Reviewerly/exHarmony huggingface dataset:
Locally Joint Sparse Marginal Embedding for Feature Extraction

Classical linear discriminant analysis (LDA) has the limitation that it requires the within-class scatter matrix to be nonsingular so that it can perform eigen-decomposition to obtain optimal solutions. To break through this limitation, many methods based on LDA have been proposed. However, these methods are either sensitive to outliers or lack joint sparsity for effective feature extraction. To release these problems, this paper proposes a locally joint sparse marginal embedding (LJSME) method. LJSME reconstructs the scatter matrices and utilizes the locality graph to weigh each pair of data, such that it is robust to outliers and able to preserve the neighborhood relationship of the data. Moreover, LJSME can easily avoid the small sample-size problem by a maximum margin criterion and obtain joint sparsity for effective feature extraction by using joint sparse regularization. The comprehensive analysis between the proposed LJSME and the related methods is presented, which indicates the advantages of the proposed method. A series of experiments was conducted to evaluate the performance of LJSME when compared with the state-of-the-art methods. The MATLAB code of LJSME can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/TungmeeMo/LJSME.git</uri> .
Mixup Based Privacy Preserving Mixed Collaboration Learning

The amount of high-quality data determines the performance of the deep learning model. In reality, the data is often physically distributed in different organizations, and model averaging can train a deep model on the distributed data, while providing competitive performance compared with training a model on the centralized data. However, it cannot prevent inversion attack, as the intermediate parameters are transmitted during training. Some data enhancement methods, such as mixup, can effectively enhance the data privacy. In this paper, we propose a novel model averaging method combined with mixup, which provides protection against inversion attack. Besides we conduct experiments using state-of-the-art deep network architectures on multiple types of dataset to show that our method improves the classification accuracy of models.
Deep Learning Driven Venue Recommender for Event-Based Social Networks

Event-based online social platforms, such as Meetup and Plancast, have experienced increased popularity and rapid growth in recent years. In EBSN setup, selecting suitable venues for hosting events, which can attract a great turnout, is a key challenge. In this paper, we present a deep learning based venue recommendation system DeepVenue which provides context driven venue recommendations for the Meetup event-hosts to host their events. The crux of the proposed model relies on the notion of similarity between multiple Meetup entities such as events, venues, groups, etc. We develop deep learning techniques to compute a compact descriptor for each entity, such that two entities (say, venues) can be compared numerically. Notably, to mitigate the scarcity of venue related information in Meetup, we leverage on the cross domain knowledge transfer from popular LBSN service Yelp to extract rich venue related content. For hosting an event, the proposed DeepVenue model computes a success score for each candidate venue and ranks those venues according to the scores and finally recommend the top k venues. Our rigorous evaluation on the Meetup data collected for the city of Chicago shows that DeepVenue significantly outperforms the baselines algorithms. Precisely, for 84 percent of events, the correct hosting venue appears in the top 5 of the DeepVenue recommended list.
3D facial expression modeling based on facial landmarks in single image

Facial expression modeling is important for many applications such as human emotional analysis and facial animation. Generally, facial expression modeling from single 2D facial image is difficult. Different head poses and scales of facial data in images affect the accuracy of the modeling results. We propose a new 3D facial expression modeling method which is based on facial landmarks from single image. Using the facial landmarks, expression modeling can be processed in Kendall shape space. The Kendall shape space is mathematic space, the facial expression modeling process in Kendall shape space can be regarded as a geodesic path search between different faces. The modeling result is more accurate. The 3D facial expression modeling result is convenient to obtain from 2D facial image with different head poses. In experiments, we show the 3D facial expression modeling performance by our method, which include expression editing and evaluation in public facial database: JAFFE, LFW, Helen and RAF-DB.
Medical Image Analysis Using Deep Learning: A Systematic Literature Review

The field of big data analytics has started playing a vital role in the advancement of Medical Image Analysis (MIA) over the last decades very quickly. Healthcare is a major example of how the three Vs of data i.e., velocity, variety, and volume, are an important feature of the data it generates. In medical imaging (MI), the exact diagnosis of the disease and/or assessment of disease relies on both image collection and interpretation. Image interpretation by the human experts is a bit difficult with respect to its discrimination, the complication of the image, and also the prevalent variations exist across various analyzers. Recent improvements in Machine Learning (ML), specifically in Deep Learning (DL), help in identifying, classifying and measuring patterns in medical images. This paper is focused on the Systematic Literature Review (SLR) of various microservice events like image localization, segmentation, detection, and classification tasks.
Understanding Cyberbullying on Instagram and Ask.fm via Social Role Detection

