Reviewerly/exHarmony huggingface dataset:
A Hyper-Converged Video Management System Based on Object Storage

In recent years, online education, video surveillance industry and live streaming platform have encountered huge evolution. Massive video capture, storage, and access are facing increasing demands. But in large-scale video scenario, traditional video management systems encounter the challenges of reliability, extendibility and fault-tolerance. In this paper, a novel hyper-converged massive video management system based on object storage is designed and realized. The hyper-converged system composes distributed video capture, tiering video storage and read/write splitting load balance. In this loosely coupled system, all components are distributed on each node and communicate via message queues. The components can harness unused resources for example CPU, memory and network of hyper-converged nodes. This system exploits high extensibility and reliability of distributed system to satisfy large-scale video capture, storage and access.
The Connection Between Approximation, Depth Separation and Learnability in Neural Networks

Several recent works have shown separation results between deep neural networks, and hypothesis classes with inferior approximation capacity such as shallow networks or kernel classes. On the other hand, the fact that deep networks can efficiently express a target function does not mean that this target function can be learned efficiently by deep neural networks. In this work we study the intricate connection between learnability and approximation capacity. We show that learnability with deep networks of a target function depends on the ability of simpler classes to approximate the target. Specifically, we show that a necessary condition for a function to be learnable by gradient descent on deep neural networks is to be able to approximate the function, at least in a weak sense, with shallow neural networks. We also show that a class of functions can be learned by an efficient statistical query algorithm if and only if it can be approximated in a weak sense by some kernel class. We give several examples of functions which demonstrate depth separation, and conclude that they cannot be efficiently learned, even by a hypothesis class that can efficiently approximate them.
AG-SGD: Angle-Based Stochastic Gradient Descent

In the field of neural network, stochastic gradient descent is often employed as an effective method of accelerating the result's convergence.Generating the new gradient from the past gradient is a common method adopted by many existing optimization algorithms.Since the past gradient is not computed based on the most updated stochastic gradient descent state, it can introduce a deviation to the new gradient computation, negatively impacting its rate of convergence.To resolve this problem, we propose an algorithm that quantifies this deviation based on the angle between the past and the current gradients, which is then applied to calibrate these two gradients, generating a more accurate new gradient.To demonstrate the broad applicability of the algorithm, the proposed method is implemented into a neural network and a logistic regression classifier which are evaluated on the datasets MNIST and NSL-KDD, respectively.An in-depth analysis is performed to compare our algorithm with nine optimization algorithms in two experiments, demonstrating the advantages in the cost and the error rate reductions from adopting the proposed method.
Embedding manifold structures into Kalman filters.

Error-state Kalman filter is an elegant and effective filtering technique forrobotic systems operating on manifolds. To avoid the tedious and repetitivederivations for implementing an error-state Kalman filter for a certain system,this paper proposes a generic symbolic representation for error-state Kalmanfilters on manifolds. Utilizing the $\boxplus\backslash\boxminus$ operationsand further defining a $\oplus$ operation on the respective manifold, wepropose a canonical representation of the robotic system, which enables us toseparate the manifold structures from the system descriptions in each step ofthe Kalman filter, ultimately leading to a generic, symbolic andmanifold-embedding Kalman filter framework. This proposed Kalman filterframework can be used by only casting the system model into the canonical formwithout going through the cumbersome hand-derivation of the on-manifold Kalmanfilter. This is particularly useful when the robotic system is of highdimension. Furthermore, the manifold-embedding Kalman filter is implemented asa toolkit in $C$++, with which an user needs only to define the system, andcall the respective filter steps (e.g., propagation, update) according to theevents (e.g., reception of input, reception of measurement). The existingimplementation supports full iterated Kalman filtering for systems on manifold$\mathcal{S} = \mathbb{R}^m \times SO(3) \times \cdots \times SO(3) \times\mathbb{S}^2 \times \cdots \times \mathbb{S}^2 $ or any of its sub-manifolds,and is extendable to other types of manifold when necessary. The proposedsymbolic Kalman filter and the developed toolkit are verified by implementing atightly-coupled lidar-inertial navigation system. Results show superiorfiltering performances and computation efficiency comparable to hand-engineeredcounterparts. Finally, the toolkit is opened sourced athttps://github.com/hku-mars/IKFoM.
Implications of Covid-19 on the management of school financial resources in quintile 5 public schools

