Reviewerly/exHarmony huggingface dataset:
MAPPING FAULTS DISTRIBUTION BASED ON DEM DATA FOR REGIONAL SPATIAL PLAN ASSESSMENT OF SABANG MUNICIPALITY, INDONESIA

Sabang Municipality is located on Weh Island.As a maritime gateway to Indonesia, the island has been inaugurated as an Integrated Economic Development Zone by the Government.A complete infrastructure has been planned and built to support the manufacturing and tourism industries in the region.On the other hand, the island is potentially affected by geological hazards.Tectonically, the island is crossed by the Great Sumatran Fault (GSF), but few studies have shown the fault trace on the island.To ensure sustainable development, such potential geological hazards have to be incorporated into the Regional Spatial Plan (RSP) of the city.In this study, fault distribution on the island were mapped using shaded relief, slope, lineament extraction, orientation, and Fault Fracture Density (FFD) distribution extracted from the National Digital Elevation Model (DEMNAS) data.The map shows that the island is crossed by several fault lines, but the one located along the eastern with northwest-southeast direction is considered tectonically active.However, according to the RSP, the eastern Weh Island is the most developed area.The city center, airport, seaports, dam, sewerage treatment plan, landfills, and diesel power electricity generator for the city are situated along this fault line.It seems that the RSP of Sabang Municipality was issued without taking into account potential geological hazard maps in it.Some recommendations have been proposed to reduce the impact of losses in the future.
Fail-Stop Group Signature Scheme

In this study, a fail-stop group signature scheme (FSGSS) that combines the features of group and fail-stop signatures to enhance the security level of the original group signature is proposed. Assuming that FSGSS encounters an attack by a hacker armed with a supercomputer, this scheme can prove that the digital signature is forged. Based on the aforementioned objectives, this study proposes three lemmas and proves that they are indeed feasible. First, how does a recipient of a digitally signed document verify the authenticity of the signature? Second, when a digitally signed document is under dispute, how can the group’s manager determine the identity of the original group member who signed the document, if necessary, for an investigation? Third, how can one prove that the signature is indeed forged following an external attack from a supercomputer? Following an attack, the signature could be proved to be forged without exposing the key. In addition, the ultimate goal of the group fail-stop signature scheme is to stop using the same key immediately after the discovery of a forgery attack; this would prevent the attack from being repeated.
A Comparative Analysis of Machine Learning Models for First-break Arrival Picking

First-break (FB) picking is an important and necessary step in seismic data processing and there is a need to develop precise and accurate auto-picking solutions.Our investigation in this study includes eight machine learning models.We use 1195 raw traces to extract several features and train for accurate picking and monitoring the performance of each model using well-defined evaluation metrics.Careful investigation of the scores shows that a single metric alone is not sufficient to evaluate the arrival picking models in real-time.Correlation analysis of predicted probabilities and predicted classes of machine learning models confirm that the performance metrics that use predicted probabilities have higher score value than those that use predicted classes.Our study which incorporates comparisons of different machine learning models based on different performance metrics, training time, and feature importance indicates that the approach we developed in this study is helpful and provides an opportunity to determine the real-time suitability of different methodologies for automatic FB arrival picking with clear deep insight.Based on performance scores, we bench-marked the Extra Tree classifier as the most efficient model for FB arrival picking with accuracy and F1score above 95%.
ALOD2Vec Matcher results for OAEI 2020

This paper presents the results of the ALOD2Vec Matcher in the Ontology Alignment Evaluation Initiative(OAEI) 2020. The matching system exploits a Web-scale dataset, i.e.WebIsALOD, as background knowledge source. In order to make use of the dataset, the RDF2Vec approach is applied to derive embeddings for each concept available in the dataset. ALOD2Vec Matcher participated in the OAEI 2018 campaign before. This is the system’s second participation. The matching system has been extended, improved, and achieves better results this year.
Simulating Municipal Cybersecurity Incidents: Recommendations from Expert Interviews

As cyberattacks on city and public infrastructures become increasingly common and harmful, it is critical that we train the professional workforce to prepare and respond appropriately.This paper supports the development of educational simulations and related experiential learning exercises that help prepare city and public infrastructure personnel to effectively respond to cybersecurity attacks.Specifically, it synthesizes the findings from 8 expert interviews including 12 cybersecurity experts from federal, state, city organizations, as well as academics with relevant expertise.We organize the findings into key learning outcomes, scenarios, roles, and issues that simulation designers should consider.The result paints a vivid picture of the complex socio-technical context of city and public infrastructure attacks and responses and the most salient skills needed to respond to them.
Utilizing Remote Evaluation for Providing Data Sovereignty in Data-sharing Ecosystems

