Reviewerly/exHarmony huggingface dataset:
Hidden Integrality and Semirandom Robustness of SDP Relaxation for Sub-Gaussian Mixture Model

We consider the problem of estimating the discrete clustering structures under the sub-Gaussian mixture model. Our main results establish a hidden integrality property of a semidefinite programming (SDP) relaxation for this problem: while the optimal solution to the SDP is not integer-valued in general, its estimation error can be upper bounded by that of an idealized integer program. The error of the integer program, and hence that of the SDP, are further shown to decay exponentially in the signal-to-noise ratio. In addition, we show that the SDP relaxation is robust under the semirandom setting in which an adversary can modify the data generated from the mixture model. In particular, we generalize the hidden integrality property to the semirandom model and thereby show that SDP achieves the optimal error bound in this setting. These results together highlight the “global-to-local” mechanism that drives the performance of the SDP relaxation. To the best of our knowledge, our result is the first exponentially decaying error bound for convex relaxations of mixture models. A corollary of our results shows that in certain regimes, the SDP solutions are in fact integral and exact. More generally, our results establish sufficient conditions for the SDP to correctly recover the cluster memberships of [Formula: see text] fraction of the points for any [Formula: see text]. As a special case, we show that under the [Formula: see text]-dimensional stochastic ball model, SDP achieves nontrivial (sometimes exact) recovery when the center separation is as small as [Formula: see text], which improves upon previous exact recovery results that require constant separation.
The Application of Mobile App to College Oral English Teaching in the Context of Internet +

Internet+ represents a new economic form. It deeply integrates the innovative achievements of the Internet into various economic and social fields, enhances the innovation and productivity of the real economy, and forms a broader new form of economic development that uses the Internet as an infrastructure and implementation tool. Nowadays, under the influence of oral English teaching, college students have some problems in English learning. Using the Internet + mobile app has become the mainstream of independent learning and expanding knowledge. The purpose of this article is to study the application of mobile apps to college oral English teaching in the context of Internet +. This article first summarizes the basic theory of “Internet +”, and then extends its core concept and technology. Combined with the current situation of college oral English teaching in my country's universities, analyze the existing problems and shortcomings. Use mobile app applications and college oral English teaching in the context of Internet +. This article systematically elaborates the system functional requirements, database design and functional module design of the spoken English teaching app under the Internet + background. And using questionnaire survey method, field survey method and other research methods, to carry out experimental research on the application of the Internet + background favorite mobile phone app to college oral English teaching. Experimental studies have shown that compared with the traditional college oral English teaching methods, the use of mobile phone apps in college oral English teaching is more efficient and more feasible.
Analysis of Feature Importances for Automatic Generation of Care Records

The aging society is becoming a more and more serious problem in developed countries. In Japan, the aging society is also becoming a serious problem same as other developed countries. As a result, the shortage of caregivers is expected to be a severe problem. For this reason, Information and Communication Technology (ICT) and other technologies are expected to be used to reduce the workload of caregivers. In our laboratory, we have developed a nursing care record application which used for recording the nursing care activities and nursing care records. In this paper, to improve the estimation accuracy of nursing care records, we analyze the nursing care record data and machine learning model using feature importance and data visualization. We proposed 10 new features e.g., previous amount of breakfast, previous amount of lunch, previous amount of dinner, snacking, level of requiring long-term care, sleep time on the previous day, amount of exercise on the day, physical condition on the day, weekday, and weather. Afterwards, we analyze the trends of the care record data and estimation results based on these new features. The evaluation of the average accuracy was 77.3% and the average F1 score of 49.8%.
Construction of Two Classes of Minimal Binary Linear Codes Based on Boolean Function

Linear codes with a few-weight have important applications in combinatorial design, strongly regular graphs and cryptography. In this paper, we first construct a class of Boolean functions with at most five-valued Walsh spectra, and determine their spectrum distribution. Then, we derive two classes of linear codes with at most six-weight from the new functions. Meanwhile, the length, dimension and weight distributions of the codes are obtained. Results show that both of the new codes are minimal and among them, one is wide minimal code and the other is a narrow minimal code and thus can be used to design secret sharing scheme with good access structures. Finally, some Magma programs are used to verify the correctness of our results.
LibGuides: Information Technology Help: Information Technology Help

Resources to help Central Penn College students navigate the technology required in their courses.
Small Sample Identification for Specific Emitter Based on Adversarial Embedded Networks