With the promulgation of the South African Schools Act, public education in South Africa was decentralised and communities were made responsible for the governance of public schools. White Paper 1 on Education and Training confirmed the inability of the state to meet the financial requirements of public education. Despite the fact that school governing bodies are responsible for raising substantial funding, their ability to appropriate school funds is limited by legislation, irrespective of the origin of the funds or assets in question. These restrictions have a substantial impact on the way financing is structured and managed by public schools. In 2020 schools were closed for more than 2 months due to the Covid-19 lockdown, and many parents were left questioning why they should pay for services not rendered. Using a qualitative research approach, we aimed to determine the impact of Covid-19 on the management of school fees and resources in public schools. The findings reveal that Covid-19 has had an impact on school budgets, teaching posts and fundraising activities, as well as on the day-to-day running of schools.
Stakeholder Views on Early Literacy and Reading and Writing Acquisition in the Preschool Period

This research aims to reveal stakeholder views on early literacy and reading and writing acquisition in the preschool period. It is a case study conducted with the descriptive survey model. The research participants consisted of academics, primary school teachers, early childhood education teachers, primary school teaching students, and prospective early childhood education students. Five semi-structured interview forms were used as the data collection tool. The data were analyzed through content analysis method. The results indicate that stakeholders have a flexible approach to teaching reading and writing in the preschool period and support early literacy education. The roles that early childhood education teachers, preschool education institutions, and parents should play, are presented. It is recommended to increase the cooperation of stakeholders having an essential role in literacy skills development and to reshape the pre-service and in-service training related to early literacy.
Embracing Differential Privacy for Anonymizing Spontaneous ADE Reporting Data

Spontaneous ADE (Adverse Drug Event) reporting system, a widely established system for collecting adverse event of drug reaction, is a kind of medical data repository containing personal information, such as sex, age, weight, disease and drug information. These data have to be anonymized to protect patient’s privacy before being released to the professionals for analysis. We previously have proposed a privacy model MS(k, $\theta^{\ast}$)-bounding for ADE reporting data, a kind of syntactic anonymity model. One of its weakness is the requirement of background knowledge available to the attacker. This model becomes awkward when full knowledge of the background information held by the attacker is impossible. Differential privacy is an emerging anonymization technique with rigorous theoretical foundation and recognized as a replacement of syntactic anonymity models. It can provide effective privacy protection without considering background knowledge available to the attacker at the cost of causing serious data distortion. This makes its hardly applicable to anonymize medical data. This research endeavored to fuse both advantages of syntactic anonymity and differential privacy. We combine MS(k, $\theta^{\ast}$)-bounding with differential privacy, proposing a new privacy framework MSDP(k, $\theta^{\ast}$, $\varepsilon$)-bounding and the corresponding anonymization algorithm. Empirical evaluation on the FAERS data show that in comparison with MS-bounding and two contemporary differential privacy methods, our proposed method achieves better trade-off between protecting personal privacy and reducing data distortion, while maintaining the same level of strength of the ADR signal as done by MS-bounding.
Learning-State-Estimation Method Using Browsing History and Electroencephalogram During Programming Language Learning and Its Evaluation

The failure of learners to obtain sufficient knowledge is caused by various factors, such as the difficulty level and quality of the learning materials and learner’s prior knowledge. The use of the learner’s learning log and biological information, such as the brain waves, heart rate, and eye movements during learning, makes it possible to detect the factors. If different brain waves can be measured according to the difficulty level of task execution, the difficulty level of e-learning materials can be adjusted so that the optimum learning effect can be obtained for each student. In this study, a system that obtains the learning logs during learning has been proposed. However, the learning time is insufficient to understand the learning state of the learners. For example, if the learning time is short, whether the learning materials were too easy or too difficult to abandon cannot be determined. Therefore, we propose a system and a method for estimating the learning state of the learners by comprehensively analyzing his/her learning history and brain wave. Moreover, we evaluate the learning state of high school students learning the C and Scratch programming languages using the proposed method. Also, by comparing the estimated results with those obtained from the questionnaire administered after the experiments, we evaluate the effectiveness of our proposed method.
Provable Defense Against Delusive Poisoning

Delusive poisoning is a special kind of attack to obstruct learning, where the learning performance could be significantly deteriorated by only manipulating (even slightly) the features of correctly labeled training examples. By formalizing this malicious attack as finding the worst-case distribution shift at training time within a specific $\infty$-Wasserstein ball, we show that minimizing adversarial risk on the poison data is equivalent to optimizing an upper bound of natural risk on the original data. This implies that adversarial training can be a principled defense method against delusive poisoning. To further understand the internal mechanism of the defense, we disclose that adversarial training can resist the training distribution shift by preventing the learner from overly relying on non-robust features in a natural setting. Finally, we complement our theoretical findings with a set of experiments on popular benchmark datasets, which shows that the defense withstands six different practical attacks. Both theoretical and empirical results vote for adversarial training when confronted with delusive poisoning.
Review and Analysis of Cryptography Techniques