Cyberbullying is a major issue on online social platforms, and can have prolonged negative psychological impact on both the bullies and their targets. Users can be characterized by their involvement in cyberbullying according to different social roles including victim, bully, and victim supporter. In this work, we propose a social role detection framework to understand cyberbullying on online social platforms, and select a dataset that contains users' records on both Instagram and Ask.fm as a case study. We refine the traditional victim-bully framework by constructing a victim-bully-supporter network on Instagram. These social roles are automatically identified via ego comment networks and linguistic cues of comments. Additionally, we analyze the consistency of users' social role within Instagram and compare users' behaviors on Ask.fm. Our analysis reveals the inconsistency of social roles both within and across platforms, which suggests social roles in cyberbullying are not invariant by conversation, person, or social platform.
Robust phase unwrapping by probabilistic consensus

Structured light scanning works by projecting over the scene a supplement of controlled information: the captured signal is processed to provide a unique label (namely a code) for each observed point, and then proceed to geometrical triangulation. In phase shift profilometry sinusoidal patterns are projected and each point is labelled according to the observed phase. Then, due to the periodic nature of the signal, a disambiguation method (known as phase unwrapping) is needed. Several unwrapping techniques have been proposed in the literature, since noisy signals lead to inaccuracies in phase estimation. This paper presents a novel phase unwrapping approach based on a probabilistic framework. The method involves the projection of multiple sinusoidal patterns with distinct period lengths, encoding different phase values at each point location. Phase values are then modelled as samples from a Wrapped Gaussian distribution with an unknown mean, determined by the projector code that generated the values. This formulation allows us to robustly perform phase unwrapping via Maximum Likelihood Estimation, recovering code values from the observed phases. Furthermore, the same likelihood function can be exploited to identify and correct faulty unwrappings by gauging mutual support in a spatial neighbourhood. An extensive experimental assessment validates the Gaussian distribution hypothesis and verifies the improvements in coding accuracy when compared to other classical unwrapping techniques.
Binary Image Carving for 3D Printing

Pierced carving is an art form that perforates the given shape by texture patterns or images, widely used in furniture design and decorations. Usually this is manually done by expert designers and carvers, such that the produced carved model is of fine details and structural soundness. The emergence of 3D printing offers substantial opportunities for generalizing manual efforts to an automatic fabricating manner. However, geometric modeling of the image pierced carving is non-trivial due to two challenging problems: how to adapt the given image to variant and limited 3D printing resolutions with high fidelity; how to ensure the printability and physical soundness of the carved model. In this paper, we take binary images as the input and attempt to tackle the pierced image carving problem from the above two folds. Given an arbitrary binary image and a 3D surface model, we formulate the image carving into image details adaption and structure enhancement problems, iteratively optimize the image and carved shape, and obtain a 3D printable and physically sound model. Experimental results show that our approach produces fabricable and visually satisfactory results.
Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering via Item-Variance Weighting

Recommender systems (RS) analyze user rating information and recommend items that may interest users. Item-based collaborative filtering (IBCF) is widely used in RSs. However, traditional IBCF often cannot provide recommendations with good predictive and classification accuracy at the same time because it assigns equal weights to all items when computing similarity and prediction. However, some items are more relevant and should be assigned greater weight. To address this problem, we propose a niche approach to realize item-variance weighting in IBCF in this paper. In the proposed approach, to improve the predictive accuracy, a novel time-related correlation degree is proposed and applied to form time-aware similarity computation, which can estimate the relationship between two items and reduce the weight of the item rated over a long period. Furthermore, a covering-based rating prediction is proposed to increase classification accuracy, which combines the relationship between items and the target user’s preference into the predicted rating scores. Experimental results suggest that the proposed approach outperforms traditional IBCF and other existing work and can provide recommendations with satisfactory predictive and classification accuracy simultaneously.
On the Complexity Reduction of Dense Layers from O(N2) to O(NlogN) with Cyclic Sparsely Connected Layers

