Reviewerly/exHarmony huggingface dataset:
Semantic and Trade-Off Aware Location Privacy Protection in Road Networks Via Improved Multi-Objective Particle Swarm Optimization

Location privacy protection is an essential but challenging topic in the field of network security. Although the existing research methods, such as k-anonymity, mix zone, and differential privacy, show significant success, they usually neglect the location semantic and the proper trade-off between privacy and utility, which may allow attackers to obtain user privacy information by revealing the semantic correlation between the anonymous region and user's real location, thus causing privacy leakage. To solve this problem, we propose a location privacy protection scheme based on the k-anonymity technique, which provides practical location privacy-preserving through generating an anonymous set. This paper proposes a new location privacy attack strategy termed semantic relativity attack (SRA), which considers the location semantic problem. Correspondingly, a semantic and trade-off aware location privacy protection mechanism (STA-LPPM) is presented to achieve privacy protection with both high-level privacy and utility. To be specific, we model the location privacy protection as a multi-objective optimization problem and propose the Improved Multi-Objective Particle Swarm Optimization (IMOPSO) to generate the optimal anonymous set calculating the well-design fitness functions of the multi-objective optimization problem. In this way, the privacy scheme can provide mobile users with the right balance of privacy protection and service quality. Experiments reveal that our privacy scheme can effectively resist the semantic relativity attack while preventing significant utility degrading.
Deep Neural Network for Gender-Based Violence Detection on Twitter Messages

The problem of gender-based violence in Mexico has been increased considerably. Many social associations and governmental institutions have addressed this problem in different ways. In the context of computer science, some effort has been developed to deal with this problem through the use of machine learning approaches to strengthen the strategic decision making. In this work, a deep learning neural network application to identify gender-based violence on Twitter messages is presented. A total of 1,857,450 messages (generated in Mexico) were downloaded from Twitter: 61,604 of them were manually tagged by human volunteers as negative, positive or neutral messages, to serve as training and test data sets. Results presented in this paper show the effectiveness of deep neural network (about 80% of the area under the receiver operating characteristic) in detection of gender violence on Twitter messages. The main contribution of this investigation is that the data set was minimally pre-processed (as a difference versus most state-of-the-art approaches). Thus, the original messages were converted into a numerical vector in accordance to the frequency of word’s appearance and only adverbs, conjunctions and prepositions were deleted (which occur very frequently in text and we think that these words do not contribute to discriminatory messages on Twitter). Finally, this work contributes to dealing with gender violence in Mexico, which is an issue that needs to be faced immediately.
Multi-object Recognition Method Based on Improved YOLOv2 Model

A method of vehicle multi-object identification and classification based on the YOLOv2 algorithm is proposed to solve the problems of low detection rate, poor robustness, and unsatisfactory classification effect for the classical multi-object detection and vehicle type classification on real road environment. Based on the YOLOv2 algorithm, the network structure of YOLOv2-voc is improved according to the actual road conditions. The classification training model was obtained based on the ImageNet data and fine-tuning technology, according to the analysis of training results and vehicle object characteristics. This paper proposed the improved vehicle identification classification network structure, namely called YOLOv2-voc_mul. In order to verify the validity of the detection method, experiments are performed using samples from simple backgrounds and complex backgrounds and compared with the existing YOLOv2, YOLOv2-voc, and YOLOv3 models after 70000 iterations, respectively. The results show that the proposed YOLOv2-voc_mul model has an accuracy of 98.6% under the simple background, and the mAP (mean Average Precision) of different models reaches 87.81%. Under the complex background, the improved YOLOv2-voc_mul model has an average accuracy of 92.09% and 89.64% for single and multi-object detection of four different models. In summary, our proposed method has better accuracy, a low false detection rate, and good robustness.
Incorporation of Potential Fields and Motion Primitives for the Collision Avoidance of Unmanned Aircraft

Collision avoidance (CA) using the artificial potential field (APF) usually faces several known issues such as local minima and dynamically infeasible problems, so unmanned aerial vehicles’ (UAVs) paths planned based on the APF are safe only in a certain environment. This research proposes a CA approach that combines the APF and motion primitives (MPs) to tackle the known problems associated with the APF. Since MPs solve for a locally optimal trajectory with respect to allocated time, the trajectory obtained by the MPs is verified as dynamically feasible. When a collision checker based on the k-d tree search algorithm detects collision risk on extracted sample points from the planned trajectory, generating re-planned path candidates to avoid obstacles is performed. After rejecting unsafe route candidates, one applies the APF to select the best route among the remaining safe-path candidates. To validate the proposed approach, we simulated two meaningful scenario cases—the presence of static obstacles situation with local minima and dynamic environments with multiple UAVs present. The simulation results show that the proposed approach provides smooth, efficient, and dynamically feasible pathing compared to the APF.
Feedback LSTM network based on attention for image description generator

