Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis Using Autoregressive Language Modeling and Broad Learning System

In the era of big data, mining the emotional tendency of opinions through natural language processing technology is meaningful for the timely understanding of human behavior data. Nowadays, Generalized Autoregressive Pre-training Language Modeling (XLNet) can not only capture bidirectional contextual knowledge but also learn the word dependency. However, its sentence-level representation didn't take broad features into account. In this paper, we design a novel architecture, called Broad Autoregressive Language Model (BroXLNet), to automatically process sentiment analysis task. BroXLNet integrates the advantage of generalized autoregressive language modeling and broad learning system, which has the ability of extracting deep contextual features and randomly searching high-level contextual representation in broad spaces. We evaluated our algorithm on binary Stanford Sentiment Treebank dataset. Compared with the state-of-the-art methods, e.g., BiLSTM, ELMo, OpenAI GPT, BERT and XLNet, BroXLNet achieved the best result of 94.0% in sentiment analysis task of binary Stanford Sentiment Treebank. The result demonstrated the excellent classifying ability of BroXLNet in sentiment analysis.
Survey of convolutional neural networks for image captioning

Image captioning refers to a machine generatating human-like captions describing the image. With the recent surge of interest in the field, deep learning models have been proved to give state-of-the-art. Unlike most of the models using CNN for image embedding and RNN for language modeling and prediction, there is a small section of models using CNN for language modeling and prediction as well. This paper provides a survey of such models and the advantage that make this section worth exploring.
Sentiment Analysis of Azerbaijani twits Using Logistic Regression, Naive Bayes and SVM

In the work, the roadmap of sentiment analysis of twits in Azerbaijani language has been developed. The principles of collecting, cleaning and annotating of twits for Azerbaijani language are described. Machine learning algorithms, such as Linear regression, Naïve Bayes and SVM applied to detect sentiment polarity of text based on bag of word models. Our suggested approach for data processing and classification can be easily adapted and applied to other Turkish language. Achieved results from different machine learning algorithm have been compared and defined optimal parameters for the classification of twits.
Perspective: The Cyber Frontier and Infrastructure

The accelerating integration of cyber technologies into physical infrastructure systems has radical implications for the operation, management, and vulnerabilities of our critical systems. Viewing the embedding of smart technologies in infrastructure as simply an interconnectedness of systems is insufficient. The acceleration of the coupling may represent a profound shift in the relationships between humans and their services. It lays the groundwork for explosions of artificial intelligence, new capacities for services, radical changes in efficiency, and new vulnerabilities. Yet we continue to approach infrastructure design and management with principles that don't reflect this new paradigm. To frame the challenges associated with modernizing infrastructure for accelerating cyberphysical relationships, we describe the new capabilities and vulnerabilities, and changes in approaches and thinking that are needed for the emerging complexity. We conclude by describing how infrastructure education and training will need to fundamentally shift from a focus on managing complicated physicals systems to working within complex cyberphysical systems that are likely to be governed by software.
Flying LEGO Bricks

In this paper, we present a case study that explores how children could learn to interact with programmable matter. Flying drone swarms enable physical visualizations of complex data and simulation of physical objects and processes, e.g., planetary movements. The swarms can be digitally controlled as an ensemble as a form of (sparse) "programmable matter." We worked with the toy company LEGO®, to design and evaluate a "build and fly" experience with 240 children in a public exhibition. The children decorated a bendable handheld controller with LEGO® bricks and then used this controller to animate the flight of a 10-drone swarm. Results indicate that children enjoyed the constructive play and performance aspects of the system. Four main patterns of player behavior emerged, which we discuss in relation to possible improvements to the system. We provide implications for design of programmable matter systems for supporting child play experiences.
Disease Diagnosis System Using Certainty Factor

