Reviewerly/exHarmony huggingface dataset:
Geo-tag’s Visual Data for Earthquake Mitigation

In our daily lives, society always live up side by side with disaster. The causes of a disaster can possibly come from either (or both) natural or human & technological factors. Several studies have stated that : improving the disaster preparedness will reduce the impact / risk of disasters. Therefore, an ICT ecosystem will be developed to increase community preparedness in facing earthquakes. Earthquake is a natural phenomenon which is pretty difficult to predict, so that one of the actions that can be taken before a disaster occurs is to increase awareness, for example by analyzing the readiness of buildings to experience earth shocks.In this paper, we will present the process of analyzing textual and image data from the condition of the building, then classifying it based on the level of readiness of the building to receive shocks caused by earthquakes. The classification process will be executed through a perceptual assessment carried out by both lay users and experts in the field of building construction and disaster. The classification results will be the input data for the process of determining the location of the building by applying the Deep Convolutional Neural Network approach. The output of this identification process is a geo-tagged photos that can be used as a reference so that disaster mitigation can be carried out by related parties.
Binary multi-layer classifier

Binary decision trees (BDTs), where each node of the tree is split into two child nodes, are among the most popular classifiers. An alternative type of classification tree, namely, the multi-layer classifier (MLC), has been proposed to split the parent node into 1 or 2 classified child nodes and an unclassified child node at each layer. In contrast to the nodes in a BDT, only the unclassified node of the MLC can be further split. Though the use of MLC is plausible, it has not been widely applied due to a lack of theoretical investigations and thorough tests with real datasets. In this study, we attempt to lay a solid theoretical foundation for a simple MLC with a binary split, i.e., a split into only two nodes, namely, one classified and the other unclassified. Based on the theories developed, we propose a variance-ratio algorithm to construct tree models. The proposed algorithm is thoroughly tested with 40 datasets from well-known repositories. The results indicate that binary MLC models are easier to interpret than other models, achieve significantly better average classification performance than seven other BDT methods and construct fewer tree nodes than most other methods except CTree and NBTree.
Research on Full Traversal Path Planning based on Improved Reciprocating Algorithm

In recent years, sweeping machines, as one of the household products in artificial intelligence applications, have continuously impacted our lives. However, there are common problems such as incomplete cleaning caused by defective path design and difficulty in escape from the “dead point” which leads to low work efficiency, such as traditional reciprocating cleaning algorithms. As a practical intelligent sweeping robot, it not only completes the most basic obstacle avoidance and sweeping tasks, but also needs to achieve the effect of rapid and full coverage of the cleaning area without dead ends, and comprehensively improve work efficiency. In response to these shortcomings of the traditional reciprocating method, this topic proposes a combination of an improved reciprocating full traversal path planning algorithm and the A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm, and the effectiveness of the improved algorithm in the path planning of the sweeping robot is verified through Matlab simulation experiments.
imdpGAN: Generating Private and Specific Data with Generative Adversarial Networks

Generative Adversarial Network (GAN) and its variants have shown promising results in generating synthetic data. However, the issues with GANs are: (i) the learning happens around the training samples and the model often ends up remembering them, consequently, compromising the privacy of individual samples - this becomes a major concern when GANs are applied to training data including personally identifiable information, (ii) the randomness in generated data - there is no control over the specificity of generated samples. To address these issues, we propose imdpGAN-an information maximizing differentially private Generative Adversarial Network. It is an end-to-end framework that simultaneously achieves privacy protection and learns latent representations. With experiments on MNIST dataset, we show that imdpGAN preserves the privacy of the individual data point, and learns latent codes to control the specificity of the generated samples. We perform binary classification on digit pairs to show the utility versus privacy trade-off. The classification accuracy decreases as we increase privacy levels in the framework. We also experimentally show that the training process of imdpGAN is stable but experience a 10-fold time increase as compared with other GAN frameworks. Finally, we extend imdpGAN framework to CelebA dataset to show how the privacy and learned representations can be used to control the specificity of the output.
IOT Search Engines: Study of Data Collection Methods

Internet of Things (IOT) presents the trend in the world of computing science, which occupies a great interest in research and industry, offers a new field of creativity and innovation and can be applied in different areas such as education, agriculture, and industry. The number of IOT solutions developed attained 25.2 billion according to study of GSMA, their deployment in different areas improve the quality of life and will make the environment intelligent and conducive to all human activity. Searching information about connected devices and information about IOT topic in the world became possible by a search tools dedicated to IOT. In this work, we are interested in data collection methods used by IOT search engines, more precisely network port scanning and data generated by this method.
SCANet: Person Re-Identification with Semantically Consistent Attention

