Reviewerly/exHarmony huggingface dataset:
PSent20: An Effective Political Sentiment Analysis with Deep Learning Using Real-Time Social Media Tweets

In the current era of computing, the use of social networking sites like Twitter and Facebook, is growing significantly over time. People from different cultures and backgrounds share vast volumes of textual comments that show their viewpoints on several aspects of life and make them available to all for commenting. Monitoring real social media activities has now become a prime concern for politicians in understanding their social image. In this paper, we are going to analyse the tweets of various social media platforms regarding two prominent political leaders and classify them as positive, negative or neutral using Machine Learning and Deep Learning methods. We have proposed a Deep Learning approach for a better solution. Our proposed model has provided state-of-the-art results using Deep Learning models.
Laser ablation ICP–MS trace element composition of native gold from the Abitibi greenstone belt, Timmins, Ontario

Trace elements in native gold provide a “fingerprint” that tends to be unique to individual gold deposits. Fingerprinting can distinguish gold sources and potentially yield insights into geochemical processes operating during gold deposit formation. Native gold grains come from three historical gold ore deposits: Hollinger, McIntyre (quartz-veined ore), and Aunor near Timmins, Ontario, at the western end of the Porcupine gold camp and the southwestern part of the Abitibi greenstone belt. Laser ablation – inductively coupled plasma – mass spectrometry (LA–ICP–MS) trace element concentrations were determined on 20–25 µm wide, 300 µm long rastor trails in ∼60 native gold grains. Analyses used Ag as an internal standard with Ag and Au determined by a scanning electron microscope with an energy dispersive spectrometer. The London Bullion Market AuRM2 reference material served as the external standard for 21 trace element analytes (Al, As, Bi, Ca, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Pd, Pt, Rh, Sb, Se, Si, Sn, Te, Ti, Zn; Se generally below detection in samples). Trace elements in native gold associate according to Goldschmidt’s classification of elements strongly suggesting that element behaviour in native Au is not random. Such element behaviour suggests that samples from each Timmins deposit formed under similar but slightly variable geochemical conditions. Chalcophile and siderophile elements provide the most compelling fingerprints of the three ore deposits and appear to be mostly in solid solution in Au. Lithophile elements are not very useful for distinguishing these deposits and element concentrations may be controlled by microinclusions such as tourmaline. The deposits show low Ag contents, which is consistent with mesothermal Au. Hollinger and McIntyre deposits have similar trace element abundances with higher Ag, Pb, Bi, Sb, and Pd and generally low Cu; however, Cu concentrations in McIntyre are higher than in Hollinger. In contrast, Aunor shows comparatively low Ag, Bi, Sb, Pb, and Pd and higher Au/Ag consistent with higher hydrothermal fluid temperatures. Gold grain signatures reflect the chemical characteristics of the host rock superimposed on a chemical signature inherited from the mineralizing fluid. The association of Pb–Bi–Cu bearing phases such as galena and chalcopyrite with gold apparently enriched in these elements supports precipitation from hydrothermal fluids carrying the elements.
A Novel Augmented Reality Approach in Oral and Maxillofacial Surgery: Super-Imposition Based on Modified Rigid and Non-Rigid Iterative Closest Point

Background: This paper aim to improve the accuracy of super-imposition and processing time during Oral and Maxillofacial surgery. Methodology: The proposed system consists of Enhanced Tracking Learning Detection (TLD) enhance by an occlusion removal algorithm to remove occlusion in the region of interest. In addition, we propose a Modified Rigid and Non-Rigid Iterative Closest Point (MRaNRICP) for pose refinement. Moreover, this proposed MRaNRICP having a new error metric Boolean function to dictate the Iterative Closest Point (ICP)'s stopping condition. Results: The proposed system using a new error metric being defined as a new MRaNRICP and it gave overlay error from 0.22 - 0.29mm and processing time of 10 - 13 frames per second. Similarly, current system achieved the overlay error from 0.23 - 0.35mm and processing time of 8 - 12 frames per second. Conclusion: This research should reduce the computation time of the TLD algorithm and improve the accuracy of it.
Training Binarized Neural Networks Using Ternary Multipliers

Editor’s note: This article considers the under-investigated problem of training neural networks based on stochastic computing. A new dynamic sign magnitude representation for symbols in ternary format {-1, 0, 1} facilitates learning while retaining SC’s benefits. — <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">John Hayes, University of Michigan</i>
10‐4: <i>Invited Paper:</i> Taking High Dynamic Range Further: Apple Pro Display XDR

