Reviewerly/exHarmony huggingface dataset:
The Truman Show: Attack On The Privacy Of Smart Homes Through Traffic Analysis

The sensor network is widely used in the Internet of Things environment to create a more intelligent and convenient life, sensors closely monitor and get information from the living environment, smart home devices respond to user instructions to provide services. The constant interaction data with the cloud server, however, is unwittingly exposing user privacy to danger. This paper attacks a variety of smart home devices and sensors through traffic analysis, proving that encrypted data has the risk of revealing user privacy in the wireless environment, even if the packet content is not obtained, the state of a single IoT device can be analyzed through the number and shape of packets. Also, considering the user behavior may lead to responses from multiple devices in the environment, so that more elaborate information can be further deduced through the analysis of the combination of multiple equipment states. Based on the attack on single devices, we carried out a joint attack on multiple devices using state sequence diagrams of all devices in the smart home. The experiments prove that the traffic patterns of IoT devices do leak private information of users, we provide the corresponding solution of traffic shaping to defend against such attack.
A Universal Physics Model based on Spherical Dimensions

In recent decades, physics has produced some interesting conclusions on the nature of our universe such as the expansion of space and singularities within black holes. There are equally fantastic conclusions of the nature of quantum particles and effects. Yet we also hear that physics cannot unify these theories and there are still many things which do not make sense or things which these theories predict which have not been found. Applying an engineering problem solving technique, one looks for common assumptions which might have been made but are incorrect. The common assumption which came to mind was Dimensions. All of physics assumes that the dimensions of Mass, Length and Time are unidirectional and linear. Physics performs mathematical extrapolations and dimensional analysis with an unquestioned assumption of the linearity and the fundamentality of 3 ML T . In addition, there appears to be an overriding assumption of the pre-existence of particles within larger particles. This paper proposes a radically different, yet readily understandable, alternative dimensional model. The model redefines those fundamental dimensions, repositioning an understanding of time and gravity, provides an alternative to the assumption of the preexistence of particles and proposes an elegant spherically dimensioned universal fabric from which everything in the universe can be constructed. The model presents a single basic physics dimensional framework on which all disciplines of physics could be reconfirmed or rebuilt. Physics appears to be attempting to resolve astronomical physics with gravity as the only field forming galaxies, stars, planets and moons while it tries to resolve atomic and quantum physics with an assumption of the pre-existence of particles plus electrical and magnetic fields to explain atomic and quantum effects. This unified physics model proposes that gravity, electric and magnetic fields are exactly the same entity, but in different dimensional directions. This combination of 3 directions of similar fields forms a fabric which constructs and transmits everything we observe in the universe
Multi-Tissue Derived Windowing Technology Based on Statistical Features and Its Colorization Application

Traditional CT data processing methods often employ the inherent window to clip the data and store the image. For some DICOM-type data processing software, although the window can be adjusted dynamically, it still requires users’ the medical knowledge and familiarity in medical images. In this work, we propose a derivative windowing process method (multi-tissue derived windowing technology) for multiple tissues and construct organs based on the statistical features of CT images. Then, we design a transformation function to convert the density value of CT image into gray value. Finally, we construct the automatic colorization network using Dense U-Net as the generator of Generation Adversarial Network(GAN) model, making the multiple organs of the human body colored in a globally optimized way to highlight the color texture features of them. In this way the quality of color images can be improved significantly. In the test study, we evaluate the proposed method using many image quality evaluation methods and subjective visual evaluation on the 3Dircadb data set. The experimental results demonstrate the effectiveness of the proposed method, which indeed improves the quality of the image.
Framework for Data Security Using DNA Cryptography and HMAC Technique in Cloud Computing

The main objective of the proposed work is to build a reliable and secure architecture for cloud servers where users may safely store and transfer their data. This platform ensures secure communication between the client and the server during data transfer. Furthermore, it provides a safe method for sharing and transferring files from one person to another. As a result, for ensuring safe data on cloud servers, this research work presents a secure architecture combining three DNA cryptography, HMAC, and a third party Auditor. In order to provide security by utilizing various strategies, a number of traditional and novel cryptographic methods are investigated. In the first step, data will be encrypted with the help of DNA cryptography, where the encoded document will be stored in the cloud server. In next step, create a HMAC value of encrypted file, which was stored on cloud by using secret key and sends to TPA. In addition, Third Party Auditor is used for authenticate the purity of stored documents in cloud at the time of verification TPA also create HMAC value from Cloud stored data and verify it. DNA-based cryptographic technique, hash based message authentic code and third party auditor will provide more secured framework for data security and integrity in cloud server.
Chaotic Map Initializations with Tiki-Taka Algorithm for Software Remodularization Problem