The maintenance of digital sovereignty is an important aspect of data-driven business models and data-sharing ecosystems.Considering this, sensitive data is often stored in proprietary systems under the data owner's control and with appropriate security mechanisms.However, nowadays, it is often necessary to share data.As executing unknown and untrusted code on systems containing sensitive data is potentially dangerous, data-processing algorithms cannot be directly sent to the data-storing systems, as one solution.Instead, we have implemented an approach called remote processing that uses the domain-specific language D • , which provides built-in usage control mechanisms for data processing tasks.The approach extends the well-known remote evaluation paradigm that allows controlled, distributed data usage without actual data sharing (transmission via network).Instead of classified data, applications and their execution results are transmitted.This way, sensitive data is never directly exposed to third parties.Furthermore, the application-integrated usage control mechanisms prevent malicious data usage.
A gold standard dataset for large knowledge graphs matching

In the last decade, a remarkable number of Knowledge Graphs (KGs) were developed, such as DBpedia, NELL and Google knowledge graph. These KGs are the core of many web-based applications such as query answering and semantic web navigation. The majority of these KGs are semi-automatically constructed, which has resulted in a significant degree of heterogeneity. KGs are highly complementary; thus, mapping them can benefit intelligent applications that require integrating different KGs such as recommendation systems and search engines. Although the problem of ontology matching has been investigated and a significant number of systems have been developed, the challenges of mapping large-scale KGs remain significant. In 2018, OAEI has introduced a specific track for KG matching systems. Nonetheless, a major limitation of the current benchmark is their lack of representation of real-world KGs. In this work we introduce a gold standard dataset for matching the schema of large, automatically constructed, less-well structured KGs based on DBpedia and NELL. We evaluate OAEI's various participating systems on this dataset, and show that matching large-scale and domain independent KGs is a more challenging task. We believe that the dataset which we make public in this work makes the largest domain-independent gold standard dataset for matching KG classes.
Conflicts Identification among Stakeholders in Goal Oriented Requirements Engineering Process

Requirements Analysis are the most important part of Software Engineering for both System Application Development, and project requirements. Conflicts often arise during the requirements gathering and analysis phase. This research aims to identifying conflicts during requirements gathering phase in software development life cycle, Research, Development, and Technology converted the world into a global village. During requirements elicitation/gathering phase it’s very difficult to understand the main objective of stakeholders, after completion of requirements elicitation task final results are used for Software Requirements Specification (SRS), SRS is the highly important outcome of the requirements analysis phase. this is the foundation between the developers and stakeholders or customers, proposed methodology will be helpful to identify those conflicts in very easy manner during the initial phase of the project.
Reconstruction Method for Binary Defocusing Technique Based on Complementary Decoding With Dual Projectors

Real-time three-dimensional reconstruction is increasingly important in many fields. There are mainly two ways to improve real-time performance: increasing hardware rate and reducing captured frames count. For increasing hardware rate, the binary defocusing technique can effectively break the limitation of speed. For reducing captured frames count, the fusion technique is typically introduced. However, the simultaneous work of binary defocusing technique and fusion technique is hard to achieve. To this end, we establish a novel system framework with a camera and dual projectors. The dual projectors can control defocusing level independently, i.e. enhance the flexibility of projector. Based on this, our paper exhibits two decoding phase methods: the strict-alignment method and the loose-alignment method. The strict-alignment method requires the projection chips to be strictly aligned, and a virtual example demonstrated effectiveness with three captured frames. In order to further improve practicality, the loose-alignment method, which relaxes the restriction, is proposed. Finally, experiments verify the performance of our proposed method.
PENGARUH MODEL PEMBELAJARAN COOPERATIVE TIPE SNOWBALL THROWING TERHADAP HASIL BELAJAR SISWA DI MI AL-MURSYID CITEUREUP-BOGOR

The learning activities carried out by the teachers at MI Al-Mursyid have been impressed that only active teachers. The teacher mostly uses the discussion method when learning takes place, but the discussion is still ineffective so that students tend to be passive in accepting the subject matter provided by the teacher. This results in student activity and learning outcomes to be very low. With this background, researchers are interested in examining whether there is an effect of the Snowball Throwing Cooperative Learning Model on Student Learning Outcomes. The research was conducted at MI Al-Mursyid, Citeureup District, Bogor Regency. The study population was 153 students while the sample was 20 people. Researchers used an experimental class, namely class V with a total of 20 students. The instrument used was a test to measure student learning outcomes in the form of a multiple choice test of 20 questions. The data analysis technique used is descriptive statistical analysis and inferential statistical analysis with t-test. Based on the results of the descriptive analysis, the average value obtained before applying the Snowball Throwing model was 57.60 for pretest and 81.55 for posttest. Based on the results of inferential statistical analysis t count 11.31 and t table 2.10. Because tcount&gt; ttable (11.31&gt; 2.10), then H0 is rejected and Ha is accepted. So, it can be concluded that the accepted hypothesis is that the Snowball Throwing learning model in science learning has a significant effect on the learning outcomes of grade V students at MI Al-Mursyid.
WITHDRAWN: Breast cancer detection using random forest classifier