Person re-identification (re-ID) is an instance-level recognition mission, which depends on specific discriminative features. However, the learned features from the network are very easily influenced and weakened by the semantic misalignment of the target and background clutters. In this work, an original deep re-ID CNN is proposed, named semantically consistent attention network (SCANet), to learn the discriminative feature. In this work, we realize the purpose by designing a foreground mask module on backbone network composed of residual block. Importantly a novel consistent attention loss is introduced to dynamically keep the inferred foreground mask similar from the shallow-level, mid-level and deep-level feature maps. As a consequence, the network will concentrate on the foreground areas at the shallow layer, which is very beneficial for learning discriminative features from the foreground areas at the deeper layers. Extensive experiments demonstrate that the semantically consistent attention network can cope with the above challenges. The results display that the matching rate (Rank-1) of the proposed algorithm on datasets Market1501, DukeMTMC-reID and CUHK03 is 93.9%, 87.3%, 73.4%, and the mean Average Precision (mAP) is 90.8%, 82.2%, 75.2%, respectively.
Enhanced Discovery with Linked Open Data for Library Digital Collections

Libraries have been digitizing their collections for years in order to provide users with enhanced access and improve preservation of library materials. This article studies how the linked data can be used to describe objects in the digital collections. Researchers selected a digital collection of the nineteenth-century British novels from the University of Illinois at Urbana-Champaign Library, and converted the metadata for 7,829 British novels from MARCXML to BIBFRAME 2.0. Their project demonstrates that the BIBFRAME 2.0 model is a feasible way to enhance the discoverability of digital collections within and beyond library communities.
RSL19BD at DBDC4: Ensemble of Decision Tree-Based and LSTM-Based Models

RSL19BD (Waseda University Sakai Laboratory) participated in the Fourth Dialogue Breakdown Detection Challenge (DBDC4) and submitted five runs to both English and Japanese subtasks. In these runs, we utilise the Decision Tree-based model and the Long Short-Term Memory-based (LSTM-based) model following the approaches of RSL17BD and KTH in the Third Dialogue Breakdown Detection Challenge (DBDC3) respectively. The Decision Tree-based model follows the approach of RSL17BD but utilises RandomForestRegressor instead of ExtraTreesRegressor. In addition, instead of predicting the mean and the variance of the probability distribution of the three breakdown labels, it predicts the probability of each label directly. The LSTM-based model follows the approach of KTH with some changes in the architecture and utilises Convolutional Neural Network (CNN) to perform text feature extraction. In addition, instead of targeting the single breakdown label and minimising the categorical cross entropy loss, it targets the probability distribution of the three breakdown labels and minimises the mean squared error. Run 1 utilises a Decision Tree-based model; Run 2 utilises an LSTM-based model; Run 3 performs an ensemble of 5 LSTM-based models; Run 4 performs an ensemble of Run 1 and Run 2; Run 5 performs an ensemble of Run 1 and Run 3. Run 5 statistically significantly outperformed all other runs in terms of MSE (NB, PB, B) for the English data and all other runs except Run 4 in terms of MSE (NB, PB, B) for the Japanese data (alpha level $$=$$ 0.05).
Research on Moving Target Detection Based on ViBe Algorithm

ViBe algorithm is an efficient pixel level video background modeling algorithm. Aiming at the problem of ghost and dynamic background false detection in traditional ViBe algorithm, an improved algorithm based on improved ViBe algorithm is proposed. Firstly, the initial background model and the background update algorithm of three frame difference are constructed based on the time information to solve the problem of ghost elimination in the ViBe algorithm. Aiming at the problem of false detection in the process of dynamic background detection in the ViBe algorithm, an adaptive threshold dynamic background motion detection algorithm combined with the three frame difference method is proposed, Three frame difference method is used to determine the dynamic interference area, and adaptive threshold motion detection is used to detect the dynamic interference area. The algorithm can quickly remove ghosts and reduce the probability of false detection in dynamic background. Experimental results show that the algorithm is effective and robust.
Joint Common Sense and Relation Reasoning for Dense Relational Captioning

Relation reasoning between objects plays a vital role in image captioning. A joint common sense and relation reasoning model is proposed, which has the ability to generate more informative and diverse sentences for dense relational captioning. Our proposed model consists of two stages, region features extraction and relational captioning generation. The features of regions including object, union and subject are encoded by Region Proposal Network (RPN). Afterwards, they are fed to triple-stream Long Short-Term Memory (LSTM) respectively where object-predicate-subject categories are used as the prior information to lead the proper sequence of words in the descriptions generation. Moreover, we present the memory-augmented union operation where the image features and union features are leveraged to learn common sense and relation reasoning. Our experimental results on VG relationship captioning datasets demonstrate the validity of the joint common sense and relation reasoning model, which achieves competitive performance in dense relational captioning.
A Comprehensive Analytic Scheme for Classification of Novel Models

