Reviewerly/exHarmony huggingface dataset:
HDL: Hierarchical Deep Learning Model based Human Activity Recognition using Smartphone Sensors

With the development and popularization of smart-phones, human activity recognition methods based on contact perception are proposed. The smartphones which are embedded with various sensors can be used as a platform of mobile sensing for human activity recognition. In this paper, we propose an automated human activity recognition network HDL with smartphone motion sensor units. The HDL network combines DBLSTM (Deep Bidirectional Long Short-Term Memory) model and CNN (Convolutional neural network) model. The DBLSTM model is first used to model long sequence data and ultimately generate a bidirectional output vector in a abstract way. The DBLSTM model is good at dealing with serialization tasks but poor in the ability to extract features. Hence, the CNN model is then used to extract features from the abstract vector. Finally, the output layer employs a softmax function to classify human activities. We conduct experiments on the Public domain UCI dataset. The experimental results show that the proposed HDL network achieves reliable results with accuracy and F1 score as high as 97.95% and 97.27%. Compared with other networks based on the same smartphone dataset, the accuracy of HDL is higher than S-LSTM and Dropout CNN network by 2.14% and 6.97% respectively.
The investment attractiveness of a regional market of educational services as the basis of its global competitiveness in industry 4.0

Purpose The purpose of this paper is to substantiate the basic role and to develop recommendations for increasing the attractiveness of a regional market of educational services in modern Russia for provision of its global competitiveness in the conditions of industry 4.0. Design/methodology/approach The authors use the method of regression analysis for evaluating the influence of the indicators of investment attractiveness of a regional market of educational services – volume of investments into fixed capital in the sphere of education and state expenditures for financing of education – on the indicator of global competitiveness of this market in the conditions of industry 4.0 – number of foreign students – by the example of regions of the Volga Federal District of the Russian Federation. The empirical basis of the research is statistical materials of the Federal State Statistics Service of the Russian Federation for late 2018 and early 2019. Findings It is proved that investment attractiveness of a regional market of educational services is the basis of its global competitiveness in the conditions of industry 4.0. A barrier on the path of full-scale digital modernization of the system of higher education in modern Russia is the absence of private venture investments, which shows low investment attractiveness of a regional market of educational services. Originality/value It is substantiated that the causes of emergence of the determined barriers have the institutional nature and could be eliminated with the help of the complex of the recommended measures that are connected to usage of the mechanism of public–private partnership for financing of digital modernization education, flexible regulation of investment activities of digital universities, which is selected for each specific project, and coordination of the investment activities of universities at the regional level and creation of a specialized digital portal. Practical implementation of the offered measures will allow achieving significant growth of global competitiveness of a regional market of educational services of modern Russia in the conditions of industry 4.0.
Cross-domain Recommendation with Semantic Correlation in Tagging Systems

The tagging system provides users with a platform to express their preferences as they annotate terms or keywords to items. Tag information is a bridge between two domains for transferring knowledge and helping to alleviate the data sparsity problem, which is a crucial and challenging problem in most recommender systems. Existing methods incorporate correlations extracted from overlapping tags at a lexical level in cross-domain recommendation, but they neglect semantical relationships between different tags, which impairs prediction accuracy in the target domain. To solve this challenging problem, we propose a cross-domain recommendation method with semantic correlation in tagging systems. This method automatically captures the semantic relationships between non-identical tags and applies them to the recommendation. The word2vec technique is used to learn the latent representations of tags. Semantically equivalent tags are then grouped to form a joint embedding space comprised of tag clusters. This embedding space serves as the bridge between domains. By mapping users and items from both the source and target domains into the same embedding space, similar users or items across domains can be identified. Thus, the recommendation in a sparse target domain is improved by transferring knowledge through correlated users and items. Experimental results with three datasets on six cross-domain recommendation tasks demonstrate that the proposed method exploits the semantic links from tags in two domains and outperforms five benchmarks in prediction accuracy. The results indicate that transferring knowledge through tags semantics is feasible and effective.
Multi-turn Intent Determination for Goal-oriented Dialogue systems

