Reviewerly/exHarmony huggingface dataset:
Timing-Sensitive Synchronization for Efficient Secure Multi-Execution

Secure Multi-Execution (SME) is a promising solution for precise automatic information flow control that can be used to transform potentially vulnerable programs into secure variants. However, due to the multi-execution, it comes with high resource demands. Recently proposed efficient optimizations of SME, such as Faceted Secure Multi-Execution (FSME) and Demand-Driven Secure Multi-Execution (DDSME), unfortunately cannot uphold the same level of security with regards to attacks on the timing-channel. In this paper, we propose a novel, queue-based synchronization scheme for DDSME (Q-DDSME) that guarantees (indirect) termination- and timing-sensitive non-interference. To show that our improvement is applicable to existing programs, we implemented a Q-DDSME prototype for compiled code and provide evidence that it is (i) more efficient than unoptimized SME in realistic scenarios, (ii) guarantees (indirect) termination- and timing-sensitive non-interference, and (iii) preserves per-channel transparency. This is an important result that shows that more efficient solutions can be used without sacrificing security.
Summary of the 14th International Conference on Global Software Engineering (ICGSE)

The International Conference on Global Software Engineering, in its 14th iteration, continues to provide researchers and practitioners with a leading forum to share their research ndings, experiences, and new ideas on diverse topics related to global software engineering. ICGSE 2019 was held in Montreal, Canada on May 25-26, in conjunction with the 41st International Conference on Software Engineering under the theme \Succeeding in the Global Software Industry". Topics included were: distributed teams, methods and processes, business strategies, technologies supporting distributed cooperative work, education, and emerging technologies to support/improve/enhance GSE. Contributions presented at ICGSE tackled these topics providing concepts, evidence, and experiences.
Using Smartphone to Enhance Education

With the growth of smartphones and associated technologies has come millions of smartphone programs which are called “apps”. Most apps are like any tool, if used sensibly they can be of value, but if used unwisely they can distract students to the detriment of their education. Many techniques that use smartphones have been proposed to help improve student learning and for better educational outcomes. This chapter looks at some of these methods that can help nullify the negative effects of smartphones in the classroom or co-opt them into helping the education process.
A coaxial thickness measuring device for full-width banknote based on hall effect

Abstract In this paper, a multi-channel banknote thickness measurement mechanism based on hall effect is designed to meet the demand of banknote identification for the cash circulation module of financial instruments. It uses coaxial type rocker mechanism to mechanical amplification of the thickness of the banknote, translates the magnetic steel with hall sensor location change into money thickness signal, and the signal is amplified, filtered and calibrated to form a banknote thickness image. Finally, the detection results of the whole paper currency are obtained through calculation, so as to realize the detection and identification of the whole paper currency tape, folding and partial damage.
Mobile Mammography: Technological Improvements Using Non-Linear Polynomial Filters

American College of Radiology (ACR) recommends that annual mammography screening is necessary to be performed for women population above the age of 40.
Estimation Effort using Developed Cat Swarm Optimization

Estimation is one of the most important factors in building and designing software. This research to provide a study of the principles of reducing the cost of software and understanding how to apply these techniques to the general programs. The swarm intelligence algorithms have been used because they have community-based methods where there is a range of potential solutions in this community. The ideal solution is sought through repetitive steps, these methods are based on solving complex problems by simple factors without central control or a comprehensive model and possess distributed control, the most important characteristics of the swarm intelligence, It will produce a self-contained, adaptive, scalable, flexible, powerful, parallel, self-regulatory and cost-effective system.
High-resolution 3D model reconstruction for light field display

In recent years, 3D display technique is one of the emerging technology and gradually becomes accessible to a broader audience. However, because of the traditional 3D reconstruction method is limited by the number of the feature found in the image, the resolution of the generated 3D model is not high enough for 3D display. A new system is purposed, in which we consider the vertical and horizontal disparity between images, and the optical flow is used to replace the feature matching segment, so that more points can be pushed into the reconstruction process for improving the resolution of the models. Experimental results prove that the resolution of the models can be enhanced effectively. The details of the model are preserved, and the holes in the weak texture region are successfully filled.
Sample Generation Combining Generative Adversarial Networks and Residual Dense Networks

