Reviewerly/exHarmony huggingface dataset:
Annotation-Free Character Detection in Historical Vietnamese Stele Images

Images of Historical Vietnamese stone engravings provide historians with a unique opportunity to study the past of the country. However, due to the large heterogeneity of thousands of images regarding both the text foreground and the stone background, it is difficult to use automatic document analysis methods for supporting manual examination, especially with a view to the labeling effort needed for training machine learning systems. In this paper, we present a method for finding the location of Chu Nom characters in the main text of the steles without the need of any human annotation. Using self-calibration, fully convolutional object detection methods trained on printed characters are successfully adapted to the handwritten image collection. The achieved detection results are promising for subsequent document analysis tasks, such as keyword spotting or transcription.
Investigating the Effect of VR + Haptics Approach on Students’ Flow Experience and Outcomes: An Empirical Study on VR Laparoscopy

Background. Virtual reality-based surgery training has become a promising trend in the sector of clinical education. Prior studies have confirmed the effectiveness of VR-based surgical simulators on training surgeons. Most existing papers employing subjective methods explored the students’ overall perceptions of surgical skills. However, few studies, from the multidimension perspective of learning performance, investigate how VR improves surgery skills. Participants. 37 college students were recruited in comparative experimental research. The experimental group was equipped with a VR + haptic surgical simulator, while the VR simulator without haptic feedback was used for the control group. Method. The study resorted to physiological approaches to investigate the influence of the VR laparoscopic surgical training system on students’ performance. Results. The experimental group scored higher than the control group in flow experience and has better performance in the four dimensions of operation evaluation skills. Conclusion. The study deposited that learners are more likely to exert to flow experience in a learning situation with haptic feedback, which will further improve medical students’ performance.
Quantum and classical dynamical semigroups of superchannels and semicausal channels

Quantum devices are subject to natural decay. We propose to study these decay processes as the Markovian evolution of quantum channels, which leads us to dynamical semigroups of superchannels. A superchannel is a linear map that maps quantum channels to quantum channels while satisfying suitable consistency relations. If the input and output quantum channels act on the same space, then we can consider dynamical semigroups of superchannels. No useful constructive characterization of the generators of such semigroups is known. We characterize these generators in two ways: First, we give an efficiently checkable criterion for whether a given map generates a dynamical semigroup of superchannels. Second, we identify a normal form for the generators of semigroups of quantum superchannels, analogous to the Gorini-Kossakowski-Lindblad-Sudarshan form in the case of quantum channels. To derive the normal form, we exploit the relation between superchannels and semicausal completely positive maps, reducing the problem to finding a normal form for the generators of semigroups of semicausal completely positive maps. We derive a normal for these generators using a novel technique, which applies also to infinite-dimensional systems. Our work paves the way for a thorough investigation of semigroups of superchannels: Numerical studies become feasible because admissible generators can now be explicitly generated and checked. Analytic properties of the corresponding evolution equations are now accessible via our normal form.
A Novel Transfer-Learning Model for Automatic Detection and Classification of Breast Cancer Based Deep CNN

Breast cancer (BC) is a leading cause of cancer death among women in which breast cells develop out of control is by encouraging patients to receive timely care, early detection of BC increases the likelihood of survival. In this context, a new deep learning (DL) model is presented for automatic detection and classification of the suspected area of the breast based on the transfer learning (TL) technique. A pre-trained visual geometry group (VGG)-19, VGG16, and InceptionV3 networks are used in the presented model to transfer their learning parameters for improving the performance of breast tumor classification. The main goals of this project are to use segmentation to automatically determine the affected breast tumor region, reduce training time, and improve classification performance. In the presented model, the Mammographic Image Analysis Society (MIAS) dataset is used for extracting the breast tumor features. We have chosen four evaluation metrics for evaluating the performance of the presented model accuracy, sensitivity, specificity, and area under the ROC curve (AUC). The experiments showed that transferring parameters from the model of VGG16 is a powerful for BC classification than VGG19 and Inception V3 with overall specificity, accuracy, sensitivity, and AUC 98%,96.8%, 96%, and 0.99, respectively.
Transforming Government banking by leveraging the potential of blockchain technology