Intent determination is one of the main tasks of natural language understanding and dialogue systems, aiming to determine the intent of the user’s input. Incorporating contextual information for intent determination has shown much promise. Recently, memory networks have been used to encode context from the dialogue history at each turn. However, these methods lack general mechanism for encoding intent patterns. In this paper, we investigate the incorporation of intent patterns extracted from regular expression for the multi-turn intent determination task. We propose a novel neural network model with two memories. The first memory encodes context from dialogue history at each turn whereas the second memory contains the information obtained from the regular expression patterns. We evaluate the model on Frames and Key-Value Retrieval datasets, the experimental results demonstrate that encoding intent patterns with memory networks significantly improves the multi-turn intent determination.
Cluster-wise learning network for multi-person pose estimation

In this paper, we propose a cluster-wise feature aggregation network that exploits multi-level contextual association for multi-person pose estimation. The recent popular approach for pose estimation is extracting the local maximum response from each detection heatmap that trained for a specific keypoint type. To exploit more contextual information, our network simultaneously learns complementary semantic information to encourage the detected keypoints subject to a certain contextual constraint. Specifically, our network uses dense and sparse branches to generate paired multi-peak detection heatmaps for clusters of keypoints. To enhance the feature passing through the network, we aggregate information from different branches. The in-branch aggregation enriches the detection features in each branch by absorbing the holistic human region attention. The cross-branch aggregation further strengthens the detection features by fusing global and local context information between dense and sparse branches. We demonstrate competitive performance of our network on the benchmark dataset for multi-person pose estimation.
Aspect-Based Sentiment Analysis with New Target Representation and Dependency Attention

Aspect-based sentiment analysis (ABSA) is crucial for exploring user feedbacks and preferences on produces or services. Although numerous classical deep learning-based methods have been proposed in previous literature, several useful cues (e.g., contextual, lexical, and syntactic) are still not fully considered and utilized. In this study, a new approach for ABSA is proposed through the guidance of contextual, lexical, and syntactic cues. First, a novel sub-network is introduced to represent a target in a sentence in ABSA by considering the whole context. Second, lexicon embedding is applied to incorporate additional lexical cues. Third, a new attention module, namely, dependency attention, is proposed to elaborate syntactic dependency cues between words in attention inference. Experimental results on four benchmark data sets demonstrate the effectiveness of our proposed approach to aspect-based sentiment analysis.
Three Ways of Being Non-Material

Abstract This paper develops a probabilistic analysis of conditionals which hinges on a quantitative measure of evidential support. In order to spell out the interpretation of ‘if’ suggested, we will compare it with two more familiar interpretations, the suppositional interpretation and the strict interpretation, within a formal framework which rests on fairly uncontroversial assumptions. As it will emerge, each of the three interpretations considered exhibits specific logical features that deserve separate consideration.
A multi-view optical technique to extract the operating deflection shapes of a full vehicle using digital image correlation

The automotive and aerospace industries are increasingly using light-weight material to improve vehicle performance while reducing fuel consumption. However, using light-weight material can increase the air-borne and structure-borne noise. Thus, special attention needs to be paid in designing the vehicle body structure and evaluating its dynamics. In order to accomplish this, modal analysis is conventionally used. In this technique, the structure is excited using an impulse hammer or a mechanical shaker, and the response is measured using accelerometers. However, using contact-based transducers can mass-load the structure and can only provide data at a few discrete points. In the last decade, stereo-photogrammetry and three-dimensional digital-image correlation have received special attention in collecting operating data for structural analysis. These non-contact optical techniques provide a wealth of distributed data over the entire structure. However, the stereo-camera system is limited by the field of views of the cameras and can only measure the response on the parts of a structure for which cameras have lines of sight. Therefore, a single pair of DIC cameras may not be able to provide deformation data for an entire structure. A multi-view 3D DIC approach, however, can be used to predict the vibrational characteristics of a full vehicle. A pair of DIC cameras is passed over the entire vehicle to capture the deformation data of each field of view. The measured data includes the geometry and displacement data, which is mapped into a global coordinate system using 3D transformation matrices. The obtained data in the time domain for each field of view is transformed to the frequency domain using a Fast Fourier Transformation (FFT) to extract the operational deflection shapes and resonant frequencies for each field of view. The obtained deflection shapes are scaled and stitched in the frequency domain to extract the operating deflection shapes of the entire vehicle.
Enhancing the Performance of Library Book Recommendation System by Employing the Probabilistic-Keyword Model on a Collaborative Filtering Approach

