, and the heterogeneity of the sensing network, making the IoT perception layer more vulnerable to various malicious attacks. Therefore, guaranteeing the trustworthy source of sensing data is the cornerstone to guarantee the secure operation of the sensing layer. In this paper, we investigate the remote proof scheme applicable to the IoT perception layer and propose a remote attestation mechanism using a threshold ring signature for a perception layer of distributed networking, which realizes the trusted proof of a data source and thus can effectively discern the trusted status of the data source, and prove that the proposed scheme in this paper outperforms other remote proof schemes through efficiency analysis and verify the correctness as well as the effectiveness of the scheme in this paper.
Fault-Aware Machine Learning and Deep Learning-Based Algorithm for Cloud Architecture

The service-oriented computing paradigm is the mutual pool of varied computing, storage and network resources across the globe. The hardware, platform resources are assigned to cloud users using service availability measurement. The resources are accessed by the end-users based on pricing models. The resources are managed using data center network topologies which connect the host machines inside the data center. The identification of faults is a challenging issue in a modular cloud environment. Researchers have developed various methods for the fault-aware scheduling of cloud resources. Fault-aware resource allocation includes static, dynamic, meta-heuristic and learning-based approaches. In this chapter, we primarily focused on existing fault-aware resource allocation techniques and then we proposed a model that will primarily focus on fault forecast in tasks allocation. The projected model is based on a nature-inspired heuristic approach and intelligent artificial neural network. The fault-tolerant aware ANN-based proposed model focuses on performance improvement and reliability testing proactively. The proposed model surpasses the existing state-of-the-art methods for proactive and reactive fault-aware scheduling techniques in a large-scale data center. The results and discussions section supports the reliability assertion of the fault-tolerant aware human brain and nature-inspired model.
Structure-Aware Stabilization of Adversarial Robustness with Massive Contrastive Adversaries

Recent researches indicate that the impact of adversarial perturbations on deep learning models is reflected not only on the alteration of predicted labels but also on the distortion of data structure in the representation space. Significant improvement of the model’s adversarial robustness can be achieved by reforming the structure-aware representation distortion. Current methods generally utilize the one-to-one representation alignment or the triplet information between the positive and negative pairs. However, in this paper, we show that the representation structure of the natural and adversarial examples cannot be well and stably captured if we only focus on a localized range of contrastive examples. To achieve better and more stable adversarial robustness, we propose to adjust the adversarial distortion of representation structure by using Massive Contrastive Adversaries (MCA). Inspired by the Noise-Contrastive Estimation (NCE), MCA exploits the contrastive information by employing m negative instances. Compared with existing methods, our method recruits a much wider range of negative examples per update, so a better and more stable representation relationship between the natural and adversarial examples can be captured. Theoretical analysis shows that the proposed MCA inherently maximizes a lower bound of the mutual information (MI) between the representations of the natural and adversarial examples. Empirical experiments on benchmark datasets demonstrate that MCA can achieve better and more stable intra-class compactness and inter-class divergence, which further induces better adversarial robustness.
Innovative Education Is An Important Factor To Increase The Efficiency Of Economic Education

This article discusses the importance of innovative education for educational institutions today and the role of innovation. At the same time, it is shown that today they are able to increase the effectiveness of education through the use of innovative education. It is stated that meeting the requirements of today through the use of new innovative technologies in educational institutions imposes a great responsibility on teachers and educators of educational institutions.
PCA driven mixed filter pruning for efficient convNets

Deployment of the deep neural networks (DNNs) on resource-constrained devices is a challenging task due to their limited memory and computational power. In most cases, the pruning techniques do not prune the DNNs to full extent and redundancy still exists in these models. Considering this, a mixed filter pruning approach based on principal component analysis (PCA) and geometric median is presented. First, a pre-trained model is analyzed by using PCA to identify the important filters for every layer. These important filters are then used to reconstruct the network with a fewer number of layers and a fewer number of filters per layer. A new network with optimized structure is constructed and trained on the data. Secondly, the trained model is then analyzed using geometric median as a base. The redundant filters are identified and removed which results in further compression of the network. Finally, the pruned model is fine tuned to regain the accuracy. Experiments on CIFAR-10, CIFAR-100 and ILSVRC 2017 datasets show that the proposed mixed pruning approach is feasible and can compress the network to greater extent without any significant loss to accuracy. With VGG-16 on CIFAR-10, the number of operations and parameters are reduced to 18.56× and 3.33×, respectively, with almost 1% loss in the accuracy. The compression rate for AlexNet on CIFAR-10 dataset is 2.61× and 4.85× in terms of number of operations and number of parameters, respectively, with a gain of 1.2% in the accuracy. On CIFAR-100, VGG-19 is compressed by 16.02 X in terms of number of operations and 36× in terms of number of parameters with a 2.6% loss of accuracy. Similarly, the compression rate for VGG-19 network on ILSVRC 2017 dataset is 1.87× and 2.4× for operations and parameters with 0.5% loss in accuracy.
BlockTorrent: A Blockchain Enabled Privacy-Preserving Data Availability Protocol for Multi-stakeholder Scenarios

