Reviewerly/exHarmony huggingface dataset:
Stronger convergence results for deep residual networks: network width scales linearly with training data size

Abstract Deep neural networks are highly expressive machine learning models with the ability to interpolate arbitrary datasets. Deep nets are typically optimized via first-order methods, and the optimization process crucially depends on the characteristics of the network as well as the dataset. This work sheds light on the relation between the network size and the properties of the dataset with an emphasis on deep residual networks (ResNets). Our contribution is that if the network Jacobian is full rank, gradient descent for the quadratic loss and smooth activation converges to the global minima even if the network width $m$ of the ResNet scales linearly with the sample size $n$ and logarithmically with the network depth $H$. Consequently, our work is able to provide a theoretical guarantee for the convergence of deep neural networks in the $m=\varOmega (n,\log H)$ regime.
São Paulo SmartViz Traffic-an information visualization tool

Information and communication technologies (ICTs) have transformed several aspects of modern society, changing how knowledge is transmitted and how the challenges faced by large cities can be addressed. Thus, based on the technological development of the Internet of Things (IoT) focused on the concept of smart cities, this paper aims to describe and model some relevant factors related to traffic accidents, and promote the visualization of their impact on the bus routes of São Paulo city. The time window considered was February 2016, 2017, and 2018. By means of information visualization techniques, we describe the location of accidents across São Paulo region and later associate important factors (such as climate and radar) to the occurrence of traffic jams. Subsequently, a spatial study is conducted to visualize the bus routes and observe possible transit bottlenecks. As result, we deploy a visualization tool named SmartViz Traffic to emphasize the importance of monitoring accidents, aiming prevention and road safety, as well to inform the traffic dynamic in São Paulo city.
Towards intelligent control via genetic programming

In this paper an initial approach to Intelligent Control (IC) using Genetic Programming (GP) for access to space applications is presented. GP can be employed successfully to design a controller even for complex systems, where classical controllers fail because of the high nonlinearity of the systems. The main property of GP, that is its ability to autonomously create explicit mathematical equations starting from a very poor knowledge of the considered plant, or just data, can be exploited for a vast range of applications. Here, GP has been used to design the control law in an Intelligent Control framework for a modified version of the Goddard Rocket problem in 3 different failure scenarios, where the approach to IC consists in an online re-evaluation of the control law using GP when a considerably big change in the environment or in the plant happens. The presented results are then used to highlight the potential benefits of the method, as well as aspects that will need further developments.
Expression of cooperative motions in team sports based on the motion data of each player

In this paper, we propose a method of generating action description and detecting certain combination plays observed in volleyball games. Motions are represented as motion symbols with lower dimensions, and positions of the ball at each time are obtained. These motion symbols and ball positions are used to detect and describe motions. In detecting combination plays, motion pattern at each time and each players are compared with reference motion patterns selected by hand, and similar motion patterns are detected. In generating motion descriptions, motion symbols and ball positions are used as the initial state of Gated Recurrent Unit, and it learns to generate motion descriptions. This method was tested using motion data obtained from real volleyball match.
The Accommodation Theory of Reference

Theories of reference are important in lots of disciplines. In philosophy they’re important in discussions of, e.g., mental representation, modal logic, analytic metaphysics, philosophical theology, and metaethics. Realists maintain, against logical empiricists, that knowledge of unobservable ‘theoretical’ entities is possible. A key point of all realist causal theories of reference has been to underwrite that idea by explaining how scientific terms can sometimes continue to refer univocally even in the face of changes in concepts, ‘operational definitions’, framework principles, etc. Accommodationism is designed to fulfill those requirements. The accommodationist conception for human languages treats natural kinds and their naturalness as features of humans’ communicative and inferential practices. The term ‘reference fixing’ is misleading: reference is really a dialectical process. Partial denotation and related phenomena are commonplace in the sciences and other intellectual disciplines.
Understanding Non-native English Speakers' Perceptions of Voice User Interfaces with and without a Visual Display: A Usability Study

In this study, we conducted a usability evaluation of the Google Home Smart Speaker (without a visual display) and Lenovo Smart Display (with an attached screen) with 34 non-native English speakers. The findings show that both commercial VUIs are user-friendly, easy-to-use, and useful for non-native English speakers regardless of their English language proficiency. However, the study points out that both VUIs still need improvements and enhancements in usability particularly for non-native English speakers. Although there is no usability difference between these devices, the findings show that most of the participants prefer to use VUIs with an attached display that can be an advantage, particularly for non-native English speakers. The usability recommendations and findings in this study can be insightful into the future study in VUI for diverse user groups.
A Template-Based Approach for Generating Vietnamese References from Flat MR Dataset in Restaurant Domain