Software system is often a dynamic entity. During its lifecycle, software system often evolves to reflect the bug fixes and upgrades as well as the addition/removal of features from its users. As the evolution process takes place, the modularization becomes complex and gradually loose its quality. Addressing this issue, this paper evaluates the performance of automated software remodularization using the newly developed Tiki-Taka Algorithm (TTA) and its variants with four chaotic map initializations based on Chebyshev map, Circle map, Logistic Map and Piecewise Map. Remodularization results of 3 selected case studies (i.e., Printer Manager, IOT controller, and Layer Monitor) demonstrate that TTA with Chebyshev map gives the best overall performance over other initializations including the pseudo-random initialization within the original TTA.
Spreading Dynamics of SHIPR Pyramid Scheme Model on Scale-Free Networks

Nowadays, pyramid schemes have caused extremely negative effects on people’s lives and seriously damaged the social economy. With the rapid development of network and communication technology, people’s direct or indirect social interaction is more frequent, which makes the phenomenon of pyramid schemes more serious. Therefore, it is necessary to study transmission mechanisms and transmission rules of pyramid schemes. In order to study the influence of government management and social interaction topology on the spreading of pyramid schemes, a novel SHIPR (susceptible-hesitator-involved-punished-resister) pyramid scheme spreading model is proposed on scale-free networks. The spreading dynamics of pyramid schemes are analyzed in detail by mean-field theory. Then, the basic reproduction number R0 and equilibria are got. Theoretical analysis shows that the basic reproduction number R0 has a great correlation with government crackdown intensity for involved individuals, the coverage rate of government anti-pyramid scheme publicity for susceptible individuals and hesitator, and social interaction topology. Furthermore, the local asymptotic stability of fraud-elimination equilibrium is analyzed based on the Routh-Hurwitz criterion, the global asymptotic stability of the fraud-elimination equilibrium is discussed by the Lyapunov function, the global attractivity of fraud-prevailing equilibrium is proved in detail by comparison principle. Finally, numerical simulations verify the theoretical analysis results.
Genetic Algorithms in Data Masking: Towards Privacy as a Service

Today’s world is one where the number of publicly stored information and private data is growing exponentially, thus so is the need for more precise and more efficient data protection methods. Data privacy is the field that studies data protection methods as well as privacy models, tools and measures to establish when data is well protected and compliant with privacy requirements. Masking methods are used to perturb a database to permit data analysis while ensuring privacy.This work provides a tool towards privacy as a service. Selecting an appropriate masking method and an appropriate parameterisation is an heuristic process. Our work makes use of genetic algorithms to find a good combination of masking methods and parameters. To do so, a number of solutions (masking methods, parameters) are applied and evaluated, the effectiveness of each solution is measured and well performing solutions are passed on to future generations. Effectiveness of a solution is in terms of information loss and disclosure risk.
Behind Clint and Hoare’s goto Proof Rule

This paper takes a new look at Clint and Hoare’s proof rule for goto statements, published almost fifty years ago, and discloses several new findings unnoticed before. We start out to propose that the rule take small changes, so it becomes more manageable. As we explore further, we have discovered a simple total correctness rule for goto statements, which uses the variant function to argue about termination just like we do for loops. We further extend the idea and have obtained general proof rules, applicable to multiple labels, for both partial and total correctness of goto statements. Those rules, although complex in appearance, are easy to use in practice. We have successfully applied them to prove programs that current published rules failed to.
Moment Evolution Equations and Moment Matching for Stochastic Image EPDiff

Models of stochastic image deformation allow study of time-continuous stochastic effects transforming images by deforming the image domain. Applications include longitudinal medical image analysis with both population trends and random subject-specific variation. Focusing on a stochastic extension of the LDDMM models with evolutions governed by a stochastic EPDiff equation, we use moment approximations of the corresponding Itô diffusion to construct estimators for statistical inference in the full stochastic model. We show that this approach, when efficiently implemented with automatic differentiation tools, can successfully estimate parameters encoding the spatial correlation of the noise fields on the image.
Performance Comparison of Semantic Web Data Using Virtual and Cloud Services