The technology of specific emitter identification (SEI) has important military significance in electronic warfare. However, it is hard to obtain sufficient signal samples from the specific emitter in the electromagnetic environment of the battlefield. Therefore, it is a challenging issue to learn from a handful of samples to accurately identify complex and changeable emitter. A small sample identification method based on adversarial embedded networks is proposed to solve this problem. This method combines the improved generative adversarial networks (GAN) and the Convolutional neural networks (CNN) for classification. In the context of a handful of samples, high-quality simulation samples are generated by the generator in the generative adversarial networks to expand the available feature quantities of the model, thereby improving the recognition efficiency and accuracy. Through the training and testing of a small number of radar emitter data and communication station emitter data, the results show that the method just needs a small amount of data to achieve higher recognition accuracy.
Importance of Security in Big Data Log Files on Cloud

Today cloud computing is a very popular technology, and many people use this technology in many ways. it's important to have it safe. This technology was primarily used to keep data safer and safer in the cloud, so in this article we suggest a security framework for large data logs in the cloud. There are many and many risks that threaten the integrity of this information in the great information. Therefore, in line with the development of technology, the level of security has also increased significantly over the years. Various technology techniques access several online activities, such as interaction with different internet sites and services, making the web more accessible to their plug-ins. As a result, these activities have created a global platform for malicious activities to add these devices that expose large data logs harmful attacks. Sky system is an online platform that requires proper security integration. In addition, the current state of online security threatens high data in the cloud, which has affected the performance and service model.
Prospect Theoretic Analysis on Weighted Vertex Cover of Networks

Weighted vertex cover (WVC) is a classical combinatorial optimization problem, which has rich practice significance. Indeed, the existing WVC studies based on game theory has introduced the traditional expected utility theory (EUT), where each vertex is treated as a rational player. However, the uncertainty caused by the weight factor is neglected. So how does this uncertainty affect players' behaviour? In this paper, the complete theoretical analysis is shown. Specifically, we model the WVC problem as an non-cooperative game on weighted networks and respectively establish EUT-based and prospect theory-based utility functions. Then, we prove the existence of mixed NE on this weighted game. Further, by designing a state function, we find that the vertices' state under mixed NE satisfies weighted vertex cover state of the network. Finally, a fictitious play algorithm is proposed to get a NE of the game. Our findings will pave the way for the future numerical simulation, which can demonstrate the impact of the prospect theory on weighted vertex cover.
Approximating the identity of convolution with random mean and random variance

We provide sufficient conditions on the function φ, on the sequence of random variables εj>0 and on the sequence of random vectors yj∈Rn such that E(1εjn(ω)∫z∈Rnφ(|x−z−yj(ω)|εj(ω))f(z)dz)⟶j→∞f(x) for almost every x∈Rn, f∈Lp(Rn), 1≤p≤∞, where E denotes the expectation, εj tends to 0∈R in law and yj tends to 0∈Rn in law.
Classification of Benign and Malignant Lung Cancer Nodule Using Artificial Neural Network

In recent years, the growth of technology can achieve and solve a large number of real-time problems. Recently, image processing techniques are used in medical areas to identify the tumor like lung cancer, brain tumour, breast cancer, etc. The main reason to use this image processing technique is to improve image quality and give accurate results. Lung cancer is the most dangerous disease among other types of cancer if it is not detected earlier. Therefore, the computer-aided diagnosis system has helped the doctors identify the disease early as possible. Here the proposed method uses image processing techniques with a machine learning approach for classification with accurate results to increase the patient survival rate.
LLJava live at the loop: a case for heteroiconic staged meta-programming

This paper investigates the use of staged meta-programming techniques for the transparent acceleration of embedded domain-specific languages on the Java platform. LLJava-live, the staged API of the low-level JVM language LLJava, can be used to complement an interpreted EDSL with orthogonal and extensible compilation facilities. Compiled JVM bytecode becomes available immediately as an extension of the running host program. The approach is illustrated with a didactic structured imperative programming language, Whilst.
Multiple Grey-scale Feature Based Shot Boundary Detection

Effective automatic tools to categorize the searching and retrieval of relevant video materials from the huge data set become vital due to the profusion of video data. Shot Boundary Detection (SBD) is an important preprocessing step of many video processing applications. Though there are many solutions developed in this area, still there is space for improvement. A variety of shot boundaries and different variant features are the big challenges in this area. In this paper, a two-stage SBD method with the combination of Gray Level Co-occurrence Matrix (GLCM), histogram and SIFT Keypoint matching is proposed. The first stage aims to reduce the miss rate and the second stage aims to reduce the false positives. Comparatively better result than the individual feature method is achieved.
Deepview: Deep-Learning-Based Users Field of View Selection in 360° Videos for Industrial Environments