In recent years, researchers in natural language generation (NLG) focus on corpus-based systems on specific or across domains. The training data should consist of meaning representations (MRs) paired with Natural Language (NL) references. In the first content of the article, we introduce a Vietnamese Flat MR dataset which is the first Vietnamese dataset for training end-to-end, data-driven NLG systems in restaurant domain. We establish a method of generating references on this dataset. The core of the method are two important stages: (i) sentence planning which determine semantic template of the output text; (ii) surface realization which selecting appropriate Vietnamese phrases to replace the corresponding predicates (slot-value) of the Flat MR in the semantic template. The evaluation results show that the dataset and proposed generating method have contributed well to the development of the NLG research direction.
Comparison of Supervised Machine Learning Models for Categorizing E-Commerce Product Titles in Myanmar Text

Although the number of online businesses is increasing in Myanmar during recent years, the number of e-commerce sites in Myanmar language is still very low. The reason is that e-commerce sites are growing rapidly and methods like automatic product categorization are needed for better experience but there is no off-the-shelf system for Myanmar text till now. Research in this area is limited by the lack of real world datasets. In this paper, an e-commerce product corpus was developed and different supervised machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB) and Logistic Regression (LR) were evaluated for classification of product titles in Myanmar Language. Over 300,000 product titles were scraped from a Myanmar e-commerce site and classified into 15 predefined categories. The experimental results show that Support Vector Machine gets the best accuracy result compared to other supervised machine learning algorithms in this paper.
Meningkatkan Kemampuan Literasi Informasi Bagi Pustakawan melalui Kegiatan Pelatihan

Based on the results of monitoring of 8 (eight) national education standards, the school library so far has not been in accordance with the specified standards. There are several weaknesses in the competence of school librarians concerning; understanding information literacy and digital literacy, electronic information sources, information retrieval strategies, ethical and legal evaluation and presentation of information, and plagiarism. Departing from this reality, this action research seeks to improve digital literacy skills for librarians in schools. This research is a school action research. The research procedure was carried out using a Spiral Model developed by Kemmis a Taggart. The research flow consists of four main activities, namely planning, implementation, observation, and reflection. Based on the results of research and discussion it can be concluded that the ability of librarians in information literacy can be improved through training activities. In cycle I, the librarian's skills in information literacy were 25% good, and 75% sufficient. While in cycle II, the librarian's information literacy ability is 100% in the good category.
Expansion or Disruption: Research on the Artistry of VR Films

VR film is a new type of film that applies virtual reality technology in the viewing process. On the one hand, it has complete story elements, and on the other hand, it has more immersive viewing experience than traditional films. This research is divided into two levels. The first level: in the interaction between VR films and traditional films, will VR films become the future form of films or exist as a film type? Second layer: In the process of impact and absorption of traditional film language, what new characteristics will VR film itself produce in the two dimensions of film perception and film language? The development of film forms and techniques has always been closely related to the progress of technology. This paper will start with the theory of film perception and the theory of film psychoanalysis, and take the mature modern film language as the comparative object to complete the above two levels of research.
Development of a three-dimensional virtual monitoring system for a workshop

Abstract Aiming at the problems of poor applicability and development efficiency of the current manufacturing workshop monitoring system, a workshop monitoring system using the industrial Internet of Things platform as the system service platform is proposed. This article takes a seal automated production line as an example to illustrate the development process of a workshop monitoring system, including virtual scene construction, data acquisition and real-time model driving based on the industrial Internet of Things platform. The results show that the 3D virtual monitoring system has good real-time performance and realizes multi-level and visual monitoring. It has certain reference value for the development of the workshop 3D virtual monitoring system.
Label360: An Annotation Interface for Labeling Instance-Aware Semantic Labels on Panoramic Full Images

We developed an annotation tool—Label360 to solve the distortion and instance matching issues across different viewing aspects in spherical image annotations. A post-processing algorithm was introduced to generate distortion-free annotations on equirectangular images. Two experiments were conducted to examine the consistency of annotations using Label360 and to compare labeling efficiency with LabelMe. Our tool obtained a mean intersection over union (mIoU) of 0.92 in the consistency test and has 1.45x the annotation speed of LabelMe. This demonstrates that Label360 is efficient for annotating instance-aware semantic segmentation labels on spherical images.
Computing and Analysis of Residue Matrices over Complex Plane for Cryptographic Applications