Big Data techniques describe a new generation of technologies and architectures designed to economically extract value from an enormous volume of a wide variety of data. Hadoop does a massively parallel processing technique for processing data distributed on a commodity cluster rather than sequential. The mapper and reducer phase in Hadoop MapReduce Model helps to overcome the processing of large amounts of data in a distributed environment, which will affect the performance. Data can be divided into smaller chunks and stores each part of the data on a separate node within the cluster. Nodes are arranged using the Amazon web service and the virtual environment with different data loading sizes. Compared with both frameworks, Hadoop configured in a cloud plays a vital role in data warehousing and clustering of the data to get the best performance than virtual environment. Hence in this study, we segregate the semantic web data into pre-processing and clustering to enhance the performance improvement.
Single Image Dehazing Using Sparse Contextual Representation

In this paper, we propose a novel method to remove haze from a single hazy input image based on the sparse representation. In our method, the sparse representation is proposed to be used as a contextual regularization tool, which can reduce the block artifacts and halos produced by only using dark channel prior without soft matting as the transmission is not always constant in a local patch. A novel way to use dictionary is proposed to smooth an image and generate the sharp dehazed result. Experimental results demonstrate that our proposed method performs favorably against the state-of-the-art dehazing methods and produces high-quality dehazed and vivid color results.
Recommending Relevant Tutorial Fragments for API-Related Natural Language Questions

Application Programming Interface (API) tutorial is an important API learning resource. To help developers learn APIs, an API tutorial is often split into a number of consecutive units that describe the same topic (i.e. tutorial fragment). We regard a tutorial fragment explaining an API as a relevant fragment of the API. Automatically recommending relevant tutorial fragments can help developers learn how to use an API. However, existing approaches often employ supervised or unsupervised manner to recommend relevant fragments, which suffers from much manual annotation effort or inaccurate recommended results. Furthermore, these approaches only support developers to input exact API names. In practice, developers often do not know which APIs to use so that they are more likely to use natural language to describe API-related questions. In this paper, we propose a novel approach, called Tutorial Fragment Recommendation (TuFraRec), to effectively recommend relevant tutorial fragments for API-related natural language questions, without much manual annotation effort. For an API tutorial, we split it into fragments and extract APIs from each fragment to build API-fragment pairs. Given a question, TuFraRec first generates several clarification APIs that are related to the question. We use clarification APIs and API-fragment pairs to construct candidate API-fragment pairs. Then, we design a semi-supervised metric learning (SML)-based model to find relevant API-fragment pairs from the candidate list, which can work well with a few labeled API-fragment pairs and a large number of unlabeled API-fragment pairs. In this way, the manual effort for labeling the relevance of API-fragment pairs can be reduced. Finally, we sort and recommend relevant API-fragment pairs based on the recommended strategy. We evaluate TuFraRec on 200 API-related natural language questions and two public tutorial datasets (Java and Android). The results demonstrate that on average TuFraRec improves NDCG@5 by 0.06 and 0.09, and improves Mean Reciprocal Rank (MRR) by 0.07 and 0.09 on two tutorial datasets as compared with the state-of-the-art approach.
RefBERT: Compressing BERT by Referencing to Pre-computed Representations

Recently developed large pre-trained language models, e.g., BERT, have achieved remarkable performance in many downstream natural language processing applications. These pre-trained language models often contain hundreds of millions of parameters and suffer from high computation and latency in real-world applications. It is desirable to reduce the computation overhead of the models for fast training and inference while keeping the model performance in downstream applications. Several lines of work utilize knowledge distillation to compress the teacher model to a smaller student model. However, they usually discard the teacher's knowledge when in inference. Differently, in this paper, we propose RemERT to leverage the knowledge learned from the teacher, i.e., facilitating the pre-computed BERT representation on the reference sample and compressing BERT into a smaller student model. To guarantee our proposal, we provide theoretical justification on the loss function and the usage of reference samples. Significantly, the theoretical result shows that including the pre-computed teacher's representations on the reference samples indeed increases the mutual information in learning the student model. Finally, we conduct the empirical evaluation and show that our RemERT can beat the vanilla TinyBERT over 8.1 % and achieves more than 94% of the performance of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{BERT}_{\mathbf{BASE}}$</tex> on the GLUE benchmark. Meanwhile, RemERT is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{7.4x}$</tex> smaller and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9.5x}$</tex> faster on inference than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{BERT}_{\mathbf{BASE}}$</tex> .
Intelligent Tutoring Systems and the Inevitability of the Message Construct