Apple Pro Display XDR, with its industry‐leading high dynamic range performance, is presented. Several optical, mechanical and electrical components of the 2D backlight sub‐system, which is key to enabling HDR, will be reviewed, along with algorithm features implemented within a custom‐designed Timing Controller.
Efficient Brain Tumor Segmentation with Dilated Multi-fiber Network and Weighted Bi-directional Feature Pyramid Network

Brain tumor segmentation is critical for precise diagnosis and personalised treatment of brain cancer. Due to the recent success of deep learning, many deep learning based segmentation methods have been developed. However, most of them are computationally expensive due to complicated network architectures. Recently, multi-fiber networks were proposed to reduce the number of network parameters in U-Net based brain tumor segmentation through efficient graph convolution. However, the efficient use of multi-scale features has not been well explored between contracting and expanding paths except simple concatenation. In this paper, we propose a light-weight network where contracting and expanding paths are connected with fused multi-scale features through bi-directional feature pyramid network (BiFPN). The backbone of our proposed network has a dilated multi-fiber (DMF) structure based U-net architecture. First, conventional convolutional layers along the contracting and expanding paths are replaced with a DMF network and an MF network, respectively, to reduce the overall network size. In addition, a learnable weighted DMF network is utilized to take into account different receptive sizes effectively. Next, a weighted BiFPN is utilized to connect contracting and expanding paths, which enables more effective and efficient information flow between the two paths with multi-scale features. Note that the BiFPN block can be repeated as necessary. As a result, our proposed network is able to further reduce the network size without clearly compromising segmentation accuracy. Experimental results on the popular BraTS 2018 dataset demonstrate that our proposed light-weight architecture is able to achieve at least comparable results with the state-of-the-art methods with significantly reduced network complexity and computation time. The source code of this paper will be available at Github.
Named Entity Recognition Algorithms Comparison For Judicial Text Data

The more developed the society, the higher the role of legal relations. Accordingly, the number of court appeals is growing rapidly both from individuals and legal entities. Therefore, in any society the following tasks become extremely important. 1) Reducing the time spent for the legal process, including reducing “errors” at various levels. 2) Reducing the number of appeals to the courts and increasing the role of mediation. The authors have developed a prototype of the "Smart Judge Assistant", SJA, recommender system, which largely solves both tasks. The prototype of the recommender system has already successfully passed the first stage of testing by the Supreme Court of the Republic of Kazakhstan. When developing the prototype, the authors faced various problems related to text recognition. One of them is the problem of data publicity. Objective of the article: One of the main tasks in making documents public, is to hide personal data of parties. In this article we compare several Named Entity Recognition (NER) models to extract personal information from judicial acts (in russian and kazakh languages), such as a person name, an organization name and a location name. Methodology: Four types of algorithms were chosen for training NER models: CRF (Conditional Random Fields), LSTM (Long Short Term Memory) with character embeddings, LSTM-CRF and BERT (Bidirectional Encoder Representations from Transformers). Findings: Models trained by all four algorithms have reasonably high accuracy because of the almost alike structure of source documents in judicial dataset. BERT algorithm shows the best performance out of four algorithms (F1 score: 0.87).
Sparse Feature Learning for Human Activity Recognition

In this paper, we propose an end-to-end deep learning model for human activity recognition. Our model is equipped with sparse learning, which absorbs a greater number of classes without making a significant change in the size of the model while sustaining the accuracy of existing classes. In addition, our model is lightweight than state-of-the-art models as we have utilized FCN-LSTM (Fully convolution network - Long Short-term Memory). Our model predicts human activities such as walking, walking-upstairs, walking-downstairs, sitting, standing, and laying (total 6 classes). For validation of our deep learning model, we have utilized a well-known opensource dataset such as the UCIHAR-dataset, which contains collections of smart-phones data of 30-subjects performing different activities with a smartphone. We evaluated the model using sparse learning and have shown that our model outperforms in learning features with few epochs with high accuracy and compact size, and efficient inference time correspondingly.
Differentially private high dimensional sparse covariance matrix estimation

In this paper, we study the problem of estimating the covariance matrix under differential privacy, where the underlying covariance matrix is assumed to be sparse and of high dimensions. We propose a new method, called DP-Thresholding, to achieve a non-trivial ℓ2-norm based error bound whose dependence on the dimension drops to logarithmic instead of polynomial, it is significantly better than the existing ones, which add noise directly to the empirical covariance matrix. We also extend the ℓ2-norm based error bound to a general ℓw-norm based one for any 1≤w≤∞, and show that they share the same upper bound asymptotically. Our approach can be easily extended to local differential privacy. Experiments on the synthetic datasets show results that are consistent with theoretical claims.
Hybrid Regularization of Diffusion Process for Visual Re-Ranking