This paper studies the integration of modular arithmetic, matrix algebra and complex number arithmetic. It describes implementation strategies of matrix transformations for residue matrices over complex plane that can be applied in the computer science fields which deal with cryptographic applications for more secure. Their mathematical properties over complex plane are analyzed to apply in cryptographic matrix transformation techniques. A new implementation approach to Hill cipher is proposed using the residue matrices over complex plane.
Reversible Information Hiding in Ciphertext of Any Digital Media

Reversible data hiding in encrypted media (RDH-EM) plays a dual role in information security and data processing, especially in cloud services. Without accessing the visual or auditory content of digital media, additional information can be hidden in encrypted data. The recipient can not only extract the information but also recover the media losslessly while maintaining confidentiality. Most existing RDH-EM techniques focus on images as host carrier, which cannot be extended to other types of digital media; Some techniques are bound only to specific encryption algorithms. In this paper, we propose a universal RDH-EM technique independent of the types of media and encryption algorithm. The payload of data hiding is as high as the amount of host ciphertext data. Although data hiding is associated with the ciphertext data of host media, it does not affect the recovery of original media at all. Experimental results confirm the performance superiority of the proposed technique.
A Real-time Face Tracking and Recognition System Based on SeetaFace

Abstract A real-time face tracking and recognition system is constructed based on Seetaface, and it is proposed to use Gamma correction to reduce the impact of illumination changes on the detection and recognition results. First, the system has face detection function, then by using OpenCV to get the image in front of the camera, the system completes the function of real-time face tracking, users can also upload local images to make face comparison. If a stranger’s face is detected, the system can collect information and train it. The system achieves multiple face tracking as well as recognition. Through the analysis and evaluation on public datasets. The improved face tracking and recognition system has a satisfactory accuracy and performance.
PLAM

In recent years, with the development of embedded software, the demand for context-aware software and the need for Context-Oriented Programming (COP) frameworks is increasing. In general, within the COP framework, each method is managed in units of layers, and time is required to activate the layers following context changes. Compared to the traditional single-thread method, the use of multi-threading to control the layer activation doubles the system efficiency. Moreover, embedded software usually includes mechanisms for exception handling, such as communication disconnection, mechanical or hardware failures, and insufficient energy. These mechanisms are expected to be types of high-priority operations, which also require multi-threading to achieve preemptive layer activation. In this study, we propose an architecture for preemptive layer activation known as PLAM by using multi-threading, which is based on the RTCOP framework. Using PLAM, the non-exception handling layers can be activated simultaneously, and when the context of the exception handling layer is perceived, the program can call a certain function to suspend or terminate the activation process of the other layers. Finally, we package a series of functions as APIs so that users can use these to customize the context listener. A test application confirms the feasibility of the proposed approach.
Spam and Fake Spam Message Detection Framework Using Machine Learning Algorithm

Online reviews about the acquisition of items or administrations gave have become the primary wellspring of clients’ conclusions. So as to pick up benefit or acclaim, as a rule spam reviews are composed to advance or downgrade a couple of target items or administrations. This training is known as review spamming. In the previous barely any years, an assortment of strategies have been proposed so as to illuminate the issue of spam reviews. It is a mainstream correspondence and furthermore known as information trade media. Information could be of a book, numbers, figures or insights that are gotten to by a PC. These days, numerous individuals relies upon substance accessible in web-based social networking in their choices. Sharing of data with people groups has additionally pulled in social spammers to endeavor and spread spam messages to advance individual web logs, notices, advancements, phishing, trick, fakes, etc. The possibility that anyone will leave a review give a brilliant possibility for spammers to post spit audit with respect to item and administrations for different interests and possibilities. In this way, we propose a fake message detection system utilizing ML to recognize the spam and fake messages on the internet based life stage.
Measuring how much an object has a ring-like shape

Abstract An image-based algorithmic tool for measuring how much an object’s shape looks like a ring is presented. The objects whose shapes have such a structure appear in nature very often, e.g. in nanotechnology, biology, medicine, industry, etc. The method proposed has several formal desirable properties, including invariance ones. In addition, the method is easy to implement and is robust (e.g. with respect to noise).
A Novel Approach for Encrypted Data-Deduplication in Clouds