Recently, there has been a significant increase in the number of Internet users. Thus, digital data has occupied the highest rank in our time, because most companies and governments adopt the total dependence for the transmission of their data transactions with customers and banking transactions over the World Wide Web. Therefore, total reliance on digital data needs to secure this data and preserve it from theft and modification in order to reach the intended user intact without distortion, and this is done by securing this data using encryption with its various techniques. There are many technologies that encrypt the data sent to the receiving party called encryption techniques. This research will examine the analysis of different encryption techniques, for example, symmetric cryptography technique and asymmetric cryptography techniques, Rivest Shamir and Adleman, Diffie-Hellman, Digital Signature Algorithm, (ECC) Elliptic curve cryptography, and how can employ them and explore the best methods of cryptography techniques that give complete confidential and transmitted data.
Infrared Image Recognition Technology Based on Visual Processing and Deep Learning

Infrared image recognition in substation is always a difficult problem. In order to solve the problem of recognition of knife gate, insulator and other components in infrared image, the target location technology of deep learning is proposed to realize the detection and recognition of typical components in infrared image, and the multi-target detection algorithm YOLO is selected to locate and identify the defects. Firstly, the image preprocessing technology is used to process the collected image, so as to filter the interference of background and other factors on the equipment identification. Then, the infrared image is detected by the YOLO target detection model based on multi feature fusion, so as to locate the position of inspection equipment in the infrared image. Then, the type of equipment is identified by the trained equipment classification model. Finally, the algorithm is tested with a large number of pictures in the substation scene.
Simulation of implementable quantum-assisted genetic algorithm

Abstract Quantum-assisted algorithms are expected to improve the computing performance of classical computers. A quantum genetic algorithm utilizes the advantages of quantum computation by combining the truncation selection in a classical genetic algorithm with the quantum Grover’s algorithm. The parallelism of evaluation can create global search and reduce the need of crossover and mutation in a conventional genetic algorithm. In this work, we aim to demonstrate and simulate the performance of an implementable quantum-assisted genetic algorithm. The algorithm was tested by using quadratic unconstrained binary optimization (QUBO) for 100 iterations; and the results were compared with those from a classical counterpart for 2000 iterations, where both simulations were performed over 100 repetitions. The results showed that the quantum algorithm converges to the optimal solution faster. While the variance is higher at early stage, it quickly and greatly reduces as the algorithm converges. The histograms of possible solutions consistently exhibits this behavior.
Design and Usability Methods

User-centered design and evaluation methods are used to create innovative solutions for human performance, including patient work. We present Agile Innovation as a practical process for “innovation with user input,” which is based on user-centered design best practices and insights from behavioral economics and complex adaptive systems theory. The eight steps of Agile Innovation are (1) confirm demand; (2) study the problem; (3) scan for solutions; (4) plan for termination and evaluation; (5) ideate and select; (6) run innovation sprints; (7) validate solutions; and (8) package for launch. Furthermore, we describe lessons learned—including challenges and recommendations—from applying and adapting user-centered design and evaluation with patient and family stakeholders. A case study illustrates design and evaluation methods we used in a recent project to create technology to support older adults with chronic heart failure.
On Self-Supervised Image Representations for GAN Evaluation

The embeddings from CNNs pretrained on Imagenet classification are de-facto standard image representations for assessing GANs via FID, Precision and Recall measures. Despite broad previous criticism of their usage for non-Imagenet domains, these embeddings are still the top choice in most of the GAN literature. In this paper, we advocate the usage of the state-of-the-art self-supervised representations to evaluate GANs on the established non-Imagenet benchmarks. These representations, typically obtained via contrastive learning, are shown to provide better transfer to new tasks and domains, therefore, can serve as more universal embeddings of natural images. With extensive comparison of the recent GANs on the common datasets, we show that self-supervised representations produce a more reasonable ranking of models in terms of FID/Precision/Recall, while the ranking with classification-pretrained embeddings often can be misleading.
An autonomous vision-based shelf-reader robot using faster R-CNN

Purpose The purpose of this study is to design, program and implement an intelligent robot for shelf-reading. an essential task in library maintenance is shelf-reading, which refers to the process of checking the disciplines of books based on their call numbers to ensure that they are correctly shelved. Shelf-reading is a routine yet challenging task for librarians, as it involves controlling call numbers on the scale of thousands of books promptly. Design/methodology/approach Leveraging the strength of autonomous robots in handling repetitive tasks, this paper introduces a novel vision-based shelf-reader robot, called \emph{Pars} and demonstrate its effectiveness in accomplishing shelf-reading tasks. Also, this paper proposes a novel supervised approach to power the vision system of \emph{Pars}, allowing it to handle motion blur on images captured while it moves. An approach based on Faster R-CNN is also incorporated into the vision system, allowing the robot to efficiently detect the region of interest for retrieving a book’s information. Findings This paper evaluated the robot’s performance in a library with $120,000 books and discovered problems such as missing and misplaced books. Besides, this paper introduces a new challenging data set of blurred barcodes free publicly available for similar research studies. Originality/value The robot is equipped with six parallel cameras, which enable it to check books and decide moving paths. Through its vision-based system, it is also capable of routing and tracking paths between bookcases in a library and it can also turn around bends. Moreover, \emph{Pars} addresses the blurred barcodes, which may appear because of its motion.
Applied of feed-forward neural network and facebook prophet model for train passengers forecasting