The industrial demands of immersive videos for virtual reality/augmented reality applications are crescendo, where the video stream provides a choice to the user viewing object of interest with the illusion of “being there.” However, in industry 4.0, streaming of such huge-sized video over the network consumes a tremendous amount of bandwidth, where the users are only interested in specific regions of the immersive videos. Furthermore, for delivering full excitement videos and minimizing the bandwidth consumption, the automatic selection of the user’s Region of Interest in a 360° video is very challenging because of subjectivity and difference in contentment. To tackle these challenges, we employ two efficient convolutional neural networks for salient object detection and memorability computation in a unified framework to find the most prominent portion of a 360° video. The proposed system is four-fold: 1) preprocessing; 2) intelligent visual interest predictor; 3) final viewport selection; and 4) virtual camera steerer. First, an input 360° video frame is split into three Field of Views (FoVs), each with a viewing angle of 120°. Next, each FoV is passed to the object detection and memorability prediction model for visual interestingness computation. Furthermore, the FoV is supplied as a viewport, containing the most salient and memorable objects. Finally, a virtual camera steerer is designed using enriched salient features from YOLO and LSTM that are forwarded to the dense optical flow to follow the salient object inside the immersive video. Performance evaluation of the proposed system over our own collected data from various Websites as well as on public data sets indicates the effectiveness for diverse categories of 360° videos and helps in the minimization of the bandwidth usage, making it suitable for industry 4.0 applications.
Image Processing Analytics: Enhancements and Segmentation

Image enhancement and image segmentation play a crucial role in an image analysis pipeline as a front end facilitating effective feature extraction for object characterization and image analytics. The image processing techniques used in these functions have a long history in machine learning and computer vision but also continue to evolve. In this chapter, we will discuss some image enhancement and segmentation methods and their applications to molecular imaging analysis, with some emphasis on new developments such as deep-learning-based approaches that have demonstrated superior performance to conventional methods in many recent studies. For image enhancement, we will focus on image restoration techniques such as denoising and superresolution, image translation techniques such as image-to-image translation and image homogenization, and image augmentation based on deep-learning methods. For image segmentation, we will focus on data-driven techniques such as thresholding-based methods and K-means clustering, and model-driven techniques such as active contours, level sets, and deep-learning segmentation methods.
L2-CVAEGAN: Feature Aligned Generative Networks for Zero-Shot Learning

Many generative methods in zero-shot learning (ZSL) and generalized zero-shot learning (GZSL) perform data augmentation by random distribution. This naive strategy ignores the visual divergences of different classes, which will result in excessive differences between generated and real samples. In this work, random vectors sample from the real visual distribution encoded by the encoder. Therefore, the generated samples are more close to real-world samples. Additionally, the instances generated by GAN and VAE are not in the uniform numerical range, which causes the inconsistency of domain distributions. We perform domain alignment on visual features through L2 normalization. This strategy narrows variance between real and generated visual features. For fine-grained datasets, we set attribute, Word2Vec and Glove as the class-embedding vector of generative models. This natural way of semantic combination adds more potential information to each class. We name the proposed approach as L2-CVAEGAN, and conduct extensive experiments on several benchmark datasets. Compared with existing methods, these simple strategies lead to significant promotions. The comprehensive experiments of ZSL and GZSL prove the effectiveness of L2-CVAEGAN.
Depth Map Reconstruction and Enhancement With Local and Patch Manifold Regularized Deep Depth Priors

Depth map reconstruction is a fundamental problem in computer vision and image understanding. The depth map captured by depth sensors e.g. time of flight (ToF) and Kinect is often prone to low resolution, degradation, noise, and poor quality. Considering different limitations, this study proposes a novel model for robust depth estimation. For this purpose, a deep convolutional neural network (CNN) is employed because of its usefulness in image recovery and enhancement, due to the hierarchical application of convolution operations image recovery and enhancement processes are inspired partly by the deep image prior (DIP) model. This idea which is called deep depth prior (DDP) when used for depth recovery is equipped by manifold modeling framework as an explicit regularization. In addition, the similarity between the depth image and the corresponding color image is utilized by characterizing the matching between them. These objectives are aggregated in an optimization problem that can be solved to achieve remarkably better sharpness. Moreover, a non-local or patch manifold modeling step is used through the Hankel structured low-rank approximation, which produces a duplication of multiple matrices, to better eliminate noise and other depth degradations. Experimental evaluation of the proposed manifold regularized DDP method shows its superiority over several well-known and powerful restoration methods on standard benchmark images.
The Effectiveness of E-6tslearning in Teaching Reading for Academic Purposes to the Students with Different Learning Styles