Finance is the backbone of any Organization. Government is no different. The financial health of the Government is critically dependent on the efficiency with which its banking needs are managed. The Central Bank is generally the banker to the Governments. In India, Reserve Bank of India and Government have been in the forefront of implementing various reforms to strengthen and streamline the processes involved in tax collections and transfer payments. Despite these reforms, some issues still persist and are having detrimental effect on overall efficiency of Government banking. One important factor for existence of these issues is the fact that every government transaction has to pass through a long chain of stakeholders before it is accounted with finality. Blockchain technology is an emerging technology that promises to facilitate speedy and efficient collaboration among all the stakeholders and also facilitates disintermediation to the extent possible. Further, during the crisis like situations (floods, earthquakes or COVID 19 like pandemics), immediate relief is of utmost importance and government aid should reach the vulnerable/affected citizens at a very high speed. However, it is during these stress times that physical access to banking channel gets restricted. Blockchain can facilitate decentralization of last mile delivery channel, by enabling peer-to-peer and cashless transactions even among the unbanked population. This paper identifies the current issues in Government banking ecosystem, analyses the root causes behind such issues, introduces Blockchain based distributed ledger technology, examines the extent to which this emerging technology can address such issues and describes the way forward.
A zeroth order method for stochastic weakly convex optimization

Abstract In this paper, we consider stochastic weakly convex optimization problems, however without the existence of a stochastic subgradient oracle. We present a derivative free algorithm that uses a two point approximation for computing a gradient estimate of the smoothed function. We prove convergence at a similar rate as state of the art methods, however with a larger constant, and report some numerical results showing the effectiveness of the approach.
MRI super-resolution using 3D cycle-consistent generative adversarial network

High-resolution magnetic resonance imaging (MRI) provides detailed anatomical information critical for clinical application diagnosis. However, current MRIs are acquired at clinical resolutions due to the limit of physical, technological, and economic considerations. On the other hand, existing approaches require paired MRI images as training data, which are difficult to obtain on existing datasets when the alignment between high and low-resolution images has to be implemented manually.Within the scope of project, we aim to provide an end-to-end system to solve the super-resolution method on 3D MRI. Our proposed method derives from recent neural network developments and does not require paired data for efficient training. By integrating different models with separated functions, our 3D super-resolution CycleGAN (SRCycleGAN) achieved compelling results on MRI volumes. The output is close with ground-truth, showing a low distortion on different scaling factors. Besides, we also compare our method against different GAN-based methods in this field to highlight the performance.
Evaluating Study Design and Strategies for Mitigating the Impact of Hand Tracking Loss

Social virtual reality uses motion tracking to place people in virtual environments as animated avatars. Often this tracking only measures the position and orientation of the head and hands, and from this estimates the body pose. Optical hand tracking is an important technology to enable such avatars, but can frequently fail and cause motion errors when the hands are visually obscured. This paper presents three amelioration strategies to handle these errors and demonstrates experimentally that all three are effective in reducing their impact. This setting is also used to explore general issues around study design for motion perception. Different strategies for presenting stimuli and soliciting input are compared. The presence of a simultaneous recall task is shown to reduce but not eliminate sensitivity to motion errors. Finally, it is shown that motion errors are interpreted, at least in part, as a shift in interlocutor personality.
Multi-granularity for knowledge distillation

Considering the fact that students have different abilities to understand the knowledge imparted by teachers, a multi-granularity distillation mechanism is proposed for transferring more understandable knowledge for student networks. A multi-granularity self-analyzing module of the teacher network is designed, which enables the student network to learn knowledge from different teaching patterns. Furthermore, a stable excitation scheme is proposed to train the student under robust supervision. The proposed distillation mechanism can be embedded into different distillation frameworks, which are taken as baselines. Experiments show the mechanism improves the accuracy by 0.58% on average and by 1.08% in the best over the baselines, which makes its performance superior to the state-of-the-arts. It is also exploited that the student's ability of fine-tuning and robustness to noisy inputs can be improved via the proposed mechanism. The code is available at https://github.com/shaoeric/multi-granularity-distillation.
The Development of ITSM Research in Indonesia: A Systematic Literature Review