In deep neural networks (DNNs), model size is an important factor affecting performance, energy efficiency and scalability. Recent works on weight pruning have shown significant reduction in model size at the expense of irregularity in the DNN architecture, which necessitates additional indexing memory to address non-zero weights, thereby increasing chip size, energy consumption and delays. In this paper, we propose cyclic sparsely connected (CSC) layers, with a memory/computation complexity of O(NlogN), that can be used as an overlay for fully connected (FC) layers whose number of parameters, O(N2), can dominate the parameters of the entire DNN model. The CSC layers are composed of a few sequential layers, referred to as support layers, which result in full connectivity between the Inputs and Outputs of each CSC layer. We introduce an algorithm to train models with FC layers replaced with CSC layers in a bottom-up approach by incrementally increasing the CSC layers characteristics such as connectivity and number of synapses, to achieve the desired accuracy given a compression rate. One advantage of the CSC layers is that there will be no requirement for indexing the non-zero weights. Our experimental results using AlexNet on ImageNet and LeNet300100 on MNIST indicate that by substituting FC layers with CSC layers, we can achieve 10× to 46× compression within a margin of 2% accuracy loss, which is comparable to non-structural pruning methods. A scalable parallel hardware architecture to implement CSC layers, and an equivalent scalable parallel architecture to efficiently implement non-structurally pruned FC layers are designed and fully placed and routed on Artix-7 FPGA and ASIC 65nm CMOS technology for LeNet300100 model. The results indicate that the proposed CSC hardware outperforms the conventional non-structurally pruned architecture with an equal compression rate by ~2× in power, energy, area and resource utilization when running at the same frequency.
Efficiency of object identification for binary images

In this paper, a comparative analysis of the correlation-extreme method, the method of contour analysis and the method of stochastic gradient identification in the objects identification for a binary image is carried out. The results are obtained for a situation where possible deformations of an identified object with respect to a pattern can be reduced to a similarity model, that is, the pattern and the object may differ in scale, orientation angle, shift along the base axes, and additive noise. The identification of an object is understood as the recognition of its image with an estimate of the strain parameters relative to the template.
Perceptual image quality using dual generative adversarial network

Generative adversarial networks have received a remarkable success in many computer vision applications for their ability to learn from complex data distribution. In particular, they are capable to generate realistic images from latent space with a simple and intuitive structure. The main focus of existing models has been improving the performance; however, there is a little attention to make a robust model. In this paper, we investigate solutions to the super-resolution problems—in particular perceptual quality—by proposing a robust GAN. The proposed model unlike the standard GAN employs two generators and two discriminators in which, a discriminator determines that the samples are from real data or generated one, while another discriminator acts as classifier to return the wrong samples to its corresponding generators. Generators learn a mixture of many distributions from prior to the complex distribution. This new methodology is trained with the feature matching loss and allows us to return the wrong samples to the corresponding generators, in order to regenerate the real-look samples. Experimental results in various datasets show the superiority of the proposed model compared to the state of the art methods.
Rethinking a Timeless Titration Experimental Setup through Automation and Open-Source Robotic Technology: Making Titration Accessible for Students of All Abilities

Titration is a common introductory experiment performed across teaching laboratories from high school to university. Yet, its setup remains inaccessible for students with disabilities, denying them the opportunity for experiential learning. Therefore, rethinking such a setup is required to increase laboratory participation for these students. To remove these physical barriers, an automated titration unit based on existing undergraduate titration setups and universal design concepts, coupled with text-to-speech (TTS) capability, is presented. This unit can connect seamlessly via Bluetooth to any mobile platform and takes advantage of the advances in assistive features, such as TTS, on either a tablet or a smartphone. The cost of this unit is between $300 and $500, not including the cost of the smartphone or tablet. However, with the popularity of mobile devices in our society, these devices are becoming highly affordable, and almost every undergraduate is equipped with such a device. Also, with the emphasis on coding literacy, this autotitration setup serves as an excellent example and exercise on design thinking and automation in an undergraduate chemistry lab.
Performance Evaluation of Robust Watermarking Using DWT-SVD and RDWT-SVD

DWT and RDWT are two prominent techniques for digital watermarking. The two key limitations associated with DWT method are: the shift variance property, and the size of original image gets decreased at every decomposition level. These limitations lead to a reduction in data payload offered by the watermarking system. RDWT succeeds in dealing with these limits of DWT. SVD is another technique extensively used in literature to improve upon the performance of digital watermarking. The paper presents two hybridized techniques DWT-SVD and RDWT-SVD. The empirical analysis suggests that RDWT-SVD provides better performance in terms of robustness and imperceptibility.
Exploring the Challenges for Adopting the Cloud PLM in Manufacturing Organizations