This research was done to have the answers of these main questions; 1) what learning style dominates the reading ability?; 2) how is the implementation of E-6tslearning?; and 3) how is the effectiveness of E-6tslearning in teaching reading for academic purpose?. The objectives of this study are 1) to explain the learning style dominates the reading ability; 2) to explain the implementation of E-6tslearning in teaching reading for the academic purpose; 3) to explain the effectiveness of E-6tslearning as the method in teaching reading for academic purposes. The research design used was 2x3 ANOVA factorial designs. The subject of the study was forty-four third semester students of the English Department of Pekalongan University, Indonesia. Twenty-two students became an experimental class, and twenty-two students became control class. The experimental was treated by using the E-6tslearning method, and the control class was given the lecturing. The result shows that the visual learning style dominated the reading ability. The dominant reason was that seeing the text and images make them focused. The E-6tslearning method was applied well online by the lecturer. It was done in and out of the class so that the students have more time to read and write. The use of 6TS method was effective to teach reading proven by the increased score of the experimental class. It was increased to 77.1 for the visual; 71.4 for the aural; and 65 for the kinesthetic group.
Capturing a Clear Image on Computer Screen or LCD Screen Directly using Smartphone Camera

Digital technology plays an important role in many fields nowadays especially in Education and Telecommunication. Although for some people it becomes a threat for business and human life socialization but if we do know how to choose good technologies and utilize them for solving daily tasks, then it will give a great of benefit that could support better living and humanization purposes. One of the essential digital technologies that people usually used in their life is digital camera. This thing helps people to take and store pictures in digital information. Digital camera is a wonderful invention that ever made in this world and one of the most valuable knowledge treasures in human life. Recently in education and telecommunication fields smartphone cameras are being used for capturing image, part of a learning process and also to support information exchange and green environment. A problem that people mainly deal with is a blurry photo that makes unsuccessful delivery of information. We seldom and almost never got a clear image when taken photo of information displays on a PC monitor or digital devices LCD screen. The electromagnetic wave from PC monitor and LCD screen affects the inside parts of digital camera so it would not give better image resolution. This simple research focuses on this kind of problem to provide a good solution in capturing clear and best images of PC monitor and LCD screen displays. Based on personal experience and problems met in learning processes, a study literature, analysis and observation have been conducted as part of this research methodology. The output shows a promising result where a clear image of PC monitor and LCD screen display can be captured by a smartphone camera using proper hardware, application, and technique.
Identification of Offensive Content in Memes

Social media is an interactive platform for communication that facilitates the creation or sharing of information, ideas, or other forms of expression among people. With the increased usage of such platforms, automatic detection of multimodal content should be mandatory and so the main focus should be on memes. A meme can be briefly described as an image or video depicting thoughts or ideas of people as well as to express culturally-relevant ideas. Although, memes mostly are captioned photos intended to elicit humor. Since memes are multimodal in nature, posting of irrelevant memes that spread hatred, trolling, cyberbullying is increasing day by day. Awful speech, aggression is explored as single modalities such as text or image. However, combining both the modalities text and image to detect whether it is offensive content or not is a difficult task. Though with the use of a multimodal approach this issue can be dealt with up-to a certain extent.
Multi-human Parsing Based on Dynamic Convolution

Multi-human parsing is attracting more and more attention due to its wide application, which not only needs to differentiate different human instances but also categorizes each pixel within the human. In this work, we improve the performance of the mask prediction branch of Nondiscriminatory Treatment between Humans and Parts for Human Parsing (NTHP), which regards both the humans and parts as objects and directly executes instance segmentation on both of them using the same structure. Specifically, we learn the mask feature and the mask kernel separately, and the mask feature is convolved by the mask kernel to obtain the binary mask prediction. Besides, to obtain translation-variance and better performance, we design a Position-sensitive Global Context (PGC) block and insert it into the mask feature module. Experiments show that our network performs superiorly against state-of-the-art methods on the MHP v2.0 and PASCAL-Person-Part datasets.
Building a Restaurant-Specific Sentiment Lexicon via Probability Theory

Online user reviews written in Portuguese language are widely used by native speakers from several countries for decision-making and a large amount user reviews are available on the Web. However, only few studies on Portuguese sentiment analysis are reported as there is a lack of resources including domain-specific sentiment lexical collections. In this paper, we present an effective method for building a set of lexicons in restaurant domain for Portuguese language. The experiments on a large data collection of user reviews illustrate that our method significantly outperforms several alternative previous approaches of building domain-specific sentiment lexicon.