Recently, the generation of adversarial networks has made great progress in image generation and image enhancement, and it is even able to generate high-quality false images to deceive the human eyes, but Generative Adversarial Networks (GANs) still have problems, such as training process instability and mode collapse. To solve the problems above, we use the dense residual network and the residual networks to construct a generator and a discriminator of the networks Combing the Generative Adversarial Networks and Residual Dense Networks (RDGAN), respectively, and use the spectrum normalization model to constrain the GAN networks which can prevent the parameter size. To avoid gradient anomaly, combining with the TTUR optimization strategy, we design and implement several simulation experiments on the 102 Category Flower Dataset. Experimental results show that our method is superior to most existing methods in most cases.
The terms of optimization of education of professional honour of students

Today, in a situation of global change in the world and society, the transformation of the man himself and the formation of a new worldview, in the center of which such quality of personality is as an honour, remains the most significant. The notion of honour is closely linked to moral categories such as conscience, dignity, responsibility, which indicates the complexity of defining the content of this phenomenon. The problem of upbringing professional honours from future specialists is not among the new ones. This is due to the fact that the term «professional honour» has long been firmly included in the system of ethical categories and reflects the level of moral assessment of a person himself, the attitude of others to him and society as a whole. That is why, with the practice and science, the issue of improving the professional education system is being raised more acutely. Under the professional honour, the author understands the set of moral qualities and values that form the person’s attitude towards professional activity and behavior in the course of solving professional problems. In the plane of his research, the author considered it expedient to substantiate both external (pedagogical) and internal (psychological) conditions of education of professional honor of future teachers. The analysis of the researchers’ positions on the definition of the term «conditions» made it possible to conclude that the conditions are components of the pedagogical system that reflect the educational environment and influence its effectiveness. So, in summary, the author determines the complex of psychological and pedagogical conditions that will promote the education of the professional honour of future teachers. Therefore, the following psychological and pedagogical conditions for the education of the professional honour of future students: enhancing the students’ moral experience regarding the peculiarities and ways of showing professional honor by updating the contents of educational disciplines and the introduction of innovative methods and forms; use of tasks involving the education of professional honour in the process of pedagogical practice; the enrichment of the educational space by dialogue interaction, the creation of situations of success and cooperation.
Picture News Collection: A Dataset for Automatic Picture News Thumbnail Selection

Picture news has become more and more popular among online news in recent years. As the first impression to viewers, thumbnail plays a very important role in picture news. However, it is time consuming to manually select thumbnails for a huge amount of picture news. In this paper, we introduce a new task of automatic picture news thumbnail selection. Given a piece of picture news containing a set of images, this task is to select several appropriate images from the picture news as candidate thumbnails. To this end, we present a large publicly available image dataset for this task, called Picture News Collection(The Picture News Collection 0.1 version can be publicly available online at https://github.com/anonymity01/Picture-News-Collection .). The Picture News Collection contains more than 4 million images of 347,731 picture news from two famous news websites, Sina News and NetEase News. Selecting good enough thumbnails is complicated and needs to consider many aspects, such as attraction, hot topics, content integrity, etc. In order to select appropriate candidate thumbnails, we propose an attention-based thumbnail selection model, and the experimental results comparing with three image classification based baselines show that our proposed methods outperform the baselines. We introduce the automatic picture news thumbnail selection task and the dataset to encourage further studies of this challenge.
Deep Q-Learning with Phased Experience Cooperation

The value-based reinforcement learning algorithms train agents by storing previous experience rewards, however, this simply sampling at the same probability results in a slow learning rate. In reality, the importance of each sample is not exactly the same. The use of prioritized experience replay greatly improves the learning rate of reinforcement learning, but good experiences and more effective strategies may be ignored or missed. In order to overcome two shortcomings, a Deep Q-learning with phased experience replay (MixDQN) is put forward in this article, where the priority is used to improve the training rate in the early stage of training and the random sampling in the later stage to make good use of good experience. Experiments with three classic control problems are based on OpenAI Gym. The experimental results prove that the MixDQN can enable an agent’s learning more stably, quickly and efficiently.
Combining arrival classification and velocity model building using expectation-maximization