To improve the retrieval result obtained from a pairwise dissimilarity, many variants of diffusion process have been applied in visual re-ranking. In the framework of diffusion process, various contextual similarities can be obtained by solving an optimization problem, and the objective function consists of a smoothness constraint and a fitting constraint. And many improvements on the smoothness constraint have been made to reveal the underlying manifold structure. However, little attention has been paid to the fitting constraint, and how to build an effective fitting constraint still remains unclear. In this article, by deeply analyzing the role of fitting constraint, we firstly propose a novel variant of diffusion process named Hybrid Regularization of Diffusion Process (HyRDP). In HyRDP, we introduce a hybrid regularization framework containing a two-part fitting constraint, and the contextual dissimilarities can be learned from either a closed-form solution or an iterative solution. Furthermore, this article indicates that the basic idea of HyRDP is closely related to the mechanism behind Generalized Mean First-passage Time (GMFPT). GMFPT denotes the mean time-steps for the state transition from one state to any one in the given state set, and is firstly introduced as the contextual dissimilarity in this article. Finally, based on the semi-supervised learning framework, an iterative re-ranking process is developed. With this approach, the relevant objects on the manifold can be iteratively retrieved and labeled within finite iterations. The proposed algorithms are validated on various challenging databases, and the experimental performances demonstrate that retrieval results obtained from different types of measures can be effectively improved by using our methods.
Flying Robot Production Program for Comprehensive Understanding of Mechanical and Aerospace Engineering

Experience-based learning programs are very important in universities' mechanical and aerospace engineering departments to understand the relationship between classroom lectures on traditional theories, such as material mechanics, fluid dynamics, dynamics of machinery, and control engineering, and real mechanical systems phenomena. Generally, the real phenomena are defined by interactions of multiple theories. Meanwhile, the use of recent digital manufacturing technologies, such as 3D printers, CAD/CAM, and CAE should be leveraged for more recent educational content and methods. In these circumstances, the Department of Mechanical and Aerospace Engineering at Nagoya University introduced a flying robot production program to help provide a comprehensive understanding of mechanical and aerospace engineering. This paper reports the first year results of the novel program and shares the questionnaire survey that was used to assess the results.
Motion Classification Using CNN Based on Image Difference

The classification of human actions has become an important topics in recent researches. Typically the function of recognition human action is converted to the function of classifying the image that represents the person’s motion. In this paper to classify the human motion the Convolution Neural Network (CNN) has been used to extract features by convolutional layers and in fully connected layer Softmax classifier is used to classify the motion. This method evaluate the differences between two sequences frames and this frame differences is used for training and testing in CNN. The propose system has been applied on three databases KTH, Ixmas and Weizmann. The results of experiments achieved accuracy 98.75% with KTH, 92.24% with Ixmas and 100% with Weizmann database.
WITHDRAWN: A proposal on semantic enhanced web mining based deep neural network framework

The Web is a series of documents on one or more web servers that are hyperlinked. The word “Web mining” is described as “quarrying (mining) of the World Wide Web to extract useful information and knowledge about user queries, content and web structure.” It is possible to apply these query finding patterns (finding relative information over the Web) to well-formed, semi-structured or unstructured data. Web Mining helps to translate readable human content over WWW to readable computer semantics. This paper introduces neural network concepts for semantic enhancement in web mining.
Fault Detection and Prediction Method of Satellite Senor In-orbit Data Based on SVM

Satellite sensor is one of the most important devices for spacecraft flight control, which is used to gather the in-orbit flight control data. The timeliness and accuracy of the sensor fault detection determined the success or failure of the mission. The traditional data monitoring strategy is triggering the alarm by setting the upper and lower threshold value of the telemetry data. This paper tries to achieve a fault detection and prediction method of satellite sensor through the process of classifier training using the normal and abnormal in-orbit simulated data based on SVM. During the training and detection of the sample data, multi-parameter has been adopted, the two-class classification and the one-class classification algorithm has been compared. According to the results, the method based on SVM was demonstrated effectively.
Trading off data resource availability and privacy preservation in multi-layer network transaction

The big data market solves the problem of the effective utilization of data through treating data as the circulating commodity in the market. The existing body of research on the big data market suggests that either improving the availability of published data or protecting sensitive information when trading data s the current mainstream topic. To date, the balancing the availability and privacy of the released dataset is a gradually emerging challenge. Unfortunately, there are few studies that have concentrated on the combination of the two points, which is more in line with the actual trading demands and data interaction patterns. Our paper proposes a novel mechanism called Differential Privacy Data Trading (DPDT) mechanism by introducing the differential privacy into the data trading process. Our DPDT mechanism can meet the actual usage requirements of data consumers for the released dataset while ensuring privacy. In short, the DPDT mechanism balances availability and privacy by generating a private synthetic dataset whose accuracy is determined by the data consumer. It is customized for the big data market by improving appropriate synthetic dataset privacy releasing techniques. In addition, DPDT can calculate the corresponding security payment costs depending on the different accuracy of the released dataset by correlating the accuracy, privacy budget, and payment. We instantiate the DPDT with real-world data and the experimental results verify the proposed mechanism is feasible and robust. Our analysis and discussion results reveal that DPDT achieves the trade-off between availability and privacy of the released dataset during data trading.
SIFT Features for Deep and Variational Autoencoders: A Performance Comparison