IT Service Management (ITSM) is a framework used to support businesses by increasing IT service quality. Several studies have tried to examine the development of ITSM based on their respective interests. However, the development of ITSM in Indonesia has not been widely studied, such as the types of research that are most often investigated, what domains are often researched, the areas and types of companies being studied. The things above are the main objectives of this research. The method used in capturing data, screening, and analysis is the systematic literature review method. There are many findings obtained from this research. One of them is the domination of the service operation research area (45%) among other areas. Meanwhile, applied research had been researched quite consistently over the last five years. From these results, it can be noticed that a deeper understanding of the synchronization between business and IT is needed. This is in accordance with the objectives of ITSM implementation so that future research is expected to provide balance in other areas, such as service strategy, design, transition, operation, and continuous service improvement.
Entangled Datasets for Quantum Machine Learning

High-quality, large-scale datasets have played a crucial role in the development and success of classical machine learning. Quantum Machine Learning (QML) is a new field that aims to use quantum computers for data analysis, with the hope of obtaining a quantum advantage of some sort. While most proposed QML architectures are benchmarked using classical datasets, there is still doubt whether QML on classical datasets will achieve such an advantage. In this work, we argue that one should instead employ quantum datasets composed of quantum states. For this purpose, we introduce the NTangled dataset composed of quantum states with different amounts and types of multipartite entanglement. We first show how a quantum neural network can be trained to generate the states in the NTangled dataset. Then, we use the NTangled dataset to benchmark QML models for supervised learning classification tasks. We also consider an alternative entanglement-based dataset, which is scalable and is composed of states prepared by quantum circuits with different depths. As a byproduct of our results, we introduce a novel method for generating multipartite entangled states, providing a use-case of quantum neural networks for quantum entanglement theory.
Translating synthetic natural language to database queries with a polyglot deep learning framework

Abstract The number of databases as well as their size and complexity is increasing. This creates a barrier to use especially for non-experts, who have to come to grips with the nature of the data, the way it has been represented in the database, and the specific query languages or user interfaces by which data are accessed. These difficulties worsen in research settings, where it is common to work with many different databases. One approach to improving this situation is to allow users to pose their queries in natural language. In this work we describe a machine learning framework, Polyglotter, that in a general way supports the mapping of natural language searches to database queries. Importantly, it does not require the creation of manually annotated data for training and therefore can be applied easily to multiple domains. The framework is polyglot in the sense that it supports multiple different database engines that are accessed with a variety of query languages, including SQL and Cypher. Furthermore Polyglotter supports multi-class queries. Good performance is achieved on both toy and real databases, as well as a human-annotated WikiSQL query set. Thus Polyglotter may help database maintainers make their resources more accessible.
A Digital Forensics Investigation of a Smart Scale IoT Ecosystem

The introduction of Internet of Things (IoT) ecosystems into personal homes and businesses prompts the idea that such ecosystems contain residual data, which can be used as digital evidence in court proceedings. However, the forensic examination of IoT ecosystems introduces a number of investigative problems for the digital forensics community. One of these problems is the limited availability of practical processes and techniques to guide the preservation and analysis of residual data from these ecosystems. Focusing on a detailed case study of the iHealth Smart Scale ecosystem, we present an empirical demonstration of practical techniques to recover residual data from different evidence sources within a smart scale ecosystem. We also document the artifacts that can be recovered from a smart scale ecosystem, which could inform a digital (forensic) investigation. The findings in this research provides a foundation for future studies regarding the development of processes and techniques suitable for extracting and examining residual data from IoT ecosystems.
Reading Interest Strength and Vocabulary Acquisition of EFL Learners: A Meta-Analysis

The purpose of this study was to examine the relationship between reading interest strength and vocabulary acquisition in English as a Foreign Language (EFL) learners through a systematic review and meta-analysis of eleven relevant studies conducted over the last decade, from 2010 to 2020. This study attended by the guidelines of PRISMA. Several stages conducted, such as; identifying the problem, collecting the data, screening, assessing the data, and extracting the data. The data was gathered from peer-reviewed journal articles indexed in databases such as Google Scholar, DOAJ, and Science Direct. Jamovi 1.6.7 was applied to analyze the data. This study aimed to report findings of a meta-analysis assessing correlation among reading interest and vocabulary acquisition. The result obtained was a relatively high relation in reading interest and vocabulary acquisition with pooled correlation = .485 (95% CI = .120 to - .850). The workings between variables were positive, which meant the students read more and increased vocabulary acquisition. Reading lots of text could improve students' vocabulary acquisition. The reading interest strength could develop vocabulary acquisition for EFL learners.
Quantum k-community detection: algorithm proposals and cross-architectural evaluation