Breast Cancer is a menacing disease and is commonly seen in women. Based on the severity, breast cancer is classified into duplet variants. One is Benign type of breast cancer, which can be detected at early stages and can be cured with the help of medication. Other is Malignant type of breast cancer, which shows severe affect and might lead to death. To detect breast cancer at early stages, wide variety of algorithm techniques are used such as Navie Bayes, Convolution Neural Network, KNN, adaptive voting ensemble machine learning algorithm and so on. Most latest algorithm that is under practice is adaptive voting ensemble machine learning algorithm. In this algorithm, Wisconsin Breast Cancer dataset and CNN algorithm is used to classify images and for object detection. But the major drawback of ensemble machine learning algorithm is lack of accuracy. It is proved that Neutral Network works more effective on humans mostly in analyzing data and to perform pre-diagnosis without medical knowledge. In this paper, we propose Random Forest Classifier algorithm to achieve more accuracy.
Application and development trend of unmanned aerial vehicle navigation technology

UAV (Unmanned Aerial Vehicle) equipment is novel because of its remote automatic control characteristics, so navigation technology is particularly critical. It is necessary to study and apply UAV technology. This paper first analyzes the UAV navigation technology, then the different types of UAV navigation system, and finally looks forward to the development direction of UAV navigation system, hoping that through this study, we can have a general understanding of the application and development trend of UAV navigation technology, and hope that the relevant research can be further enriched and improved to guide practical application.
Estimating Position of Multiple People in Common 3D Space via City Surveillance Cameras

Automatic detection of dangerous situations to ensure the safety of residents is one of the areas of Smart Surveillance. Often dangerous situations are caused by deviant behavior of people (vandalism, brawl, robbery, etc.). An important technology for analyzing the actions and interactions of people in the context of detecting deviant behavior is 3d human poses estimation, but the estimation of the relative position of multiple people in 3d space, which is necessary to analyze the interaction of people, is a separate task that remains outside the scope of this technology. In this paper, we propose an approach for estimating the relative position of 3d poses of people based on the surface normals detection in plain RGB images. The approach was tested using a computer-graphic based dataset containing scenes of interacting people in a city. The results were compared with the existing approach for estimating the relative position of 3d poses of people, based on the assumption of the constant human skeleton length.
Convolutional Neural Network for Elderly Wandering Prediction in Indoor Scenarios

This work proposes a way to detect the wandering activity of Alzheimer's patients from path data collected from non-intrusive indoor sensors around the house. Due to the lack of adequate data, we've manually generated a dataset of 220 paths using our own developed application. Wandering patterns in the literature are normally identified by visual features (such as loops or random movement), thus our dataset was transformed into images and augmented. Convolutional layers were used on the neural network model since they tend to have good results finding patterns, especially on images. The Convolutional Neural Network model was trained with the generated data and achieved an f1 score (relation between precision and recall) of 75%, recall of 60%, and precision of 100% on our 10 sample validation slice
Three State Estimation Fusion Methods Based on the Characteristic Function Filtering

There are three state estimation fusion methods for a class of strong nonlinear measurement systems, based on the characteristic function filter, namely the centralized filter, parallel filter, and sequential filter. Under ideal communication conditions, the centralized filter can obtain the best state estimation accuracy, and the parallel filter can simplify centralized calculation complexity and improve feasibility; in addition, the performance of the sequential filter is very close to that of the centralized filter and far better than that of the parallel filter. However, the sequential filter can tolerate non-ideal conditions, such as delay and packet loss, and the first two filters cannot operate normally online for delay and will be invalid for packet loss. The performance of the three designed fusion filters is illustrated by three typical cases, which are all better than that of the most popular Extended Kalman Filter (EKF) performance.
Parameter tuning of software effort estimation models using antlion optimization

