Reviewerly/exHarmony huggingface dataset:
Guaranteeing differential privacy for sequence predictions in bike sharing systems

Summary Being part of public transportations, the bike sharing system plays an important role in solving the “last mile” problem. With more advantages than traditional bike sharing systems relying on fixed docks, dockless bike sharing systems are rapidly expanding, which generally operated under cloud‐based architectures. Emerging Things‐Edge‐Cloud (TEC) architectures are showing many advantages than cloud‐based architectures including greater potential in reducing risks of privacy leakage due to their decentralized computing manners. However, even in the TEC architecture, differential attacks, which aims to mine information of individuals in the aggregated data, still cause major threats. To solve this problem, this article proposes a novel encoding and decoding framework within the TEC architecture for time series predictions in bike sharing systems, with considerations of guaranteeing differential privacy. In particular, we first construct a dynamic autoencoder based on the Long Short Term Memory (LSTM) network, then the collected raw temporal data are encoded into a hidden state in the edge end. In the cloud end, the trained output weights are used to reconstruct the current time series and predict the next‐period time series according to received hidden states. This framework not only improves the computation efficiency of bike sharing system by leveraging computing power at the node end but also provides safer data publications that meet the differential privacy requirements. Experiments are conducted on real‐world datasets, results demonstrate the effectiveness of the proposed framework in providing data services with both high utility in sequence predictions and high safety level for users' privacy.
Algorithm for Detecting Opinion Polarity in Laptop and Restaurant Domains

Knowing the state of public opinion regarding a product, the reputation of a celebrity or the assessment of a political candidate is very useful in today’s world. For example, in the particular case of a company, analyzing the opinions of its customers is very beneficial, since it allows to know the disconformities and, from these, to define new strategies that guarantee to obtain a greater degree of satisfaction in the users, as well as a greater success within the market. This article aims to develop a method for classifying the polarity of product aspects.
Finite Mixture Models with Student t Distributions: an Applied Example

The use of finite mixture modeling (FMM) to identify unobservable or latent groupings of individuals within a population has increased rapidly in applied prevention research. However, many prevention scientists are still unaware of the statistical assumptions underlying FMM. In particular, finite mixture models (FMMs) typically assume that the observed indicator variables are normally distributed within each latent subgroup (i.e., within-class normality). These assumptions are rarely met in applied psychological and prevention research, and violating these assumptions when fitting a FMM can lead to the identification of spurious subgroups and/or biased parameter estimates. Although new methods have been developed that relax the within-class normality assumption when fitting a FMM, prevention scientists continue to rely on FMM methods that assume within-class normality. The purpose of the current article is to introduce prevention researchers to a FMM method for heavy-tailed data: FMM with Student t distributions. We begin by reviewing the distributional assumptions that underlie FMM and the limitations of FMM with normal distributions. Next, we introduce FMM with Student t distributions, and show, step by step, the analytic and substantive results of fitting a FMM with normal and Student t distributions to data from a smoking-cessation trial. Finally, we extend the results of the applied example to draw conclusions about the use of FMM with Student t distributions in applied settings and to provide guidelines for researchers who wish to use these methods in their own research.
Review of the algorithms steganography in PDF documents and analysis

In this article, we will explore methods of using data hiding in a pdf document. The widespread use of PDF files can make its use for this purpose an interesting and practical solution. We was explore of algorithms of steganography algorithms from open sources and based on the analysis, an algorithm for detecting steganography proposed. We explore the methods to use a hide data in structure of the pdf document format, the basic strategies for hiding data, the statistical characteristics of PDF operators, we analysis the detection of changes after applying the algorithm for hiding data.
Exploration and Application of Software Development Automation Technology

Abstract Application development often faces problems such as demand changes, long cycle, and low efficiency. This paper attempts to solve such problems with automated generation technology. The article abstracts the design and development process of a typical application system; analyzes the key issues; then, after designing the overall architecture of the builder, it describes the design of interface presentation layer, control layer (business logic layer) and data model layer; finally this paper describes the challenges encountered by the builder and improvements in practice.
Learning Long-text Semantic Similarity with Multi-Granularity Semantic Embedding Based on Knowledge Enhancement

