Reviewerly/exHarmony huggingface dataset:
Word Embeddings and Deep Learning for Spanish Twitter Sentiment Analysis

Spanish is the third language most used on the internet. However, Natural Language Processing research in this language is still far below the level of other languages like English. The aim of this paper is to fill this gap in the literature and to provide a comprehensive assessment of Deep Learning applied to Spanish sentiment analysis. We focus on the polarity detection task which, in the context of Spanish Twitter messages, remains as a challenging task. To do so, we explore the combination of several Word representations (Word2Vec, Glove, Fastext) and Deep Neural Networks models. Unlike poor performance obtained by previous related work using Deep Learning for Spanish sentiment analysis, we show promising results. Our best setting combines three word embeddings representations, Convolutional Neural Networks and Recurrent Neural Networks. This setup allows us to obtain state-of-the-art results on the TASS/SEPLN 2017 Spanish Twitter benchmark dataset, in terms of accuracy and macro F1-measure.
Secure IoT Supply Chain Management Solution Using Blockchain and Smart Contracts Technology

Supply Chain has played a key role in developing the corporate processes and significantly improved the way they interconnect one to each other. In the last decades, the Supply Chain Management has attracted the attention of academic research, in the scope of improving or creating better solution for coordinating the flaws that undergo a supply chain. Last research in the area have focused on the integration properties of the Supply Chains. This is due to the globalization effects over the worldwide economy. The study of the integration creates the knowledge and the infrastructure needed for the networks of interdependent corporations, in order to provide a more dynamic demand and supply of products and services. The paper presents a solution by using the block chain and smart contracts technologies within IoT – Internet of Things environment.
A Threat Intelligence Tool for the Security Development Lifecycle

Threat intelligence is critical when executing a well-informed Security Development Lifecycle (SDL). However, the lack of threat intelligence tools focusing on SDL is a known issue within the security community. To address this shortcoming, we present "Threat Miner for SDL" to automate the process of mining open source threat information sources to deliver product specific threat indicators designed to strategically inform the SDL while continuously monitoring for disclosures of relevant potential vulnerabilities during product design, development, deployment and beyond.
SGNet: Design of Optimized DCNN for Real-Time Face Detection

This paper proposes to optimize the deep convolution neural networks for real time video processing on detecting faces and facial landmarks. For that, we have to reduce the existing weight size and duplication of weight parameters. By utilizing the strengths of the two previous powerful algorithms which have shown the best performance, we overcome the weakness of the existing methods. Instead of using the old-fashioned searching method like sliding window, we propose our grid-based one-shot detection method. Furthermore, instead of forwarding one image frame through a very deep CNN, we divide the process into 3 stages for incremental detection improvements to overcome the existing limitation of grid-based detection. After lots of experiments with different frameworks, deep learning frameworks are chosen as the best for integration of 3-stage DCNN. By using transfer learning, we can remove the unnecessary convolution layers in the existing DCNN and retrain hidden layers repeatedly and finally succeed in obtaining the best speed and accuracy which can run on the embedded platform. The performance to find small sized faces is better than YOLO v2.
Geochemical characteristics of the Carboniferous-Permian source rocks in Western Henan, China

55 source rock samples in the Carboniferous-Permian strata, southern Northern China basin were analyzed in this paper to obtain the sedimentary characteristics, organic matter abundance, kerogen type and thermal maturity and the unconventional oil and gas prospects. The results demonstrated that the source rocks were all dominated by the evolution of the sedimentary environment. High thermal evolution resulted in low S1, whereas poor preservation caused by strong tectonic activities resulted in low S2. The units of the southern North China Basin with weak tectonic activity and good preservation conditions may be the main areas for future unconventional gas exploration.
Dynamic human object recognition by combining color and depth information with a clothing image histogram

Human object detection, tracking, and recognition have applications in many areas, such as in the development of assistance robots and intelligent monitoring systems. The emergence of an RGB-D camera, namely the Kinect v2, has simplified the process of human object detection and tracking. Color space methods are dependent on lighting conditions. Because skeleton-tracking algorithms are based on depth images, they are light invariant relative to color space methods. However, skeleton information may sometimes be incorrect or become lost. An algorithm for human-target recognition is thus required. Therefore, this study proposes a human-target tracking and recognition system combining RGB images, depth images, body index, and skeleton information. The system first extracts the color information of five body parts (two upper arms, the torso, and two thighs) using color, depth, and skeleton information. The system then analyzes the color information using a mixed nine-dimensional histogram and single-color analysis method. The algorithm also includes overlap detection during the process of human-target tracking to prevent misidentification caused by occlusion. To test the proposed system, various scenarios were carefully designed to simulate the extremely complex environmental changes characteristic of the real world. Furthermore, the dynamic statistical method of event statistics was used to collect results. Experiments revealed that the proposed method is robust under varying lighting conditions and increases the success rate for individuals wearing similar clothing with monochrome colors.
Web-Induced Heterogeneous Transfer Learning with Sample Selection