The use of traditional medicine has been growing rapidly lately. One of the medicinal plants that have been developed especially in East Kalimantan is Dayak onion plant (Eleutherinepalmifolia (L.) Merr). Dayak onion concoctions have also been used by various groups of Dayaks as an alternative medicine for various diseases, and also to find out the possibility of people who being affected by the disease and how to process Dayak onion ingredients, an disease diagnosis system is needed that is able to diagnose the disease and how to process Dayak onion ingredients to get proper treatment. This study uses ten diseases that can be diagnosed, namely Hemorrhoid Disease, Nephrolithiasis, Diabetes Mellitus, Hypertension, Hepatitis, Tuberculosis, Bronchitis, Sinusitis, Uric Acid, and Rheumatism. The method used is Certainty Factor; the result of this research is a web-based expert system that is used to assist people in diagnosing diseases that can be cured by Dayak Onion.
Minimum adjusted Rand index for two clusterings of a given size

Abstract The adjusted Rand index (ARI) is commonly used in cluster analysis to measure the degree of agreement between two data partitions. Since its introduction, exploring the situations of extreme agreement and disagreement under different circumstances has been a subject of interest, in order to achieve a better understanding of this index. Here, an explicit formula for the lowest possible value of the ARI for two clusterings of given sizes is shown, and moreover a specific pair of clusterings achieving such a bound is provided.
Image Feature Point Matching Based on Improved SIFT Algorithm

An improved SIFT algorithm (SIFT-BRISK) is proposed for the acquisition of image feature points due to the multi-scale, noise, light intensity and rotation between images, which cause image matching is not ideal and feature point matching time is too long. The feature points are collected in the form of DOG pyramid. By comparing the pixel points of the upper and lower layers of the DOG pyramid at multiple scales, we find the extreme points as our feature points. When constructing the feature point descriptors, we abandon the traditional basis. then use the BRISK algorithm to construct concentric circles of different radii in uniform sampling mode as sample points to generate binary descriptors, this way and traditional SIFT descriptor generation Compared to the faster speed, the sample points are more unique because of the different levels of filtering. In the image matching part, the traditional violent matching is matched and innovative. From finding the shortest Hamming distance between the different pictures, the group matching method is used to find the Hamming distance with the nearest set of matching descriptors. The description sub-pair of the shortest Hamming distance greatly reduces the time for matching between binary descriptors and improves efficiency.
Semantic deep cross-modal hashing

Because an increasing number of modality data emerge on the Internet, cross-modal retrieval has become a nontrivial research topic. Furthermore, given the massive amount of cross-modal data and the high dimension of their features, hashing has been explored because it can reduce storage cost and accelerate retrieval speed. In this paper, we put forward a deep cross-modal hashing approach, dubbed semantic deep cross-modal hashing (SDCH). It can make effective use of semantic label information and generate more discriminative hash codes. Specifically, it utilizes the semantic label branches to improve the feature learning part, which can preserve semantic information of the learned features and keep the invariability of cross-modal data. Furthermore, it employs the hash codes learning branches to maintain the consistency of hash codes between different modalities in the Hamming space. Besides, it adopts inter-modal pairwise loss, cross-entropy loss and quantization loss to ensure that the ranking relevance of all similar instance pairs is higher than that of dissimilar ones. Compared to the most advanced method, attention-aware deep adversarial hashing (AADAH), SDCH averagely improves 6.14%, 4.84%, and 3.75% on three widely used datasets, IAPR TC-12, MIR-Flickr 25k, and NUS-WIDE, respectively.
Examining Critical Success Factors of Cloud Computing Adoption