research-article Learning Long-text Semantic Similarity with Multi-Granularity Semantic Embedding Based on Knowledge Enhancement Share on Authors: Deguang Peng Chongqing Megalight Technology co. LTD, China Chongqing Megalight Technology co. LTD, ChinaView Profile , Bohui Hao Chongqing University of Posts and Telecommunications, China Chongqing University of Posts and Telecommunications, ChinaView Profile , Xianlun Tang Chongqing University of Posts and Telecommunications, China Chongqing University of Posts and Telecommunications, ChinaView Profile , Yingjie Chen Chongqing University of Posts and Telecommunications, China Chongqing University of Posts and Telecommunications, ChinaView Profile , Jian Sun Chongqing University of Posts and Telecommunications, China Chongqing University of Posts and Telecommunications, ChinaView Profile , Runzhu Wang University of Chongqing, China University of Chongqing, ChinaView Profile Authors Info & Claims CCRIS 2020: 2020 International Conference on Control, Robotics and Intelligent SystemOctober 2020 Pages 19–25https://doi.org/10.1145/3437802.3437806Published:27 October 2020 0citation35DownloadsMetricsTotal Citations0Total Downloads35Last 12 Months35Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Text Detection of Power Equipment Nameplates Based on Deep Learning

Recognition of the nameplate text in power equipment by image recognition technology can effectively improve the equipment management in power system. Text detection is the first step of text recognition, which also affects significantly the accuracy of text recognition. In this paper, an Efficient and Accuracy Scene Text Detector (EAST) based text detection model is proposed for detecting the characters on power equipment nameplates. Data augmentation technology is used to expand the data set of nameplate images. The experiment results show that the proposed model achieves a detection precision of 95.48% and a recall of 97.08%. Compared with the traditional text detection method, the EAST-based model performs better on the nameplate images of power equipment and has stronger generalization ability. Moreover, the nameplate text detection model runs at 7 frames per second (FPS) on average, which is faster than the traditional methods.
Development of Android-Based Quiz Video Game: Mathventure

This study aims to build an Android-based quiz video game. This video game application is designed using the Game Development Life Cycle (GDLC) process model. That starts from, the initiation stage, followed by the stages of preproduction, production, then video games are tested internally and externally (beta), until later it will be released in general. The results of this study are Android-based quiz video game prototypes. This video game takes the form of a fight between two magicians. This game features single player and two-player. Each player fights by answering the true-false questions which can be chosen by taping the selected option. With this gameplay it is expected to be able to practice cognitive abilities remembering, understanding, and evaluating the players.
A new 3D segmentation approach using extreme learning machine algorithm and morphological operations

Segmentation is one of the most crucial steps of image processing. Because 3D images contain depth information, they have gradually gained importance for numerical systems in image analysis. In the present study, a new 3D segmentation method based on extreme learning machine and morphological operations (3DS-ELM) is proposed. The present study benefits from extreme learning machine (ELM) algorithm, which is a novel and fast learning algorithm for single-hidden layer feedforward networks (SLFNs), for training objects. Because a 3D model contains many points, direct segmentation on a 3D model is time-consuming and causes problems in the segmentation process, the proposed approach minimizes these problems and offers a quick and high-performance 3D segmentation method that can be used in various industrial fields. The proposed 3DS-ELM was compared with different approaches in order to analyze its 3D segmentation performance. Experimental studies proved that the proposed 3DS-ELM performed better than other approaches.
CNN Based Electromagnetic Side Channel Attacks on SoC

Abstract Side channel attacks are well-known thread to ciphers in real world. In this paper, we give a CNN based profiled side channel attacks on AES cipher implemented on HI Silicon Kirin 620 SoC, which is a 1.2GHz multi-core platform. With 100,000 acquired side channel traces to train, the CNN have ability to recover real key byte under 1,000 traces, even though the traces are misaligned.
Rapid Clustering with Semi-Supervised Ensemble Density Centers

Clustering algorithm regards robustness, stability, accuracy as performance measurement. In most recent studies, there has been no detailed investigation of resource usage such as CPU, memory, and executing time in most recent studies, one unanticipated finding was that several consensus functions waste many resources to give the outcomes of a small features dataset. In order to resolve these issues, we suggest two new measurement aspects that termed as resource usage and rapidity. Thus, we proposed a new method characterized by rapid implementation, as well as improved the accuracy with the best pure result. This model termed as Rapid Clustering with Semi-supervised Ensemble Density Centers. The strengths of our model is based on fewer iterations and depend on formulas and built-in functions instead of complex coding procedures, then pick objects with its features as cluster centers, and finally, utilize the semi-supervised learning specifically the pairwise constraints, and take advantage of density calculation. Furthermore, the experimental results demonstrated that our model gained the results rapidly with best accuracy and purity.
Cycle Structures of a Class of Cascaded FSRs