In this work, the antlion optimization (ALO) is employed due to its efficiency and wide applicability to estimate the parameters of four modified models of the basic constructive cost model (COCOMO) model. Three tests are carried out to show the effectiveness of ALO: first, it is used with Bailey and Basili dataset for the basic COCOMO Model and Sheta’s Model 1 and 2, and is compared with the firefly algorithm (FA), genetic algorithms (GA), and particle swarm optimization (PSO). Second, parameters of Sheta’s Model 1 and 2, Uysal’s Model 1 and 2 are optimized using Bailey and Basili dataset; results are compared with directed artificial bee colony algorithm (DABCA), GA, and simulated annealing (SA). Third, ALO is used with Basic COCOMO model and four large datasets, results are compared with hybrid bat inspired gravitational search algorithm (hBATGSA), improved BAT (IBAT), and BAT algorithms. Results of Test1 and Test2 show that ALO outperformed others, as for Test3, ALO is better than BAT and IBAT using MAE and the number of best estimations. ALO proofed achieving better results than hBATGSA for datasets 2 and 4 out of the four datasets explored in terms of MAE and the number of best estimates.
Determinants of Intra Sub-Saharan African Trade: Evidence from ECOWAS and CEN-SAD Countries

This study investigates the determinants of intra-regional trade in the Economic of West African States (ECOWAS) and the Community of Sahel-Saharan Sates (CEN-SAD) over the period of 1995-2018.The study employs the modified Poisson models, which captures the source of zero counts. Data on the real exchange rate, population, and gross domestic product were sourced from World Bank Development Indicators. Import flow, time of import, and time of export were computed from WITS (COMTRADE). Further, the study obtained data on voice and account, law and order, government effect, regulatory quality, reduction in political violence, control of corruption from World Wide Governance indicator. The results of the study indicated that imports within ECOWAS are driven by one governance variable or the other either in the importing countries or the partner countries. Besides, trade facilitation is a binding constraint to imports, while population and GDP are important drivers of intra-ECOWAS trade. For CEN-SAD, it is evident that the gravity variables are responsible for imports, whereas governance variables have no significant effect on imports. The implication of these results is that authorities in ECOWAS and CEN-SAD should strengthen governance institutions as doing so will boost trade within the region. Also, it is necessary for government, particularly in CEN-SAD, to come up with policies that will allow for accountability and transparency.
Towards self-similarity consistency and feature discrimination for unsupervised domain adaptation

Recent advances in unsupervised domain adaptation mainly focus on learning shared representations by global statistics alignment, such as the Maximum Mean Discrepancy (MMD) which matches the Mean statistics across domains. The lack of class information, however, may lead to partial alignment (or even misalignment) and poor generalization performance. For robust domain alignment, we argue that the similarities across different features in the source domain should be consistent with that in the target domain. Based on this assumption, we propose a new domain discrepancy metric, i.e., Self-similarity Consistency (SSC), to enforce the pairwise relationship between different features being consistent across domains. The Gram matrix matching and Correlation Alignment is proven to be a special case, and a sub-optimal measure of our proposed SSC. Furthermore, we also propose to mitigate the side effect of the partial alignment and misalignment by incorporating the discriminative information of the deep representations. Specifically, a simple yet effective feature norm constraint is exploited to enlarge the discrepancy of inter-class samples. It relieves the requirements of strict alignment when performing adaptation, therefore improving the adaptation performance significantly. Extensive experiments on visual domain adaptation tasks demonstrate the effectiveness of our proposed SSC metric and feature discrimination approach.
Real-time vehicle counting in complex scene for traffic flow estimation using multi-level convolutional neural network

Due to the rising traffic congestions, information about the traffic conditions can be used by traffic management centres in many ways, including to synchronize traffic lights, to assist drivers in the route's selection, and to assist governments in intercity connections and new roads planning.Traffic management not only provides benefits to the road users, but also to the municipals and central governments, and the environment.Drivers could benefit with less time spent in travelling in urban and rural road, while governments can acquire those data for analysis and used those data to improve urban road management.From the perspective on caring for the environment, this effort could reduce the emission of pollutants.
Generating new concepts with hybrid neuro-symbolic models.

Human conceptual knowledge supports the ability to generate novel yet highly structured concepts, and the form of this conceptual knowledge is of great interest to cognitive scientists. One tradition has emphasized structured knowledge, viewing concepts as embedded in intuitive theories or organized in complex symbolic knowledge structures. A second tradition has emphasized statistical knowledge, viewing conceptual knowledge as an emerging from the rich correlational structure captured by training neural networks and other statistical models. In this paper, we explore a synthesis of these two traditions through a novel neuro-symbolic model for generating new concepts. Using simple visual concepts as a testbed, we bring together neural networks and symbolic probabilistic programs to learn a generative model of novel handwritten characters. Two alternative models are explored with more generic neural network architectures. We compare each of these three models for their likelihoods on held-out character classes and for the quality of their productions, finding that our hybrid model learns the most convincing representation and generalizes further from the training observations.