Transfer learning algorithms utilize knowledge from a data-rich source domain to learn a model in the target domain where labeled data is scarce. This paper presents a novel solution for the challenging and interesting problem of Heterogeneous Transfer Learning (HTL) where the source and target task have heterogeneous feature and label spaces. Contrary to common space based HTL algorithms, the proposed HTL algorithm adapts source data for the target task. The correspondence required for aligning the heterogeneous features of the source and target domain is obtained through labels across two domains that are semantically aligned using web-induced knowledge. The experimental results suggest that the proposed algorithm performs significantly better than state-of-the-art transfer approaches on three diverse real-world transfer tasks.
Exploration of virtual human running based on CCD algorithm

Summary To make a more thorough understanding of human movement, the research of virtual human running based on the CCD (Cyclic‐Coordinate Descent) algorithm was focused on. First of all, the CCD algorithm, the rotation range of the human joint, the running cycle, the key moment of running movement, and the decomposition of running movement were introduced in detail. Then, the inverse kinematics based on the CCD algorithm was solved, the inverse kinematics in the running was solved, and the motion simulation of the upper body and the specified path were carried out. Finally, the simulation results were analyzed. The results showed that the parameters such as the percentage of the support period, the vacant height, the running level, the step frequency, and the frame rate were adjusted by the interactive mode. In a word, it makes the running posture generated diversified and more characteristic.
The Matthew Effect in Computation Contests: High Difficulty May Lead to 51% Dominance?

We study the computation contests where players compete for searching a solution to a given problem with a winner-take-all reward. The search processes are independent across the players and the search speeds of players are proportional to their computational powers. One concrete application of this abstract model is the mining process of proof-of-work type blockchain systems, such as Bitcoin. Although one's winning probability is believed to be proportional to his computational power in previous studies on Bitcoin, we show that it is not the case in the strict sense.
Chinese Text Classification Based on Character-Level CNN and SVM

With the rapid development of the Internet, the high dimensional text data has increased rapidly. How to build an efficient and extensible text classification algorithm has become a hot topic in the field of data mining. Aiming at the problems of high feature dimension, sparse data and long computation time in traditional SVM classification algorithm based on TF-IDF (Term Frequency-Inverse Document Frequency), we propose a novel hybrid system for Chinese text classification: CSVM, which is independent of the hand-designed features and domain knowledge. Firstly, the encoding words are done by constructing a text vocabulary of size m for the input language, and then quantize each word using 1-of-m encoding. Secondly, we exploit the CNN (Convolutional Neural Network) to extract the morphological features of character vectors from each word, and then through large scale text material training the semantic feature of each word vectors are be obtained the semantic feature of each word vectors. Finally, the text classification is carried out with the SVM multiple classifier. Testing on a text dataset with 10 categories, the experimental results show that the CSVM algorithm is more effective than other traditional Chinese text classification algorithm.
Earthquake location by a sparse seismic network

Hypocentral locations, and depths in particular, are affected by large uncertainties in situations where the available seismic network is sparse and with a small number of local stations recording Pg phases as first arrival (Pg phase is direct wave in the upper crust). In this study, we consider the variability of locations because of the errors associated with arrival time picks, analyzing how this variability, for a constant standard deviation in arrival times, depends on the azimuthal distribution of stations and the number of available Pg phases in the input dataset. Our analysis was carried out on real cases and confirmed the importance of including local stations in the earthquake location process, quantifying how the removal of an increasing number of local stations increased the dispersion of hypocenters.
Sparse coding based multi-option semantic composition

As a classic technique in the field of computer vision, image composition has been widely used and recognized. It could composite new regions extracted from candidate images into target images, which could achieve realistic composited results. However, in the process of composition, it is difficult for traditional composition methods to select enough semantically valid candidate images. Moreover, imperfect composition techniques apparently reduce the effectiveness of composition. Aiming at these problems, we proposed a sparse coding based candidate image selection model that could offer more semantically valid candidate images. At the same time, optimized composition techniques were also used for improving the composition quality. Finally, compared with traditional models, adequate experimental results using a large number of images indicate that our model not only could retrieve more effective candidate images but also could achieve consistent composition results.
Transformation of Petri Nets into Context-Dependent Fusion Grammars