SummaryProbabilistic inversions of wide angle reflection and refraction data for crustal velocity models are regularly employed to understand the robustness of velocity models that can be inferred from these data. It is well understood that the uncertainties associated with the picks of individual arrivals contribute to overall model uncertainty. Typically only a modicum of effort is devoted to quantifying uncertainty in the traveltime picks; a constant noise estimate is commonly assigned to a given class of arrivals. Further, determining the class of arrivals is often left to the behest of the interpreter, contributing additional uncertainty to the data that is both difficult to quantify and may be altogether incorrect. Given the crucial role data uncertainty plays in characterising model robustness, there is a need to thoroughly and appropriately quantify uncertainty in the traveltime data which itself is inferred from the waveform. Here we propose a method that treats arrival or phase classification as part of the velocity model building (inversion) framework using the well-established expectation-maximization (EM) algorithm.
Preliminary Simulation of Robot on Script Detection from Camera Images

Here is a lot of information is available in a photo images such as advertisement, book cover, banners and many more. Images with text are widely available because of efficiency and low cost of digital portable devices, which provide chances to manual transfers of a document images. Analysis techniques of manually transferred images could serves as a reference and starting point of further technique development but the method cannot be used directly on images captured using cameras. Camera pictures can be problematic with blurry, low resolution, distorted, disorientated images, apart from, complex interaction between content and background. Therefore, Optical Character Recognition (OCR) technique was used to change printed text into editable text that is convenient and accessible in various applications. However, OCR accuracy depends on text pre-processing and segmentation algorithm. Hence, this manuscript to introduce OCR Tesseract method and the history of OCR Open Source Tesseract system, its architecture and outcome of trial on various type of images to determine efficiency of OCR Tesseract and accuracy ratio of extracted images from camera.
Evaluation Methods of English Advanced Pronunciation Skills Based on Speech Recognition

The assessment of English advanced pronunciation skills evaluates the pronunciation fluency, employing techniques from various fields including artificial neural network, speech recognition and linguistics. The pronunciation fluency includes 4 main parts of pronunciation skills, including speech stress, pause, liaison and plosive. This paper mainly studies liaison and plosive. In the aspect of liaison recognition, GRU (Gated Recurrent Unit) are used to train the classification model. In the field of plosive recognition, words both with l plosive label or not are added to the process of language model construction; and acoustic features both with plosive pronunciation skill or not are used to the process of acoustic model construction, with these two parts, static decoding network WFST (Weight Finite-State Transducer) is constructed. The experimental results show that, for different types of liaison, the average accuracy of GRU models are up to 92.34%; for plosive model, the accuracy of the static decoding network model is 72.17%.
SIMILANT

We present SIMILANT, a data analytics tool for modeling similarity in content-based retrieval scenarios. In similarity search, data elements are modeled using black-box descriptors, where a pair-wise similarity function is the only way how to relate data elements to each other. Only these relations provide information about the dataset structure. Data analysts need to identify meaningful combinations of descriptors and similarity functions effectively. Therefore, we proposed a tool enabling a data analyst to systematically browse, tune, and analyze similarity models for a specific domain.
A Novel Particle Filter for High-Dimensional Systems Using Penalized Perturbations

In order to efficiently perform inference on high-dimensional nonlinear non-Gaussian state-space models using particle filtering, it is critical that particles are generated from the optimal proposal distribution. However, finding a closed-form to the optimal proposal proves to be difficult in practice, as many application problems do not satisfy the requirement of conjugate state and observation equations. In this paper, we overcome this challenge by designing a novel method that introduces conjugate artificial noise into the system and optimally perturbs the particles in a way that balances a bias-variance tradeoff. Our method is validated through extensive numerical simulations applied to a gene regulatory network problem, and results show better performance than that of state-of-the-art methods, especially in cases where the state noise is heavy-tailed.
Integrating Hyperspectral and Radiometric Remote Sensing, Spatial Topographic Analysis and Surface Geochemistry to Assist Mineral Exploration in Southern Australia

Exploration and mining companies in Australia are faced with the challenge of locating deep ore deposits buried under hundreds of metres of cover sequence. This research aimed to investigate how the integration of hyperspectral remote sensing and surface geochemistry can be used to identify potentially economic mineralisation under cover. While each of these methods are individually mature, their combination for the purpose of mineral exploration is novel. A variety of methods including classification of spatial data and radiometric imagery, hyperspectral alteration mineral mapping and lithogeochemical analysis produced useful results that might benefit exploration specifically in the study area. Further, the methods developed might benefit the wider Australian and international minerals industry, especially in areas where there is no obvious expression of alteration at the surface.
A Comprehensive Analysis of the Most Common Hard Clustering Algorithms