Abstract Emerging quantum technologies represent a promising alternative for solving hard combinatorial problems in the post-Moore’s law era. For practical purposes, however, the current number of qubits limits the direct applicability to larger real-world instances in the near-term future. Therefore, a promising strategy to overcome this issue is represented by hybrid quantum classical algorithms which leverage classical as well as quantum devices. One prominent example of a hard computational problem is the community detection problem: a partition of a graph into distinct communities such that the ratio between intra-community and inter-community connectivity is maximized. In this paper, we explore the current potential of quantum annealing and gate-based quantum technologies to solve the community detection problem for an arbitrary number of communities. For this purpose, existing algorithms are (re-)implemented and new hybrid algorithms, that can be run on gate-model devices, are proposed. Their performance on standardized benchmark graphs has been evaluated and compared to the one of a state-of-the-art classical heuristic algorithm. Although no quantum speed-up has been achieved, the existing quantum annealing-based methods as well as the novel hybrid algorithms for gate-based quantum computers yield modularity values, which are similar to those of the classical heuristic. However, the modular architecture of the used algorithms allows for fast utilization of more powerful quantum technologies once they become available. Reproducibility: Our code and data are publicly available (Github in Quantum Modularization. https://github.com/jku-win se/quantum_modularization 2021).
Contrast threshold adaptive adjustment algorithm for remote sensing image matching

Remote sensing image processing has been widely used in environmental monitoring, terrain survey, military investigation, disaster early warning, and other fields. The remote sensing images matching is a key step, and the accuracy and real-time of the alignment have a great impact on these applications. Due to the high resolution and complexity of remote sensing images, scale-invariant feature transformation (SIFT) algorithm has the problems of high computational complexity and poor matching effect. Adaptive threshold adjustment algorithm has proposed in the remote sensing image matching, but minor changes in the contrast threshold can bring about drastic changes in the image matching quality, which will affect applications such as monitoring, measurement, and early warning. To improve the matching quality of a SIFT detector, an adaptive contrast threshold SIFT method based on image complexity calculation (CACT-SIFT) is proposed. The CACT-SIFT finds two contrast thresholds for the target image and the reference image, respectively, and achieves the target by minimizing the proposed criteria Cik. Experiments show that the method can be applied to the matching of remote sensing images. The information of reference images and target images can be detected at the same time, and the key points can be extracted in a robust way, with better accuracy and real-time accuracy of the alignment.
Static Analysis: An Introduction

Modern static-analysis tools provide powerful and specific insights into codebases. The Linux kernel team, for example, developed Coccinelle, a powerful tool for searching, analyzing, and rewriting C source code; because the Linux kernel contains more than 27 million lines of code, a static-analysis tool is essential both for finding bugs and for making automated changes across its many libraries and modules. Another tool targeted at the C family of languages is Clang scan-build, which comes with many useful analyses and provides an API for programmers to write their own analyses. Like so many things in computer science, the utility of static analysis is self-referential: To write reliable programs, we must also write programs for our programs. But this is no paradox. Static-analysis tools, complex though their theory and practice may be, are what will enable us, and engineers of the future, to overcome this challenge and yield the knowledge and insights that we practitioners deserve.
Towards Efficient Cryptographic Data Validation Service in Edge Computing

Edge computing brings data computation and storage closer to the mobile device to save response time for decision making. After being processed at the edge, commonly, the data will be uploaded to the cloud for further enriched analysis. For privacy concerns, local devices may encrypt the collected data before sending it to the cloud server. However, this treatment increases the servers processing time and makes it hard to pick out the desired data. In this paper, to address this problem, we design an encrypted data validation scheme, which enables the edge to clean the encrypted data to be uploaded. Because edge computing encompasses numerous edge devices from different service providers, we explore the public-key cryptographic mechanism to implement our secure data validation scheme. Considering potential risks from quantum computers, we propose to leverage ideal lattice to realize our protocol, which reaches better performance in both time and storage for edge devices. Extensive evaluation results show that our proposed proposal achieves considerable performance improvements in terms of communication and computation aspects. Particularly, compared to prior work, nearly 123x-238x speed up is achieved in our key derivation procedure and the storage cost of the secret key is reduced from 547MB to 1.7MB.