Data deduplication is one of the main techniques for copying recovery data duplicates and was widely used in distributed storage to minimize extra space and spare data transfer capacity. It was proposed that the simultaneous encryption method encode the data before re-appropriating to preserve the confidentiality of delicate data while facilitating de replication. Unlike conventional de duplication systems, consumers are therefore viewed as having differential advantages as indupli-cate tests other than the data itself. Security analysis shows that our approach is safe in terms of the values set out in the proposed security model. For this deduplication M3 encryption algorithm and DES algorithm are used. M3 encryption is to compare another with the latest technology, for more effective, security purposes, fast actions and. The second DES encryption that was used to open the file and decrypt understandable language for humans in a secure language. A model of our current accepted copy check program is revised as proof of concept by the current research and explicitly shows the tests using our model. The proposed research shows that when opposed to conventional operations, our proposed duplicate test plot creates marginal overhead.
A framework for Sudanese Arabic – English Mixed Speech Processing

Using more than language in a single discourse is a practical phenomenon appears in bilingual and multilingual communities known as mixed speech communication. The mixed speech occurs in communication to express ideas and thoughts using vocabulary of all used languages. This paper addresses the problem of automatic speech recognition and language identification for Sudanese Arabic and English languages in mixed speech mode, in which number and location of languages are not previously known. Native language in mixed speech is dominant based on assumption that native speaker does not suddenly reconfigure his articulation organs to produce sounds when switching to other language. This study proposes a generalized framework of Automatic Speech Recognin in Mixed Speech mode (ASR-MS). Proposed framework defines mixed speech as a hybrid language not belong to any language participates in mixed sentence. This new language is processed based on its distinct attributes not based on attributes of languages originated from. A single Language Model (AM) is built based on Sudanese Arabic pronunciation. The supporting components such as phonetic dictionary and language lexicon are bilingual, keeps words in their original forms. For measuring the soundness of a framework, a 100 Sudanese Arabic - English daily life mixed sentences were collected and recorded. Preliminary results are encouraging towards building a generalized mixed speech recognition and language identification based on speakers instead of languages.
Enhanced Privacy with Blockchain-based Storage for Data Sharing

The core concept of electronic money is blockchain, which can be regarded as a decentralized database. It is a decentralized storage service that does not rely on third-party storage. It records each transaction information on the block. Related applications include electronic ledgers, which further extend smart contracts. In storage, not only record transactions, but the extended smart contract can be used in medical treatment, recording the patient's medical records. However, there is a need for privacy in medical records, and it must be able to be accessed by appropriate people in accordance with the authority. So it needs a technology like Enigma to achieve it. Enigma uses multi-party computation (hereinafter referred to as MPC) and distributed hash-table (hereinafter referred to as DHT) technology, combined with the blockchain to divide the data to be stored into public blocks and private parts The blockchain has an existing way of computing and storing, and private data is Enigma's method of computing and storing by using the method called Off-Chain. Only authenticated users can access private data. However, many applications have the need for group sharing and decentralized records. This study uses Enigma to encrypt the data that requires privacy using the key of the encrypted file and then uploads it to DHT for storage, so that the owner of the private data can further share the data securely.
Security Analysis of a Threshold Quantum State Sharing Scheme of an Arbitrary Single-Qutrit Based on Lagrange Interpolation Method

Nowadays, quantum computing is gaining popularity in managing and distributing keys for data transfer this is because of Heisenberg uncertainty principle of quantum physics, which provides unconditional security compared to conventional cryptosystem. The present paper proposes two quantum secret sharing (QSS) schemes, using Lagrange’s interpolation method, for conventional and quantum key distribution system. Security analysis of the proposed schemes exhibits that our schemes are immune to the possible existing attacks, namely intercept-and-resend, photon number splitting (PNS) attack and participants attack. The proposed schemes are studied theoretically; their design and implementation in practical quantum channel indeed may require further research work.
Vulnerability of Cloud: Analysis of XML Signature Wrapping Attack and Countermeasures

Simple Object Access Protocol (SOAP) uses text-supported eXtensible Markup Language (XML) messaging configuration to interchange web encrypted information over a multitude of internet protocols such as SMTP, FTP, HTTP, etc. SOAP is one of the backbone solutions to exchange data between cloud and cloud service users via the Internet. The wide use of decentralized distributed cloud computing systems and rapidly increasing demand for cloud services result in a substantial increase in security vulnerabilities in the cloud. A comprehensive analysis of signature wrapping attack of XML Data and it’s countermeasures to detect and prevent this web security threats have been analyzed in this paper. In the context of security vulnerabilities, we have discussed the use of Xpath expression, ID referencing, relative XPath referencing, absolute XPath referencing, fastXPath Structure-based referencing, and their weaknesses.