In this paper, we introduce context-dependent fusion grammars as a new type of hypergraph grammars where the application of fusion rules is restricted by positive and negative context conditions. Our main result is that Petri nets can be transformed into these grammars such that the reachable markings are in one-to-one correspondence to the members of the generated language. As a corollary, we get that the membership problem for context-dependent fusion grammars is at least as hard as the reachability problem of Petri nets.
Three-dimensional measurement with reflection suppression based on high-dynamic range images

To deal with the three-dimensional (3-D) point cloud loss caused by object reflection in the active fringe projection 3-D measurement, an active reflection suppression method for 3-D measurement is proposed. The method employs high-dynamic range images obtained by multiple exposure image fusion and a three-wavelength phase-shift profilometry method to achieve high-precision 3-D measurement of reflective objects. Experimental results show that, compared to traditional 3-D measurement methods, the proposed one can more effectively handle reflections thereby avoiding 3-D point cloud loss in the measurement of reflective objects.
An Improved Naive Bayes Classifier on Imbalanced Attributes

Data plays a major and prominent role in this modern information era. Classification is a data mining task to discover the hidden information from large amounts of data stored in the repository. This process becomes extremely challenging in case of highly imbalanced dataset. Prediction from imbalanced attributes cannot be done accurately in the following case: During the training phase, the categorical variable is not observed but the test phase encounters the categorical variable and hence it assigns zero probability which leads to false prediction. To overcome this scenario, this article proposes a novel smoothing technique called optimized laplace smoothing estimation. This technique adds a bias value function to improve the accuracy of imbalanced attributes. For example, a child dataset has more attributes and the classification model is used to predict the child weight. Some of the attribute values may not be present in the child dataset due to which Naive Bayes assigns a zero for incomplete and an empty attribute. This leads to inaccurate prediction. In such cases, Naive Bayes can be further tuned by adding some new parameters as well as altering the existing optimization method. Experimental analysis shows that this novel smoothing technique enhances the classification accuracy by means of accurate predictions for imbalanced attributes.
DNN-based Speaker-adaptive Postfiltering with Limited Adaptation Data for Statistical Speech Synthesis Systems

Deep neural networks (DNNs) have been successfully deployed for acoustic modelling in statistical parametric speech synthesis (SPSS) systems. Moreover, DNN-based postfilters (PF) have also been shown to outperform conventional postfilters that are widely used in SPSS systems for increasing the quality of synthesized speech. However, existing DNN-based postfilters are trained with speaker-dependent databases. Given that SPSS systems can rapidly adapt to new speakers from generic models, there is a need for DNN-based postfilters that can adapt to new speakers with minimal adaptation data. Here, we compare DNN-, RNN-, and CNN-based postfilters together with adversarial (GAN) training and cluster-based initialization (CI) for rapid adaptation. Results indicate that the feedforward (FF) DNN, together with GAN and CI, significantly outperforms the other recently proposed postfilters.
Health360: An Open, Modular Platform for Multimodal Data Collection and AAL Monitoring

Health and care features a strong need for computerised tools providing support in daily life activities, especially among elderly people. Platforms capable of safely collecting several data can represent a useful add-on to the home-care of elderly people, paving the way for a patient empowerment, improving their quality of life without needing for recurrent medical examinations at clinics. Here, we present an open, modular platform capable of collecting multimodal (anamnestic, clinical, etc.) data, gathered through different approaches, including questionnaires, medical examinations, and wearable sensors, usable by the caregiver to remotely check the health status of a patient, without needing for frequent recalls at clinics. The extensive use of this platform will allow the patient performing an automated self-monitoring, eventually with the help of a caregiver directly at home, enabling remote data sharing with General Practitioners, reducing the needs for clinics admittance, without sacrificing the quality of care.
A Novel Framework to Localize Moving Targets in Video Surveillance Systems via Spectral Clustering