Classification is one of the major tasks in supervised learning. This had a great impact on deep leaning in developing various State-of-the-art architectures every year. These models can capture invariances by extracting the finest features by variant design models developed using Convolution Neural Networks. These deep networks have proven their resilience in the large scale image recognition by overhauling the existing drawbacks in architectures.In this research, a complete analytical scheme is developed to extract features from various models and to depict its performance on Fruits-360 dataset. During the analysis, the data is processed from feature extraction block to classification block. After the classification, the individual models' performance is evaluated and assessed with standard classification metrics. Out of these, an optimal model is selected in all prospects. It is observed that VGG models tend to outperform the existing models by capturing invariances by providing transferability on variant tasks. The networks VGG-16, VGG-19 obtained the highest precision of 97.2±0.005, 96.6±0.66 respectively. A pair-plot is obtained to analyse the behaviour pattern of predicted labels to that of true labels. This research is a first attempt to analyse the behaviour of pre-trained models on Fruits-360 dataset.
Geochemical proxies and formation mechanism of Hatay (Başlamış) travertine and relation with Dead Sea Fault Zone (S-Turkey)

This study is the first investigation of the Başlamış (Hatay) travertines formed in the northern part of the left-lateral strike-slip Dead Sea Fault Zone. In this study, Başlamış travertines are examined in terms of structural, mineralogical and formation mechanism by conducting geochemical, hydrogeochemical and facies analysis. The Başlamış travertine occurrences are characterized by successions of terraces and pools developed in slope depositional system, mound depositional system and depression depositional system. The travertines have been classified into five lithotypes, namely: laminated, coated bubble, reed, intraclasts and micritic travertine. According to the thin section, XRD, SEM and EDX analysis, the Başlamış travertines are divided into two groups, the first group contains aragonite and secondary minerals such as silica and clay minerals, while the second group consists only of calcite minerals, therefore, the travertines are morphologically classified as aragonite-bearing travertines and massive calcite bearing travertines. The negative correlation between Ca and Sr abundances in travertine samples indicates that some Ca was replaced by Sr in the calcite lattice. Relatively rich trace element content in the Başlamış travertines is attributed to leaching of the underlying gabbro and limestone. Higher concentrations of some of the elements were probably the result of diagenetic processes and syngenetic mineralization. The Başlamış travertines are defined as meteogene travertine deposits and are fed with meteoric-origin waters along Dead Sea Fault Zone in terms of hydrogeochemical results. The conceptual model of the Başlamış travertine shows that the travertines are formed by precipitation of dissolving the limestone. The heat source of the geothermal fluid forming the travertines is of tectonic origin.
Thinking globally, composing locally

With the emergence of online media, as traditional boundaries are breaking in the field of communication and the notion of audience is changing, the necessity of reviewing the modes of writing becomes consequential. Thinking Globally, Composing Locally , an edited collection by Rich Rice and Kirk St. Amant, takes a kairotic approach and aims to offer a foundation for addressing the technical issues related to online writing in terms of composition and rhetoric. To address the new audience, the editors believe writers need to rethink their writing contexts, reassess their perception of global communication, and reconsider their writing methods to create content for online media. To practically implement these key issues, this book proposes "3Cs"--- Contacting, Conveying , and Connecting. In an interview with Gustav Verhulsdonck (2018), Kirk St. Amant clarified the 3Cs. According to him, contact is a wish to get recognized by the audience that the composed work is worthy of attention, convey is the way information flows, and connect is the bridge of interaction between author and audience.
Design and Implementation of Pedagogical Innovations Based on Digital Technologies

The effectiveness of learning process nowadays largely depends on the applied scientific and pedagogical approaches. The use of modern methods and approaches in the learning process contributes to the development of creativity, overcoming stereotypes during the process. Teachers should contribute to the increase of cognitive activity of students. In determining the scientific and pedagogical approaches to improving the educational process, it is necessary to take into account the impact of educational reforms, economic, social, and political features of the regions. Modernization of the education system necessitates the search for new methods and an integrated approach to the organization of the learning process. Management of educational institutions in modern society is at a qualitatively new level with the use of information support decision-making based on network computer technology, modern software tools, provides for the relationship with regional and Federal management systems.
Prediction of Inflation Rate in Indonesia Using Local Polynomial Estimator for Time Series Data