The objective of this article is to identify the critical success factors of cloud computing adoption through the integration of AHP-Structural Mediation Model in MSMEs in India. The article is a mixed method research which includes both qualitative and quantitative study. The analytical hierarchical process (AHP) is a process of ranking the critical success factors based upon the priority and in our study, we have taken nine factors that affect business performance. Then these nine factors are employed to construct a conceptual model to check the final mediation effect of the factors (full and partial mediation) on business performance. The findings in AHP show Trust (T) is having lowest priority and perceived IT security risk (PITR) has the third priority. The structural mediation model shows that trust and PITR do not have any mediation effect on business performance. Rarely any study has integrated AHP and structural mediation to check the importance of critical success factors for Cloud computing adoption in business performance.
Stacking-Based Ensemble of Support Vector Regressors for One-Day Ahead Solar Irradiance Prediction

The integration of Solar Energy in smart grids and many utilities is continuously increasing due to its environmental and economical benefits. However, the uncertainty of available solar energy provides challenges regarding stability in power generation and therefore consistency in production-consumption balance. The availability of an efficient solar energy short-term predictor is found adequate to remedy such type of problems, and therefore permits to enhance the overall system reliability and power generation scheduling. In this paper we survey several ensemble methods based on machine learning named stacking for one-day-ahead predicting of solar radiation intensity. The support vectors regressors are used as base forecasters in an ensemble, and the Multi-layer perceptrons as well as the Decision Trees and K-Nearest Neighbor Regressors are used as meta-learners to combine the predictions of the ensemble in a stacking mode. The sliding window technique is used in a preprocessing phase to reframe the time series predicting problem as a supervised learning problem. The combiners provide a smart weighted averaging of the forecasters' outputs. The solar radiation prediction of the surveyed models has been evaluated and analyzed over an entire year. The stacking based ensemble models outperformed all individual models as well as other combining techniques.
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization

SM1. Extra Experiment.In this section, we present an additional experiment using a three-layer fully connected neural network with 256 neurons in each hidden layer and ReLU activation functions.We train this network on the MNIST dataset.In the previous experiment, we show that adaptive methods give better performance and therefore we omit non-adaptive variance reduced methods from the comparison.We compare SGD with batch-size equal to 1024 with its adaptive counterparts -SCSG, Q-Geom-SARAH and E-Geom-SARAH.For all three adaptive methods, we cap the big batch size B j at 1024 to match SGD batch size.Due to ReLU activations, the loss function is not smooth and smoothness constant L cannot be estimated, thus for each method, we tune step-size individually.Figure SM1 shows that all three adaptive methods outperform SGD baseline.Furthermore, our Q-Geom-SARAH and E-Geom-SARAH exceed the performance of SCSG.We don't perform experiments for the more complex models such as deep networks.These models pose several issues that make variance reduction hard to apply.This includes popular tricks such as dropout, batch norm layers or data augmentation.
Towards Large-Scale Photonic Neural-Network Accelerators

Optical approaches to AI acceleration have gained intense interest recently due to the potentially breakthrough advantages of photonics: high bandwidth, low power consumption, and efficient data movement. We overview leading photonic AI platforms based on beamsplitter mesh networks, weight banks, and photoelectric multiplication. While the theoretical performance can be orders of magnitude beyond current state of the art, practical issues of chip area, input / output, and crosstalk paint a more nuanced near-term picture of photonic AI acceleration. Both fundamental and near-term limitations to energy efficiency are addressed, and bandwidth limitations due to temporal crosstalk are analyzed.
Data-Driven Evaluation Metrics for Heterogeneous Search Engine Result Pages

Evaluation metrics for search typically assume items are homoge- neous. However, in the context of web search, this assumption does not hold. Modern search engine result pages (SERPs) are composed of a variety of item types (e.g., news, web, entity, etc.), and their influence on browsing behavior is largely unknown.
Drones' Face off: Authentication by Machine Learning in Autonomous IoT Systems