From past decades, Clustering is the process of observation that set assignment into subsets called clusters. It is an unsupervised method and can be grouped as hard and soft clustering. Hard clustering methods assign the sample point to a specific cluster whereas soft clustering methods give a probability of assignment to all clusters. In this paper, we have tried to give intuition to some of the popular hard clustering methods with their associated algorithms.
Information Flow Versus Divisibility for Non-invertible Dynamical Maps

We study the equivalence between information flow and completely positive divisibility—the two main approaches to Markovianity in quantum regime. Such equivalence is well known to hold for maps which are invertible. For non-invertible maps, the problem is more subtle. We show that for a class of so-called image non-increasing dynamical maps, the equivalence still holds true. Moreover, for qubit dynamics we show that the equivalence is universal, thus providing a comprehensive theory of quantum Markovianity at least for two dimensions. In the course of our proofs, we found certain mathematical restrictions on existence and impossibility of existence of completely positive trace-preserving projectors onto subspaces of finite-dimensional operator space. We illustrate our results with appropriate examples.
A Context Model for Code and API Recommendation Systems based on Programming Onsite Data

Code and application programming interface (API) recommendation systems are important guarantees for efficient and accurate code reuse to improve the efficiency of software development.Context data plays a key role in code and API recommendation.A large amount of programming onsite data has been generated, but existing code and API recommendation systems rarely consider the context based on programming onsite data, which leads to low efficiency and poor accuracy of code and API recommendation.In this paper, we proposed a context model for code and API recommendation systems.Our context model is based on programming onsite data collected during programming.It includes four aspects: developer, project, time, and environment.Developer data is labeled data abstracted from information according to developers' programming habits and abilities, project data is information about the project, time data is information about temporal aspects of developers interacting with the project, and environment data is all environment elements used by developers during programming.Then, we collected programming onsite data in three ways: explicit collection, implicit collection, and reasoning.Lastly, we built the context model using a coarse-grained abstract model for recommendation.Our context model retains the key information in the code while eliminating redundant information that may affect the accuracy of the recommend task, and it can theoretically improve the efficiency and accuracy of recommendation.
Dependable Person Recognition by Means of Local Descriptors of Dynamic Facial Features

In this work, a complementary approach that adds a dynamic component to face biometrics is proposed. The dynamic appearance and the time-dependent local features characterizing the face of an individual during speech utterance are indeed considered in their spatial and temporal components. Ultimately, the aim is to capture, represent and compare facial patterns related to speech utterance, to improve biometric system dependability thanks to an intrinsically difficult to forge descriptor. The proposed approach applies the concept of dynamic texture to the domain of person identification through dynamic facial patterns modeled by means of the Volume Local Binary Pattern (VLBP) descriptor, which effectively combines local features and movement. To the aim of improving the efficiency of this technique, only the occurrences of the Local Binary Patterns related to Three Orthogonal Planes (LBP-TOP) have been considered. A deep feed forward network has been trained and optimized on video samples from the XM2VTS database concerning utterance of a given sentence. The results obtained in the recognition task performed on test video sequences confirm that the proposed approach features state-of-the-art performances with regard to accuracy and robustness of the identification.
A New (3, 3) Secret Sharing Cryptography Scheme for Efficient Secret Data Transmission

The increased rate of internet usage in data transmission has led to the tampering or loss of data over the internet. The transmitted data may contain some confidential information, wherein the privacy of the information cannot be compromised. Secret Sharing scheme is used to secure the transmitted data. It deals with hiding a secret image (text image, scenery, etc.) into various cover images and then transmitting over the web to the receiver. Thus, a secret sharing scheme is proposed in this paper where a secret image is hidden in 3 cover images using LSB (least significant bit) technique to protect it during transmission. Each share contains a part of the secret image but the secret cannot be revealed independently. After comparing the proposed method with the state-of-the-art approaches, it was found to be the best.
An End-to-end Clustering and Classification Learning Network

With the dramatic increase of image data, image clustering algorithms have received extensive attention. Meanwhile, deep learning has shown excellent performance in various fields, while there are few studies on its application in image clustering. Moreover, previous deep clustering frameworks are multistage and difficult to embed the available supervision information. In view of the above, we propose a deep clustering network called CCLN (Clustering and Classification Learning Network), which combines feature learning, clustering, and classification to achieve end-to-end learning. At the same time, we add a branch of the supervisory to utilize multi-Grained supervision information, so as to improve the performance of image clustering. Extensive experiments show that CCLN effectively utilizes available supervision information, and improves clustering performance.