Images are complex multimedia data which contain rich semantic information. Most of current image description generator algorithms only generate plain description, with the lack of distinction between primary and secondary ob... | Find, read and cite all the research you need on Tech Science Press
Cross-Platform Mobile App Development in Industry: A Multiple Case-Study

Cross-platform frameworks for mobile application development are pervasive and allow developers to build applications that can operate on a spectrum of mobile platforms. With the high availability of such frameworks, it is imperative to give better understanding about the perception of industry and the challenges they face in this field. Although there are several studies in this field of research, only few research works investigate cross-platform mobile development in the industrial contexts in-depth. We conducted a qualitative research using multiple case-study approach. Interviews and focus groups methods were applied to gather information from four different software development companies in Palestine. This study aims at understanding industrial teams approach to mobile cross-platform development. In particular, it seeks to identify the challenges they face in the areas of code maintenance and software testing. Our findings cover the important aspects in industrial contexts, including the development process, as well as, how and why the cross-platform development approach is chosen. From the practitioners’ perspectives, we argue that the developers’ experience is the most influential factor in the development process. Further, our study investigates the different cross-platform technologies in the industry, and the dominant one from the practitioners’ perspectives, where they agreed that the React-Native is the promising and dominant technology. From the challenges perspective, we found that the APIs provided by the vendors are not easy to operate by developers and considered to be a major challenge. Finally, this research investigates how testing and maintenance methodologies are applied along with their challenges.
Application of Blockchain Technology in Supply Chain Finance of Beibu Gulf Region

Blockchain technology is a database that is operated by multiple parts and forms a chain structure through hash index. The blockchain uses multiple nodes and distributes multiple accesses to data, thereby reducing the dependence on the central Internet server and avoiding the possibility of damage to the central server point due to data and data loss. Encryption technology is used to ensure its integrity and ensure that the data files stored in the blockchain are not tampered with or deleted maliciously. Blockchain technology has inherent advantages in supply chain finance with its technical attributes such as nontampering, distributed ledger, and traceability and has great potential to build trust to solve the main problems of supply chain finance, which is conducive to promoting financial development in the Beibu Gulf region. This article mainly introduces the application research of blockchain technology in supply chain finance in the Beibu Gulf region and intends to provide some ideas for the development of supply chain finance in the Beibu Gulf region combined with blockchain technology. This article proposes the application research methods of blockchain technology in supply chain finance in the Beibu Gulf region, including blockchain technology, supply chain financial risk evaluation on the blockchain, and supply chain finance game for relevant experiments. The experimental results of this article show that the average processing time of the algorithm of the designed blockchain supply chain financial system is 4.10 seconds, the algorithm processing efficiency is faster, and the relevant risks can be better assessed.
Evidence integration credal classification algorithm versus missing data distributions

In complex incomplete pattern classification, the classification results produced by a single classifier and used for decision-making may be quite unreliable and uncertain due to the random distribution of missing data. This paper proposes a new evidence integration credal classification algorithm (EICA) for multiple classifiers working on different attributes, aiming to reduce the negative impact on incomplete pattern classification by modeling the missing values locally. In EICA, the dataset is first grouped into several subsets, and missing values in each subset are estimated by similar subpatterns with different weights. The similarity is measured by discounting the overall similarity of subpatterns and the local similarity of attributes on the basis of fully exploiting the distribution characteristics of the attributes. The greater the variation in distribution across classes, the greater the weight. The classification results of the edited subpatterns with different discounting factors obtained by the optimization function can often provide (more or less) useful information for the classification of the query pattern. Thus, these discounted pieces of evidence (outputs) represented by basic belief assignments (BBAs) are globally fused to classify the query pattern on the basis of evidence theory. The validity has been demonstrated with various real datasets.
Optimal Bezier Curve Modification Function for Contrast Degraded Images

Contrast enhancement plays a vital role in image processing and computer vision applications. However, the existing work inevitably introduces some unwanted artifacts and often fails in revealing image details when enhancing the invisible images. This article presents a novel Bezier curve modification scheme to improve the visual quality of contrast degraded imperceptible images. First, a salp swarm algorithm is incorporated to compute an optimal threshold value using weighted cumulative distribution function for adjusting the transformation in bright and dark areas separately. Then, an optimized Bezier curve uses a novel regularization parameter to improve the fine details of bright and dark regions, respectively. The experiential results are provided to demonstrate that the proposed method yields better quantitative results compared with the existing methods. Extensive experiments on different data sets demonstrate that the proposed method produces an enhanced image with superior visual quality for all five levels of contrast distorted images.
Strategy to improve the accuracy of convolutional neural network architectures applied to digital image steganalysis in the spatial domain