This paper proposes the probabilistic-keyword CF method for a library book recommendation system. Our focus is to address the sparsity problem commonly occurs on the Collaborative Filtering (CF) approach. The framework of the method consists of four processes. First, building the circulation and keyword matrices respectively based on the book circulation records and the book keyword attribute data. Second, building the keyword model that takes into account both the book circulation records and the book keyword data. Third, building the probabilistic-keyword model that employs a probabilistic technique to calculate the probability of a user to borrow a book conditional to his/her keyword model. Fourth, generating the top-N book recommendations. Experiment results on a library dataset show that our probabilistic-keyword CF method outperforms the traditional user-based and item-based CF methods in terms of all evaluation metrics. This result conjectures that the probabilistic-keyword CF method that employs the probabilistic-keyword model can enhance the recommendation performance and is able to deal with the sparse dataset better than the traditional methods.
Clinical Data Extraction and Normalization of Cyrillic Electronic Health Records Via Deep-Learning Natural Language Processing

A substantial portion of medical data is unstructured. Extracting data from unstructured text presents a barrier to advancing clinical research and improving patient care. In addition, ongoing studies have been focused predominately on the English language, whereas inflected languages with non-Latin alphabets (such as Slavic languages with a Cyrillic alphabet) present numerous linguistic challenges. We developed deep-learning-based natural language processing algorithms for automatically extracting biomarker status of patients with breast cancer from three oncology centers in Bulgaria.We used dual embeddings for English and Bulgarian languages, encoding both syntactic and polarity information for the words. The embeddings were subsequently aligned so that they were in the same vector space. The embeddings were used as input to convolutional or recurrent neural networks to derive the biomarker status of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2.We showed that we can resolve ambiguity in highly variable medical text containing both Latin and Cyrillic text. Final models incorporating both English and Bulgarian syntax and polarity embeddings achieved F1 scores of 0.90 or higher for all estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 biomarkers. The models were robust against human errors originally found in the training set. In addition, such models can be extended for analyzing text containing words not seen during training.By using several techniques that incorporate dual-word embeddings encoding syntactic and polarity information in two languages followed by deep neural network architectures, we show that researchers can extract and normalize parameters within medical data. The principles described here can be used to analyze Cyrillic or Latin mixed medical text and extract other parameters.
Machine Cleaning of Online Opinion Spam: Developing a Machine-Learning Algorithm for Detecting Deceptive Comments

Humans are not very good at detecting deception. The problem is that there is currently no other particular way to distinguish fake opinions in a comments section than by resorting to poor human judgments. For years, most scholarly and industrial efforts have been directed at detecting fake consumer reviews of products or services. A technique for identifying deceptive opinions on social issues is largely underexplored and undeveloped. Inspired by the need for a reliable deceptive comment detection method, this study aims to develop an automated machine-learning technique capable of determining opinion trustworthiness in a comment section. In the process, we have created the first large-scale ground truth dataset consisting of 866 truthful and 869 deceptive comments on social issues. This is also one of the first attempts to detect comment deception in Asian languages (in Korean, specifically). The proposed machine-learning technique achieves nearly 81% accuracy in detecting untruthful opinions about social issues. This performance is quite consistent across issues and well beyond that of human judges.
Analysis of KNN Information Estimators for Smooth Distributions

KSG mutual information estimator, which is based on the distances of each sample to its k-th nearest neighbor, is widely used to estimate mutual information between two continuous random variables. Existing work has analyzed the convergence rate of this estimator for random variables whose densities are bounded away from zero in its support. In practice, however, KSG estimator also performs well for a much broader class of distributions, including not only those with bounded support and densities bounded away from zero, but also those with bounded support but densities approaching zero, and those with unbounded support. In this paper, we analyze the convergence rate of the error of KSG estimator for smooth distributions, whose support of density can be both bounded and unbounded. As KSG mutual information estimator can be viewed as an adaptive recombination of KL entropy estimators, in our analysis, we also provide convergence analysis of KL entropy estimator for a broad class of distributions.
Intrinsic Dimensionality in Combined Visible to Thermal Infrared Imagery

Intrinsic dimensionality (ID) provides an objective metric with which to quantify the number of detectable signal components in a spectroscopic image. Here, we use ID to illustrate the information gained by fusing spectroscopic data acquired over different wavelength ranges. For Cuprite, a mineral-rich site in the Nevada desert, the signal content from visible to short-wave infrared (VSWIR) describes almost entirely different signal content from the thermal infrared (TIR). Due to the extremely limited number of coincident VSWIR and TIR acquisitions previously acquired, this article provides a unique opportunity to quantify the information content gained by adding TIR acquisitions to the more commonly acquired VSWIR data. We highlight the importance of combined VSWIR/TIR imaging for the complete characterization and mapping of mineral and other sites.
AWOSE - A Process Model for Incorporating Ethical Analyses in Agile Systems Engineering