Object detection tasks widely use the Scale Invariant Feature Transform (SIFT). SIFT key-points are extracted from model images of an object and are used to form descriptors. To achieve an object's existence from a query image, we find the candidate's matching features based on their feature vector's Euclidean distance. The goal is to maximize the SIFT features so that classification can be more accurate and reliable. In this work, we compare the encoding efficiency using the number of SIFT matches found in the images of Deep Autoencoder with that of Variational Autoencoder.
Ways to win: strategic choices, institutions and performance in sub-Saharan Africa

Purpose The purpose of this study is to analyse the effects of market-based and nonmarket-based strategies on firm performance in African countries. This study also investigates host country institutions' effect on the relationship between firm strategies and performance in these countries. Design/methodology/approach Data of 1,276 firms in five African countries were obtained from two different sources: The World Bank Enterprise Database and The Global Competitiveness Report. Two-stage least squares regression was applied. Findings Both market-based strategies and corporate political activity (CPA)improve firm performance in the African countries included in the analysis. Institutional development also has a direct positive impact on firm performance. However, the effect of CPA weakens as the host country shifts towards more efficient, market-oriented institutions. Furthermore, the results show that local African firms benefit more from institutional development than foreign firms. Originality/value The paper confirms and extends our understanding of the dynamic fit between institutions and strategy by highlighting the moderating role of institutional development on CPA and market-based strategies in enhancing firm performance.
Performance Analysis of Telugu Characters Using Deep Learning Networks

Character recognition is an active research area for recognizing handwritten or optical characters using computers. The number of languages in the world is about 7000, which demands different technologies to deal with the characters. Different tools help to get the scanned documents in the form of images and then technologies are used to recognize the characters. In this paper, a convolution neural network (CNN) model is proposed for recognizing 52 Telugu characters. The performance of the proposed model has been evaluated by optimizers such as Adam, Adagrad, Adadelta, and Stochastic Gradient Descent. The preprocessing step is also supported in improving the accuracy. The CNN model is compared with VGG-16 and the accuracy obtained is less. A highest accuracy of 90.8% is achieved by Adam for the proposed CNN model.
Unsupervised Domain Adaptive Re-Identification with Feature Adversarial Learning and Self-similarity Clustering

In this paper, we propose a novel unsupervised domain adaptation re-ID framework by fusing feature adversarial learning and self-similarity clustering. Different from most of the existing works which only regard the source domain data as network pretraining data, we use the source domain data both in network pretraining and finetuing stage. Concretely, we construct an feature adversarial learning module to learn domain invariant feature representations. The feature extractor network is optimized in an adversarial training manner through minimizing the discrepancy of feature representations between source and target domains. To further enhance the discriminability of the feature extractor network, we design the self-similarity clustering module to mine the implicit similarity relationships among the unlabeled samples of the target domain. By unsupervised clustering, we can generate pseudo-identity labels for the target domain data, which are then combined with the labeled source data together to train the feature extractor network. Additionally, we present a relabeling algorithm to construct correspondence between two groups of pseudo-identity labels generated by two iterative clusterings. Experimental results validate the effectiveness of our method.
The Waikato Open Source Frameworks (WEKA and MOA) for Machine Learning Techniques

Abstract WEKA and MOA are a free open-source software project specific for data mining and data stream mining, respectively. They are written in Java and developed at the University of Waikato, New Zealand. This research paper presents a comprehensive study of both consists of algorithms, evaluation, visualization, correlation between WEKA and MOA, workflow of implementation, and the classification accuracy.
A Study of Segmentation Techniques to Detect Leukaemia in Microscopic Blood Smear Images

In medical image processing, the segmentation of the image is considered to be a vital stage and is effectively used to extract the region of interest. Automated diagnosis of leukaemia is highly associated with the accurate segmentation of the cell nucleus. The purpose of this paper is to review and analyze literature related to some of the major segmentation techniques used in the field of Acute lymphoblastic leukaemia (ALL) detection. This paper presents an overview of segmentation methods along with the experimental results of six implemented methods and highlights some of the advantages and disadvantages of implemented segmentation techniques.