Abstract Train is public transportation that is widely used by people in Indonesia. Due to the high level of comfort at a low price relatively. Based on released data by PT KAI, the number of train passengers has increased in almost every holiday season, thereby, it is suspected that there are seasonal patterns with fixed and random periods. In 2013, the government issued a policy related to infrastructure development, then, it caused the number of passengers significantly increasing in the following year. Thus, we need a model that can accommodate these patterns to forecast the number of train passengers accurately. The use of neural network methods such as Feed Forward Neural Network (FFNN), nowadays, becomes popular in facing big data including unexpected fluctuation on the data. Additionally, recently, Facebook announced an accurate method of forecasting, called Prophet model, for data which have trend, seasonality, holidays, and missing data. Hence, the forecast for monthly train passengers on this research is modelled by FFNN and Facebook Prophet. The result shows that Prophet performs better than FFNN. However, the difference in the value of MAPE is not too large.
Understanding the Rise of Twitter-Based Cyberbullying Due to COVID-19 through Comprehensive Statistical Evaluation

The COVID-19 pandemic has created a challenging situation for everyone, sparking digital evolution due to stay-at-home restrictions to stop the spread. This has led to an uprise of digital presence, which many hypothesize has lead to a rise of cybersecurity attacks, including cyberbullying. To evaluate the significance of COVID-19 on cyberbullying reports, we collected 454, 046 of publicly available tweets from Twitter by using MongoDB and Python libraries from January 1st, 2020–June 7th, 2020. We performed statistical analyses on the collected sample set to understand the situation from a quantitative perspective. We extracted tweets related to 27 unique keywords specific to cyberbullying, including online bullying, cyberbullying, Twitter bullying, and others. Due to the time-series’ count nature, we propose a Bayesian estimation of this count data trends utilizing an autoregressive Poisson model. A simple change-point model fails to explain the subtle changes adequately. On the other hand, our Bayesian method clearly shows the upward trend beginning in mid-March, which is reportedly the time from which the stay-at-home orders were widespread globally. The pattern remains similar if we focus on one or more such keywords instead of the total count. We also provide a fine-grained lag based analysis of our model and contrast our methods with an alternative semi-Bayesian AR-ARCH model. Overall, such analysis shows somewhat conclusive evidence of the rise around the same time as COVID.
3-D Quasi-Conformal Mappings and Grid Generation

The difficulties of creating three-dimensional (3-D) analogs of conformal mappings are related to the topological and analytical features of 3-D space. The most interesting quasi-conformal mappings of 3-D regions are obtained with help of hydrodynamic analogy. For the steady irrotational flow of incompressible inviscid fluid together with the potential of velocities, two streamline functions are introduced. Any solenoid vector can be represented as a vector product of the gradients of two streamline functions. We obtain the connection of the velocity components with the streamline functions. These transformations are the basis for Lavrentiev's type of harmonic mappings. On the other hand, these conditions can be considered as a generalization of the Cauchy-Riemann conditions in the 3-D case. As shown in our work, generalized 3-D Cauchy-Riemann conditions for harmonic mappings are reduced to Cauchy-Riemann conditions for two functions of a usual complex variable. An analog of 3-D quasi-conformal mappings is obtained as combination of two ordinary functions of a plane complex variable. Examples of grid generation obtained by the theory of 3-D quasi-conformal mappings are given. The best proof of these results is their visualization.
Identifying Relevant Data Center Telemetry Using Change Point Detection

In this paper, we focus on the problem of data center performance monitoring, more specifically, how to manage the large volume of data generated by data center telemetry tools. We propose a framework that uses Change Point Detection (CPD) to identify sources of useful telemetry and based on that information, filters incoming telemetry data in real-time as the data center operates. To evaluate our proposed CPD-based telemetry triage framework, we conducted experiments using a small emulated data center driven by different workloads. We also report results from experiments with telemetry data collected from a privately-owned, commercial, multi-tenant data center. Preliminary experimental results show that our CPD-based tool can filter out significant amounts of irrelevant telemetry while preserving most relevant telemetry sources.