New hardware and software concepts matured in the 1960s to open the way for a great variety of new, dynamic instructional forms. Serious and well-funded experimentation in intelligent tutoring systems marked the beginning of a new epoch of exploration. Change was rapid and constant. However, one element of instructional designs, the message construct, remained a stable factor and even moved to the foreground during this period of heated discovery. Because design was taking place at a much more detailed level during this period, there was much greater need for more granular constituents of designs, and messages (and their representations) filled the vacuum.
Guangxi University Scientific Research Management Based on Neural Network AHP and BP

At the shortcomings of low convergence efficiency and weak generalization performance in current scientific research management, a scientific research management model based on neural network AHP-BP is proposed. Construct the performance evaluation index system, collect the corresponding performance evaluation data, use neural network AHP-BP to simulate the human brain to train the performance data, and use the gradient method to determine the parameters of the neural network AHP-BP to establish a performance evaluation model. Finally, its performance is tested through simulation experiments. Actually, this paper studies the management of colleges and universities in Guangxi based on neural network of AHP and BP.
All Instantiations of the Greedy Algorithm for the Shortest Common Superstring Problem are Equivalent

In the Shortest Common Superstring problem (SCS), one needs to find the shortest superstring for a set of strings. While SCS is NP-hard and MAX-SNP-hard, the Greedy Algorithm “choose two strings with the largest overlap; merge them; repeat” achieves a constant factor approximation that is known to be at most 3.5 and conjectured to be equal to 2. The Greedy Algorithm is not deterministic, so its instantiations with different tie-breaking rules may have different approximation factors. In this paper, we show that it is not the case: all factors are equal. To prove this, we show how to transform a set of strings so that all overlaps are different whereas their ratios stay roughly the same.
<scp>J2EE</scp>architecture‐based network guidance system integration infrastructure and applications

The current world is in an era of the explosive growth in information and rapid renewal. This objective reality puts new demands on education reform. Educational systems and educational models must undergo major changes to meet the needs of creative talent. With the wide application of modern computer technology and network technology, the traditional education structure based on classrooms is constantly changing. In the traditional education process, it emphasizes directly instilling a large amount of knowledge to the students to obtain the quality of teaching. In the network environment, it focuses on cultivating students. Learning methods allow students to have independent knowledge acquisition and also update skills. Online teaching provides a broader and free platform and environment for students' learning. It not only provides rich and colorful teaching resources, but also broadens and breaks the time and space constraints of traditional teaching. The current main method of management of teaching resources is the component teaching resource library, which uses information technology to carry out unified maintenance and management of the teaching resources with increased benefits, and realizes rapid retrieval and convenient use of teaching resources. The numerical verifications are conducted in the final section of the research to verify the performance.
Text recognition technology for natural scenes

research-article Text recognition technology for natural scenes Share on Authors: Dandan Wu Shanghai Polytechnic University, China Shanghai Polytechnic University, ChinaView Profile , Yue Peng Shanghai Polytechnic University, China Shanghai Polytechnic University, ChinaView Profile , Qiang Zhan Shanghai Polytechnic University, China Shanghai Polytechnic University, ChinaView Profile , Jiajun Ma Shanghai Polytechnic University, China Shanghai Polytechnic University, ChinaView Profile , Guangjie Li Shanghai Polytechnic University, China Shanghai Polytechnic University, ChinaView Profile , Xiong Wei Shanghai Polytechnic University, China Shanghai Polytechnic University, ChinaView Profile , Xu Wang Shanghai Polytechnic University, China Shanghai Polytechnic University, ChinaView Profile , Jiancun Zuo Shanghai Polytechnic University, China Shanghai Polytechnic University, ChinaView Profile Authors Info & Claims DSIT 2021: 2021 4th International Conference on Data Science and Information TechnologyJuly 2021 Pages 212–217https://doi.org/10.1145/3478905.3478948Online:28 September 2021Publication History 0citation7DownloadsMetricsTotal Citations0Total Downloads7Last 12 Months7Last 6 weeks1 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
Model Maturity to Determine Student Profile During COVID-19 Pandemic at Public University

COVID-19 has accelerated the virtualization of processes in universities, academically, the role played by students. A maturity model was developed to determine the student profile based on digital culture, organizational culture and the psychological aspect, the latter being the most affected during confinement. These aspects were integrated to create a model in which students were classified into five profiles: silent, cooperative, creative, star, and superstar. This will allow us to know what type of student we have and what strategies to implement to achieve that they reach the profile of superstar student and can face the virtual teaching - learning process with success. To validate our model, it was applied to a public university where the level of maturity of the students was evidenced and strategies were established that would allow us to lead students with a superstar profile.