Abstract Inflation prediction is needed to determine strategies and policies to control a country’s economic stability. Inflation is one of the important macroeconomic indicators. Fluctuating inflation rates can disrupt a country’s economy, so this is a particular concern. In this paper, inflation prediction was carried out through two approaches: the parametric regression model approach based on the Autoregressive Integrated Moving Average (ARIMA) model and the nonparametric regression approach based on the local polynomial estimator. The model’s accuracy is determined based on prediction results using the mean absolute percentage error (MAPE)value. We obtained a MAPE value of 9.0% for the ARIMA model approach and MAPE values of 4.0% and 1.778% for first and second orders, respectively, of local polynomial nonparametric regression model approach. It means that the best model for predicting Indonesia’s inflation rate is the second order of local polynomial nonparametric regression model because it has the smallest MAPE value.
Model Transfer and Fuzzy PID Control of Mach Number of Wind Tunnel Flow Field

In the process of wind tunnel experiments, it is often necessary to change the working condition of the wind tunnel. For the system which only uses the traditional PID controller, if the PID parameters are not adjusted in time, the control effect may become worse. Therefore, in this paper, first, for the working conditions with only a small number of data, transfer models are established and applied. Then, on the basis of PID control system, fuzzy controller is added to improve the control system. In addition, in the process of finding the optimal parameters of migration, genetic algorithm is used to achieve global optimization.
View-synthesized ‘re-calibration’ of an array projector for 3D measurement from an arbitrary monocular view

We previously presented a method on how to calibrate a high-speed Multi-Aperture Array Projector (MAAP) in order to obtain three-dimensional (3D) surface measurements using only a single camera instead of a stereo-camera configuration. The MAAP calibration procedure however can be time-consuming as it requires the camera to encode each aperture's illumination frustum by imaging the pattern incident on a depth-wise scanned test plane. This is as if recording discretized depth slices of the illumination frustums. This can be detrimental as the calibration is coupled to a single specific camera view. It would be laborious and time-consuming to repeat the entire MAAP calibration process for an alternate camera view. This study demonstrates a so-called MAAP ‘re-calibration’ solution that artificially synthesizes MAAP calibration images from an arbitrary camera view without direct imaging. This presents the opportunity to decouple the MAAP calibration from a single camera view and obtain 3D measurements from alternate camera placement setups without having to perform the entire MAAP calibration process again. This study shows that the synthesized MAAP calibration images resemble well the ground truth images. As a result, the proposed method has similar 3D measurement performance compared to when calibrating the MAAP with true images.
Research on Military Event Detection Method Based on BERT-BiGRU-Attention

Military event detection is one of the main tasks of military information extraction, which aims to identify event information in unstructured military text. Event detection is the first step of military event detection. Traditional military event detection methods are difficult to solve the problems of insufficient artificial features, inaccurate Chinese word segmentation in military field, and insufficient utilization of entity relationship features between sentences. Therefore, based on the pre-trained model (BERT), this paper proposes a military event detection method which combines BiGRU and attention mechanism. The language model is trained to construct a vector representation method combining word vector and position vector. BiGRU neural network is used to learn the above semantic features, and the attention mechanism is integrated to improve the expression ability of semantic features of sentences. This paper constructs a certain number of military text corpus. The experimental results show that our model improves the efficiency of military event detection compared with the traditional non attention model.
New width parameters for SAT and #SAT

We study the parameterized complexity of the propositional satisfiability (SAT) and the more general model counting (#SAT) problems and obtain novel fixed-parameter algorithms that exploit the structural properties of input formulas. In the first part of the paper, we parameterize by the treewidth of the following two graphs associated with CNF formulas: the consensus graph and the conflict graph. Both graphs have as vertices the clauses of the formula; in the consensus graph two clauses are adjacent if they do not contain a complementary pair of literals, while in the conflict graph two clauses are adjacent if they do contain a complementary pair of literals. We show that #SAT is fixed-parameter tractable when parameterized by the treewidth of the former graph, but SAT is W[1]-hard when parameterized by the treewidth of the latter graph. In the second part of the paper, we turn our attention to a novel structural parameter we call h-modularity which is loosely inspired by the well-established notion of community structure. The new parameter is defined in terms of a partition of clauses of the given CNF formula into strongly interconnected communities which are sparsely interconnected with each other. Each community forms a hitting formula, whereas the interconnections between communities form a graph of small treewidth. Our algorithms first identify the community structure and then use them for an efficient solution of SAT and #SAT, respectively.