Ethical, legal and social implications are widely regarded as important considerations with respect to technological developments. Agile Worth-Oriented Systems Engineering (AWOSE) is an innovative approach to incorporating ethically relevant criteria during agile development processes through a flexibly applicable methodology. First, a predefined model for the ethical evaluation of socio-technical systems is used to assess ethical issues according to different dimensions. The second part of AWOSE ensures that ethical issues are not only identified, but also systematically considered during the design of systems based on information and communication technology. For this purpose, the findings from the first step are integrated with approaches from worth-centered development into a process model that, unlike previous approaches to ethical system development, is thoroughly compatible with agile methodologies like Scrum or Extreme Programming. Artifacts of worth-centered development called Worth Maps have been improved to guide the prioritization of development tasks as well as choices among design alternatives with respect to ethical implications. Furthermore, the improved Worth Maps facilitate the identification of suitable criteria for system evaluations in association to ethical concerns and desired positive outcomes of system usage. The potential of the AWOSE methodology has been demonstrated in the context of a technical system (smart glasses for cognitive assistance) that supports elderly and people with particular handicaps.
Security Threats and Recent Countermeasures in Cloud Computing

The cloud is an appealing innovation that has driven, in record time, an extraordinary interest. It is right now one of the busiest regions of research in IT, due to its adaptability, vigor, and capacity to altogether lessen the expenses of administrations to clients on the web. Additionally, cloud stores all client information in server farms dispersed the world over. In this manner, security has turned into a foremost worry that anticipates numerous organizations to receive its administrations. Clients should store their information, a few of which are ordinarily secret or individual; in this way, the authors are exceptionally mindful to information honesty and classification amid exchange to a cloud server. This exploration chapter plans to build up an investigation on the different security threats engaged with distributed computing along with their recent countermeasures.
Seq2Seq Deep Learning Models for Microtext Normalization

Microtext analysis is a crucial task for gauging social media opinion. In this paper, we compare four different deep learning encoder-decoder frameworks to handle microtext normalization problem. The frameworks have been evaluated on four different datasets in three different domains. To understand the impact of microtext normalization, we further integrate the framework into a sentiment classification task. This paper is the first of its kind to incorporate deep learning into a microtext normalization module and improve the sentiment analysis task. We show our models as a sequence to sequence character to word encoder-decoder model. We compare four deep learning models for microtext normalization task which further improve the accuracy of the sentiment analysis. Results show that the attentive LSTM and GRU cell both increase the sentiment analysis accuracy in the range of 4%–7% whereas LSTM and CNN with LSTM improve the accuracy in the range of 2%–4%.
Emotion Recognition from 2D Facial Expressions

This work proposes an approach to find and learn informative representations from 2 dimensional gray-level images for facial expression recognition application. The learned features are obtained from a designed convolutional neural network (CNN). The developed CNN enables us to learn features from the images in a highly efficient manner by cascading different layers together. The developed model is computationally efficient since it does not consist of a huge number of layers and at the same time it takes into consideration the overfitting problem. The outcomes from the developed CNN are compared to handcrafted features that span texture and shape features. The experiments conducted on the Bosphours database show that the developed CNN model outperforms the handcrafted features when coupled with a Support Vector Machines (SVM) classifier.
A Detailed Study of Azure Platform &amp; Its Cognitive Services

Windows Azure is the Microsoft's conveyed registering contraption. In this paper, we will learn about the prelude to the Azure framework. The scattered enlisting stage outfits clients with assets and associations for an affiliation. We would in like way talk about azure associations: Register, structuring, Stockpile, Fabric Controller Mesh, Cellphone, Holder, Index, Problem-Solving, A.I + M.L, I.O.T, Combination, Identity, Safety, DevOps, Migrate and The executive's aids. Microsoft's Azure is the standard circled figuring stage which draws in the client to send and work assets with speed of adaptability. Our basic work in this paper is to go up against recognizing confirmation and face attestation utilizing “Face API” where we will use our own one of a kind codes, for example, to perceive and see the powers of the all-inclusive community.