Product lifecycle management (PLM) is an engineering management approach which helps organizations to manage product-related technical data for its complete lifecycle. The right product data enables better business functioning in organizations. PLM systems are recent and upcoming information technology tools in manufacturing firms. Although PLM systems were initially introduced as the on-premise tools, the current cloud technology is taking it from organizational boundaries to a global level through the Internet. Most enterprise applications have already started moving to clouds; PLM is also all set to be utilized through clouds. But it brings various challenges during cloud PLM adoption. Hence, this paper aims to empirically investigate the critical challenges that manufacturing firms face while adopting cloud PLM. There is rare academic research in the cloud PLM domain. This paper results in the three most significant challenges viz., “Data security on clouds,” “Lack of trust on services,” and “Lack of customization.” These critical challenges not only help the managers in manufacturing firms in planning to migrate on clouds but also the technical cloud providers to improve the cloud services and systems appropriately in order to increase the compatibility of cloud PLM in firms. The present paper uniquely identifies the critical challenges for cloud PLM adoption in large manufacturing firms.
Anomaly detection method for sensor network data streams based on sliding window sampling and optimized clustering

When detecting abnormal data in the sensor network data stream, it is necessary to accurately obtain the source of the abnormal data. The traditional data stream clustering algorithm has the disadvantages of large clustering information loss and low accuracy. Therefore, this paper proposes a sensor network data stream anomaly detection method based on optimized clustering. Firstly, the proposed sampling algorithm is used to sample the data stream. The sampling result is used as a sample set. Use dynamic data histogram to divide the data dimension into different dimension groups, calculate the maximum entropy division dimension space cluster of each dimension, and aggregate the data of the same dimension cluster into the micro cluster. The abnormality detection of the data stream is realized by comparing the information entropy size of the micro cluster and its distribution characteristics. The experimental results show that the proposed algorithm can improve the accuracy and effectiveness of data stream anomaly detection.
Derivative-Based Steganographic Distortion and its Non-additive Extensions for Audio

Steganography is the art of covert communication, which aims to hide the secret messages into cover medium while achieving high undetectability. To this end, the framework of minimal distortion embedding is widely adopted for adaptive steganography, where a well-designed distortion function is significant. In this paper, inspired by the phenomenon that the modification of audio samples with the low amplitude will be easily detected, a novel distortion is presented for audio steganography. Taking the fragility of the low amplitude audio samples into account, the proposed distortion is inversely proportional to the amplitude. Furthermore, in order to resist the strong steganalysis, the derivative filter is utilized for acquiring the residual of audio, which plays an important role in distortion definition. The experimental results show that the proposed distortion outperforms the state-of-the-art methods defending strong steganalytic methods. To take a step forward, considering the mutual impact caused by embedding modification, the non-additive extensions of the proposed methods are put forward. The extending experiments show that in most cases, the proposed non-additive extensions can achieve higher level of security than the original methods.
Hydrochemical Properties of Groundwater Used for Korea Bottled Waters in Relation to Geology

Bottled waters have been becoming increasingly popular in Korea over the last two decades due to the high demand for safe drinking water. Hydrochemical characterization of groundwater is essential for understanding quality properties of bottled waters. We investigated hydrochemistry of 60 manufacture factories for bottled waters in relation to geology. The mean EC value is highest in groundwaters of Ogcheon metamorphic rocks (213.6 μS/cm) &gt; Precambrian gneiss (177.8 μS/cm) &gt; Cretaceous granite (160.4 μS/cm) &gt; Jurassic granite (131.3 μS/cm) &gt; Quaternary Jeju Island volcanic rocks (99.2 μS/cm). The groundwater types are commonly classified as Ca-HCO3, Ca-Na-HCO3, or Ca-Mg-HCO3 types depending on bed rocks. Based on correlation matrix, the groundwater chemistry was controlled by water–rock interactions. We established relationships between groundwater compositions and bedrock geology in Korea.
Sentiment based approval prediction for enhancement reports

The maintenance and evolution of the software application is a continuous phase in the industry. Users are frequently proposing enhancement requests for further functionalities. However, although only a small part of these requests are finally adopted, developers have to go through all of such requests manually, which is tedious and time consuming. To this end, in this paper we propose a sentiment based approach to predict how likely enhancement reports would be approved or rejected so that developers can first handle likely-to-be-approved requests. This could help the software applications to compete in the industry by upgrading their features in time as per user's requirements. First, we preprocess enhancement reports using natural language preprocessing techniques. Second, we identify the words having positive and negative sentiments in the summary attribute of the enhancements reports and calculate the sentiment of each enhancement report. Finally, with the history data of real software application, we train a machine learning based classifier to predict whether a given enhancement report would be approved. The proposed approach has been evaluated with the history data from real software applications. The cross-application validation suggests that the proposed approach outperforms the state-of-the-art. The evaluation results suggest that the proposed approach increases the accuracy from 70.94% to 77.90% and improves the F-measure significantly from 48.50% to 74.53%.