In this paper, we study a class of binary nonlinear feedback shift register sequences generated by cascaded feedback registers, one is an LFSR and the other one generates a de Bruijn sequence. The cycle structure (in particular, the initial state of each cycle) is determined by solving a system of linear equations. As an application, we can generate de Bruijn sequences of large period algorithmically.
USBBBR-FD: An optimal object localization algorithm for remote sensing images

Recent years have observed an immense refinement in the domain of object localization, especially over remote sensing images. Object localization aims to predict the objects within an image as well as its boundaries. Achieving accurate object localization over remote sensing images is always challenging due to the complex context information. The paper mainly concentrates on solving the problem of accurate object localization using a three-stage localization pipeline. The introductory stage in the pipeline focus on generating regions of interest (RoI) called candidate regions using selective search algorithm. Then, each of these candidate features is extracted by passing it through a hybrid convolutional neural network (CNN) model. Finally, to improve the localization accuracy we propose an optimal object localization technique called Unsupervised Score Based Bounding Box Regression using Feature Descriptors (USBBBR-FD) algorithm integrated with non-maximum suppression (NMS) algorithm to optimize the bounding boxes of regions which are detected as objects. Analysis shows that the detection precision of the hybrid CNN model is higher than that of any other single CNN model and the dimension-reduction CNN model performs far better than retrained CNN models. Experiments show that the proposed USBBBR-FD algorithm can more accurately locate objects within remote sensing images and also shows robustness in complex background as compared with traditional features extraction methods.
Charlie and the CryptoFactory: Towards Secure and Trusted Manufacturing Environments

The modernization that stems from Industry 4.0 started populating the manufacturing sector with networked devices, complex sensors, and a significant proportion of physical actuation components. However, new capabilities in networked cyber-physical systems demand more complex infrastructure and algorithms and often lead to new security flaws and operational risks that increase the attack surface area exponentially. The interconnected nature of Industry 4.0-driven operations and the pace of digital transformation mean that cyberattacks can have far more extensive effects than ever before. Based on that, the core ideas of this paper are driven by the observation that cybersecurity is one of the key enablers of Industry 4.0. Having this in mind, we propose CryptoFactory - a forward-looking design of a layered-based architecture that can be used as a starting point for building secure and privacy-preserving smart factories. CryptoFactory aims to change the security outlook in smart manufacturing by discussing a set of fundamental requirements and functionality that modern factories should support in order to be resistant to both internal and external attacks. To this end, CryptoFactory first focuses on how to build trust relationships between the hardware devices in the factory. Then, we look on how to use several cryptographic approaches to allow IoT devices to securely collect, store and share their data while we also touch upon the emerging topic of secure and privacy-preserving communication and collaboration between manufacturing environments and value chains. Finally, we look into the problem of how to perform privacy-preserving analytics by leveraging Trusted Execution Environments and the promising concept of Functional Encryption.
A Finite-Element Simulation-based Prototyping for Classical Guitars using COMSOL Multiphysics

In this paper, a Finite Element Model of a classical guitar was developed, and then compared to empirical data and actual prototypes. Our previous work showed the effects of wood material properties on the vibration modes of a simple model of a classical guitar body without the bracing and air coupling. This paper compares a more comprehensive finite-element model for the coupled string, body and air. Using COMSOL Multiphysics, Eigenfrequency and frequency response simulations were compared to an actual Chladni experiment. A model for D'Addario Pro Arte strings were also developed using manufacturer's specifications. The string model simulation showed 16.68 cents absolute average deviation with 7.21 cents deviation range. Differences on the mode frequencies between the body and the coupled system were also observed. Lastly, the differences between the observed modes of the actual classical guitar and the coupled finite-element model lie within 21% percent.
Inter-packet delays normalization to limit IP covert timing channels

Covert channels are used for information transmission in a manner that is not intended for communication and is difficult to detect. Methods of active covert channels detection may be insecure. Therefore, it is better to use approaches that limit covert channels preliminary. We propose some traffic normalization techniques to limit covert timing channels. Recommendations to using the counteraction methods were given and their influence on the communication channel capacity was investigated.
Training of Student Practical Training Managers