Accurately localizing moving targets in each individual frame of video clips captured via an ordinary surveillance system is still challenging nowadays, provided the fact that many serious problems, including sudden illumination changes, partial or full obstacles, rigid or non-rigid targets’ shape transformations, etc., are still hard to be tackled at the current stage. In this study, a new semi-supervised offline incremental-learning framework via spectral clustering is introduced to solve the above moving target localization problem. The framework is composed of two steps. First, a computer-user interaction step is enabled on the first frame of a video clip, in order to allow the ending user to delineate a region-of-interested enclosing the interested target. Positive and negative samples are automatically determined via a stratified random sampling strategy therein. Second, a spatially-weighted metric-based measure is defined to reveal the similarity between pair-wise pixels. This similarity measure is then determined via a supervised spectral clustering technique algorithmically. The newly introduced framework is evaluated using a database composed of over 1000 frames, with comparisons towards 6 other well-established approaches for moving targets localization. Promising outcomes are demonstrated and the superiority of the new framework is suggested from the statistical point of view.
Related Terms Extraction from Arabic News Corpus Using Word Embedding

Different techniques are used in text mining to analyze data, extract knowledge, information and relations. We aim in this work to extract related terms for specific keywords. In the first step, we extract Arabic keywords from news articles titles using the TF-IDF terms weighting measure. In the next step, we extract the related terms, from both titles and main texts, using Word2Vec model as a word embedding technique. In order to evaluate our proposed approach, we compute the precision values of the extracted terms that are present in Wikipedia articles. The experiments results perform better for the extracted terms from the articles main texts than titles and the international news category has the highest precision value.
Binary Data Analysis for Source Code Leakage Assessment

Side Channel Analysis (SCA) is known to be a serious threat for cryptographic algorithms since twenty years. Recently, the explosion of the Internet of Things (IoT) has increased the number of devices that can be targeted by these attacks, making this threat more relevant than ever. Furthermore, the evaluations of cryptographic algorithms regarding SCA are usually performed at the very end of a product design cycle, impacting considerably the time-to-market in case of security flaws. Hence, early simulations of embedded software and methodologies have been developed to assess vulnerabilities with respect to SCA for specific hardware architectures. Aiming to provide an agnostic evaluation method, we propose in this paper a new methodology of data collection and analysis to reveal leakage of sensitive information from any software implementation. As an illustration our solution is used interestingly to break a White Box Cryptography (WBC) implementation, challenging existing simulation-based attacks.
RGN-Defense: erasing adversarial perturbations using deep residual generative network

In recent years, deep neural networks have achieved great success in various fields, especially in computer vision. However, recent investigations have shown that current state-of-the-art classification models are highly vulnerable to adversarial perturbations contained in the input examples. Therefore, we propose a defense methodology against the adversarial perturbations. Prior to a targeted network, adversarial perturbations are erased or mitigated via a deep residual generative network (RGN). Through adopting an auxiliary network VGG-19, the RGN is trained toward optimization of a joint loss, including low-level pixel loss, middle-level texture loss, and high-level task loss, thereby the restored examples are highly consistent with the original legitimate examples. We call our proposed defense based on RGN as RGN-Defense. It is an independent defense module that can be flexibly integrated with other defense strategy, for example, adversarial training, to construct a more powerful defense system. In the experiment, we evaluate our approach on ImageNet, and the comprehensive results have demonstrated robustness of RGN-Defense against current representative attacks.
The Significance of Interdisciplinary Projects in Becoming a Research Engineer

The chapter presents the importance of interdisciplinary projects for modern engineer education. The components of the educational process influencing the quality of engineer education are allocated. The importance of the “learning through research” principle is noted. Examples of projects centered on the creation of information-analytical systems in various subject domains, and also, professional tasks which were solved by students are given. The conclusion is drawn that experience in the development of systems in one area can effectively be used in the development of systems in another area. The features of the project centered on the creation of a remote monitoring system of the human state, and the protection of the transferred physiological data are considered.
Design of Knowledge Map Construction Based on Convolutional Neural Network

With the advent of the Web era, data has exploded, with tens of thousands of textual data being generated every day. Traditional text sentiment analysis methods are mainly based on lexicon and machine learning-based methods. These methods show certain limitations as the data size increases. Here, we propose a new knowledge map design method based on convolutional neural network. For the academic literature data crawled from HowNet and Baidu Academic Website with the theme of computer, the corresponding ontology database, the fusion application of multiple data sources, and the mapping of different ontology libraries through data fusion are constructed for data sources in different fields. The global ontology library then uses the entity alignment and entity link methods for knowledge acquisition and fusion. Finally, the convolutional neural network is used for training and testing. The experimental results show that the subject search task can not only obtain the book through the convolution network, the effective academic literature in the question bank can also be used to obtain the relevance of the keyword in the search results, which verifies the effectiveness of the method.