As industries across the globe continue to digitize their processes, the need for a mechanism to share private data between multiple stakeholders is becoming increasingly apparent. However, sharing data poses challenges around privacy and accessibility, particularly in the event of disputes between stakeholders with a shared interest, such as a supply chain. Auditors currently rely on stakeholders' compliance in order to verify data. Malicious parties may falsify the data before passing it onto the auditor. Using supply chains as a case study we present BlockTorrent, a protocol to address these challenges and help facilitate data sharing between supply chain participants. BlockTorrent allows participants to securely share their data in near real-time with other participants without the risk of information leakage or allowing the falsification of data, whilst guaranteeing data availability for auditors. This is achieved using a novel combination of distributed storage and on-chain secret sharing. This paper provides an implementation and evaluation of BlockTorrent, highlighting its performance and a security discussion. Lastly, we provide a discussion on the privacy challenges that were considered when designing BlockTorrent.
Game Edukasi Pengenalan Huruf Hiragana Untuk Meningkatkan Kemampuan Berbahasa Jepang

Educational games are very interesting to develop, because of the diversity that can be done. Some of the advantages of educational games compared to conventional educational methods, one of the main advantages of educational games is the visualization of real problems. Based on the pattern possessed by the game, players are required to learn so that they can solve existing problems. Game status, instructions, and tools provided by the game will guide players actively to explore information so that they can enrich their knowledge and strategies while playing. One of the interesting educational games to focus on is an educational game that discusses the introduction of foreign languages ​​focused on Japanese. With this educational game, it is hoped that it can help students to understand and memorize the hiragana letters in Japanese. This study aims to create a windows-based educational game application as an alternative medium for learning to recognize Japanese Hiragana letters. This game can later be used as an alternative learning media for the general public who just want to learn Japanese, by knowing the basics of Hiragana letters. The research method that can be used for this research can use the MDLC development method. The stages in this research process are Concept, Design, Material Collecting, Assembly, Testing, Distribution. The result of the research is an educational game of Japanese hiragana letters which introduces the basic Japanese hiragana letters and how to write them. In addition there is a quiz game of stacking letters into words to train children after knowing the basic letters of Japanese hiragana. Thus it can help children learn Japanese more easily. With this educational game, it is hoped that it can help children to understand and memorize the hiragana letters in Japanese
Compressing Interpretable Representations of Piecewise Linear Neural Networks using Neuro-Fuzzy Models

We present Rectified Linear Unit based Local Linear Model Tree (ReLUMoT). A model that bridges the gap between Piecewise Linear Neural Networks (PLNN) and Local Model Networks (LMN) like those resulting from the LoLiMoT algorithm. Essentially, we perform the input space partitioning of LoLiMoT by training a PLNN and extracting its linear regions. These become the input space partitions of ReLUMoT. From the perspective of PLNNs our approach compresses and smoothens low-dimensional models, while making them interpretable. From the perspective of LoLiMoT, our approach replaces the incremental and heuristic input space partitioning scheme with gradient-based training of a neural network, which is considerably more flexible.
Anonymous Sidechains

Sidechains allow two or more blockchains to communicate with each other by transferring coins (or other ledger assets) from one to the other. Their functionalities set sidechains as one of the most prominent solutions towards blockchain scalability and interoperability. A number of sidechain constructions have already been proposed on the literature presenting ways to securely move assets between blockchains for different types of underlying consensus mechanisms (PoW and PoS). In this work we study the problem of sidechains in the anonymous setting by demonstrating how multiple anonymous blockchains can interact with each other. We present the first formal definition for an anonymous sidechain and provide a first construction for privacy-preserving Zerocash [5] cross-ledger transactions.
Feature-based POS tagging and sentence relevance for news multi-document summarization in Bahasa Indonesia

Sentence extraction in news document summarization determines representative sentences primarily by employing the news feature known as news feature score (NeFS). NeFS can achieve meaningful sentences by analyzing the frequency and similarity of phrases while neglecting grammatical information and sentence relevance to the title. The presence of instructive content is indicated by grammatical information carried by part of speech (POS). POS tagging is the process of giving a meaningful tag to each term based on qualified data and even surrounding words. Sentence relevance to the title is intended to determine the sentence's level of connectivity to the title in terms of both word-based and meaning-based similarity, primarily for news documents in Bahasa Indonesia. In this study, we present an alternative sentence weighting method by incorporating news features, POS tagging, and sentence relevance to the title. Sentence extraction based on news features, POS tagging, and sentence relevance is introduced to extract the representative sentences. The experiment results on the 11 groups of Indonesian news documents are compared with the news features scores with the grammatical information approach method (NeFGIS). The proposed method achieved better results. The increasing f-score rate of ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-SU4 sequentially are 1.84%, 3.03%, 3.85%, 2.08%.