In today’s higher education system, most teachers do not undergo special education to acquire teaching competencies. Even though they possess expertise in the profession relevant to the teaching they perform, in teaching, methodology can only rely on the experiences of other colleagues or their own intuition or talent. In this paper, special attention is paid to the preparation of practical teaching teachers to work with students. To develop teacher competences from the point of view of professionalism and professional development, it is especially important that the teachers know the theoretical and methodological approaches to teaching, with the aim of including into research on teaching that should directly lead to the improvement of one’s own teaching practice. Teachers need to understand the importance of self-evaluation and know its techniques, especially collaborative evaluation in improving their own teaching practice. For students, it is important to develop the ability to integrate theoretical knowledge and to transform and apply them in a practical context. Practical training must be closely related to the study program. It is designed to be a productive work experience for both sides, students and employers. In the long run, students’ experiences form opinions and attitudes about the company, industry and their future career, while on the other hand, employers are able to meet young students and hire them in their company after graduating. It is reflective practice that should develop students’ abilities for independent observation, decision-making, continuous research, collaboration and communication with others.
Public registers with personal data under scrutiny of DPA regulators

Data is critical for business in the digital age. Cloud, mobile, IoT, machine learning, AI, blockchain, and other technology-driven developments are accelerating process automation with data-driven disruption that re-defines roles and boundaries of data processing. Data privacy and security laws are pivotal in this context. Regulators draft these laws to restrict how businesses collect, use, share, store, and disclose data. Recent years have witnessed an unprecedented expansion in data privacy and security regulations globally as regulators seek to catch up to technology. This paper underlines the principles of how EU General Data Protection Regulation ("GDPR") focus at specific purpose of processing and storage of personal data in public registers. Some of public dataset providers ("public registers") offer this data as part of their services without consent or other adequate purpose. Selection of reliable partner that cover this service with appropriate legal coverage (consent, terms & conditions that define the purpose) is crucial part for data rental. Assurance of organization acts as a step of necessary prevention with an aim to avoid fines from Data Protection Authorities ("DPA") regulators. This paper excludes the issue of public registers such as business register or central register of contracts and private registers, which have their status defined by law, such as common banking register. Focus and emphasis is on the processing of private data in public registers without a consent by statutory as a data subject. The personally identifiable information ("PII") of himself used to be provided as a part of service – data rental of B2B contacts. Typical examples of such registers in Slovakia are finstat.sk, otvorenesudy.sk, public.digital, ekosystem.slovakia.digital, data.gov.sk, bisnode.sk or cribis.sk.
Decision Support System for Determining the Quality of Salt in Sumenep Madura-Indonesia

Abstract Salt is one of Indonesia’s major commodities. However, the quality of industrial salt in Indonesia is still an obstacle, so the need for industrial salt still relies on imported salt, especially from Australia. Quality improvement is done through purification using the recrystallization method. The use of a method that is still simple results in the salt being produced still has an as-is quality. Quality is shown from the appearance of salt physically and chemically. Good salt is shown by the crystal form which is smooth and has clear white color. Therefore, good knowledge of salt quality must be known early, in addition to being able to meet the Indonesian National Standard (SNI),in this way salt farmers will more easily improve the quality of salt produced and can differentiate salt designation based on its quality category. This study takes the theme of how to make decisions to determine the quality of salt, so that a decision support system will be built to assist in determining good salt quality by using the Simple Additive Weighting (SAW) method. This method can support the decision making of salt quality determination based on the weight of each attribute. Morever, the total score of the end result can produce a good alternative decision in accordance with specified criteria, so that it will produce salt quality.
Predictor Based Block Adaptive Near-Lossless Coding Technique for Magnetic Resonance Image Sequence

In today's world of information exchange, digital imaging of pathological information has grown very fast and hence plays an essential role in disease diagnosis. Radiological modalities such as Computed Tomography (CT) scanners, Magnetic Resonance Imaging (MRI), X-Ray are increasing massively. The demand for handling images in digital form has increased dramatically in recent years for efficient archiving and transmission that raises the need of efficient compression. In this paper, a near-lossless predictive based coding technique is proposed that address the low coding efficiency of lossless coding and low image quality of lossy coding. The proposed technique removes inter-pixel redundancy and physco-visual redundancy by employing Resolution Independent Gradient Edge Detector (RIGED) at optimal threshold value and optimal q-level for quantization respectively. Coding redundancy is removed by employing block based encoding at optimal fixed size block. Volumetric MR image datasets of 8 bit and 16 bit are utilized for implementation of the proposed technique. The proposed technique is compared with the state-of-the art techniques and outperforms Context Adaptive Lossless Image Compression (CALIC), Low Complexity Lossless Compression for Images (LOCO-I) and Differential Pulse Code Modulation (DPCM) with context switching NN predictor by 34.33%, 39.80% and 30.35% respectively.