Autonomous Internet-of-Things (IoT) are comprised of moving objects such as drones and rovers that use self-control techniques to accomplish a mission while following a path. However, losing control in such systems usually by spoofing their sensors or hijacking with misleading commands can lead to catastrophic safety consequences. In this paper, we close the gap by authenticating the behavior of autonomous IoT systems during operation. In particular, we check the behavior of a moving IoT object, e.g., a drone, by evaluating its time-series telemetry traces during the flight. We examine three different machine-learning algorithms for this purpose, namely, K-Nearest Neighbour (KNN), Support Vector Machine (SVM), and Logistic Regression (LR). Our results show that KNN is the best method of the three selected techniques for authentication in dynamic IoT systems, e.g., drones. We achieved 93.4% in precision rate and 100% recall rate with KNN.
Comparison of Named Entity Recognition Tools Applied to News Articles

Named Entity Recognition in texts is an important natural language processing task. There are many systems to solve this problem. These systems differ in targeting domains, processing methodologies, supported languages and recognized entity types. The presence of a large number of aspects creates difficulties for the user when choosing the appropriate tool for solving a specific problem. The aim of this work is a comparative study of seven publicly available and well-known libraries that can elicit named entities: Stanford NER, spaCy, NLTK, Polyglot, Flair, GATE and DeepPavlov. The article consists of seven sections. The introduction lists the areas of application for the Named Entity Recognition task and the approaches used to solve it. The second section is devoted to a review of works in which comparative studies of existing tools are presented. In the third section, the characteristics of the four text corpora that were used during the experiments are given. The fourth section contains a brief description of the tools selected for research. The fifth section describes the metrics used to evaluate tool performance. The sixth section presents the results of the experiments and their discussion. In conclusion the results of the work are summarized. The results of the study show that for the English language close values of the F1-score for the problem of Named Entities Recognition have the Flair and DeepPavlov libraries. For the Russian language the first place is taken by the DeepPavlov library, significantly surpassing other tools in quality.
TFPN: Twin Feature Pyramid Networks for Object Detection

FPN (Feature Pyramid Networks) is one of the most popular object detection networks, which can improve small object detection by enhancing shallow features. However, limited attention has been paid to the improvement of large object detection via deeper feature enhancement. One existing approach merges the feature maps of different layers into a new feature map for object detection, but can lead to increased noise and loss of information. The other approach adds a bottom-up structure after the feature pyramid of FPN, which superimposes the information from shallow layers into the deep feature map but weakens the strength of FPN in detecting small objects. To address these challenges, this paper proposes TFPN (Twin Feature Pyramid Networks), which consists of (1) FPN+, a bottom-up structure that improves large object detection; (2) TPS, a Twin Pyramid Structure that improves medium object detection; and (3) innovative integration of these two with FPN, which can significantly improve the detection accuracy of large and medium objects while maintaining the advantage of FPN in small object detection. Extensive experiments using the MSCOCO object detection datasets and the BDD100K automatic driving dataset demonstrate that TFPN significantly improves over existing models, achieving up to 2.2 improvement in detection accuracy (e.g., 36.3 for FPN vs. 38.5 for TFPN on COCO Val-17). Our method can obtain the same accuracy as FPN with ResNet-101 based on ResNet-50 and needs fewer parameters.
A novelty detection scheme for rolling bearing based on multiscale fuzzy distribution entropy and hybrid kernel convex hull approximation

Novelty detection as an effective condition detection method is of great necessity for rolling bearing. In this paper, a novelty detection scheme based on multiscale fuzzy distribution entropy (MFDE) and hybrid kernel convex hull approximation (HKCHA) is proposed. First, MFDE is presented as a new entropy to measure complexity, reflect non-linear and non-consistent characteristics, and extract underlying features of vibration signals. Then, the first several MFDE values containing fault information are fed to a novel classifier, based on HKCHA, to detect rolling bearing working status more accurately. Finally, two cases concerning about rolling bearing are conducted respectively to validate the feasibility of proposed scheme, whose results indicate that MFDE is superior to other entropies and HKCHA surpasses other existing detection methods with higher detection accuracy and recall rate.
A vulnerability analysis and prediction framework

As the world approaches a state of greater dependence on technology, many products face increasing threats from malicious attackers who are attempting to take advantage of vulnerabilities in software design. Most of the known vulnerability information is already aggregated, stored in text format, and readily accessible to the public, making such an aggregated database a prime corpus for analysis using data mining methods. Multiple research efforts have been launched in which individual aspects of such cyber-security corpora were analyzed to create taxonomies, assess vulnerability impact, and improve vulnerability detection. However, minimal effort has been committed to analyze cyber-security corpora to explore correlations between vulnerabilities, to study the evolution of a vulnerability from its genesis, and to predict vulnerabilities using multi-faceted algorithms. In this paper, we propose an integrated data mining framework to automatically describe how vulnerabilities develop over time and detect the evolution of a specific vulnerability. Additionally, our framework has a predictive functionality that can be used to predict specific vulnerabilities or to estimate future appearance probabilities of vulnerability groups. In our framework, we use (1) a Topically Supervised Evolution Model (TSEM) that can discover temporal themes from a text corpus, (2) a diffusion-based storytelling technique that sifts through past vulnerability reports to describe how a current vulnerability threat evolved, and (3) several prediction models that use features from a cyber-security corpus to predict vulnerabilities. A series of experiments demonstrate that the proposed framework can not only discover evolutionary patterns in today's most pressing vulnerabilities with a high degree of precision, but it can also predict vulnerabilities with impressive accuracy. As case studies, we also explore the development of vulnerabilities in certain products, providing a unique insight into the correspondence between seemingly unrelated vulnerabilities and the impact of that correspondence on overall software security.
Automatic Speech Recognition for Sanskrit

This paper presents our work on building a speaker independent, large vocabulary continuous speech recognition system for Sanskrit using HMM Toolkit (HTK). To our knowledge, this is the maiden attempt on a Sanskrit automatic speech recognizer. A Sanskrit speech corpus with a vocabulary size of 8370 words is built. The corpus contains orthographic, phoneme and word level transcriptions of 1360 sentences. The speech data were collected from 3 sources: All India Radio website, Indian Heritage Group under C-DAC and Vyoma Linguistic Labs Foundation. Mel Frequency Cepstral Coefficients together with 0th order coefficient and delta and acceleration parameters are used as features. Triphone HMMs, trained using HTK, are used as acoustic model. Bigram probabilities with backoff smoothing are used as language model. Both phoneme and word level recognizers were developed on the Sanskrit corpus. The system provides a word level accuracy of 89.64% and a sentence level correctness of 58.76% on the test set of 274 sentences. A graphical user interface for the speech recognizer is built using Java Swings.
Effects of Depth Information on Visual Target Identification Task Performance in Shared Gaze Environments

Human gaze awareness is important for social and collaborative interactions. Recent technological advances in augmented reality (AR) displays and sensors provide us with the means to extend collaborative spaces with real-time dynamic AR indicators of one's gaze, for example via three-dimensional cursors or rays emanating from a partner's head. However, such gaze cues are only as useful as the quality of the underlying gaze estimation and the accuracy of the display mechanism. Depending on the type of the visualization, and the characteristics of the errors, AR gaze cues could either enhance or interfere with collaborations. In this paper, we present two human-subject studies in which we investigate the influence of angular and depth errors, target distance, and the type of gaze visualization on participants' performance and subjective evaluation during a collaborative task with a virtual human partner, where participants identified targets within a dynamically walking crowd. First, our results show that there is a significant difference in performance for the two gaze visualizations ray and cursor in conditions with simulated angular and depth errors: the ray visualization provided significantly faster response times and fewer errors compared to the cursor visualization. Second, our results show that under optimal conditions, among four different gaze visualization methods, a ray without depth information provides the worst performance and is rated lowest, while a combination of a ray and cursor with depth information is rated highest. We discuss the subjective and objective performance thresholds and provide guidelines for practitioners in this field.