In recent years, Deep Learning techniques applied to steganalysis have surpassed the traditional two-stage approach by unifying feature extraction and classification in a single model, the Convolutional Neural Network (CNN). Several CNN architectures have been proposed to solve this task, improving steganographic images’ detection accuracy, but it is unclear which computational elements are relevant. Here we present a strategy to improve accuracy, convergence, and stability during training. The strategy involves a preprocessing stage with Spatial Rich Models filters, Spatial Dropout, Absolute Value layer, and Batch Normalization. Using the strategy improves the performance of three steganalysis CNNs and two image classification CNNs by enhancing the accuracy from 2% up to 10% while reducing the training time to less than 6 h and improving the networks’ stability.
Arabic Sentiment Analysis on Chewing Khat Leaves using Machine Learning and Ensemble Methods

Sentiment analysis plays an important role in obtaining speakers' opinions or feelings towards events, products, topics, or services, helping businesses to improve their products. Moreover, governments and organizations investigate and solve current social issues by analyzing perspectives and feelings. This study evaluated the habit of chewing Khat (qat) leaves among the Yemeni society. Chewing Khat plant leaves, is a common habit in Yemen and East Africa. This paper proposes a model to detect information about the Khat chewing habit, how people explore it, and the preference for Khat leaves among Arabic people. A dataset consisting of user comments on 18 youtube videos was prepared through several natural language processing techniques. Several experiments were conducted using six machine learning classifiers and four ensemble methods. Support Vector Machine and Linear Regression had almost 80% accuracy, whereas xgboot was the most accurate ensemble method reaching 77%.
Subword Pooling Makes a Difference

Contextual word-representations became a standard in modern natural language processing systems.These models use subword tokenization to handle large vocabularies and unknown words.Word-level usage of such systems requires a way of pooling multiple subwords that correspond to a single word.In this paper we investigate how the choice of subword pooling affects the downstream performance on three tasks: morphological probing, POS tagging and NER, in 9 typologically diverse languages.We compare these in two massively multilingual models, mBERT and XLM-RoBERTa.For morphological tasks, the widely used 'choose the first subword' is the worst strategy and the best results are obtained by using attention over the subwords.For POS tagging both of these strategies perform poorly and the best choice is to use a small LSTM over the subwords.The same strategy works best for NER and we show that mBERT is better than XLM-RoBERTa in all 9 languages.We publicly release all code, data and the full result tables at https://github.com/juditacs/subword-choice.
The critical care data exchange format: a proposed flexible data standard for combining clinical and high-frequency physiologic data in critical care

Objective. To develop a standardized format for exchanging clinical and physiologic data generated in the intensive care unit. Our goal was to develop a format that would accommodate the data collection pipelines of various sites but would not require dataset-specific schemas or ad-hoc tools for decoding and analysis. Approach. A number of centers had independently developed solutions for storing clinical and physiologic data using Hierarchical Data Format—Version 5 (HDF5), a well-supported standard already in use in multiple other fields. These individual solutions involved design choices that made the data difficult to share despite the underlying common framework. A collaborative process was used to form the basis of a proposed standard that would allow for interoperability and data sharing with common analysis tools. Main Results. We developed the HDF5-based critical care data exchange format which stores multiparametric data in an efficient, self-describing, hierarchical structure and supports real-time streaming and compression. In addition to cardiorespiratory and laboratory data, the format can, in future, accommodate other large datasets such as imaging and genomics. We demonstated the feasibility of a standardized format by converting data from three sites as well as the MIMIC III dataset. Significance. Individual approaches to the storage of multiparametric clinical data are proliferating, representing both a duplication of effort and a missed opportunity for collaboration. Adoption of a standardized format for clinical data exchange will enable the development of a digital biobank, facilitate the external validation of machine learning models and be a powerful tool for sharing multiparametric, high frequency patient level data in multisite clinical trials. Our proposed solution focuses on supporting standardized ontologies such as LOINC allowing easy reading of data regardless of the source and in so doing provides a useful method to integrate large amounts of existing data.
Institutional Reforms and Their Impact on Economic Growth and Investment in Developing Countries

It is widely accepted that the economic and social system may be more efficient by reforming institutions. Institutional reforms are attempts to change the rules affecting human interactions and these reforms are fundamental for development and economic prosperity. The reforms can be divided into two categories; political and economic institutional reforms. It is need of the hour to determine the category of reform that is more suitable for developing countries. Moreover, a vast literature describes the impact of institutional reforms but little focused on exploring their impacts on macroeconomic activities. So, this study is an effort to determine the impact of institutional reforms on macroeconomic variables by considering the panel data of 122 developing countries covering the time span from 1996 to 2019. The study applied treatment analysis using the difference-in-differences technique to gauge the effects of reforms. Besides, it will be interesting to know the causes triggering the institutional reforms in developing countries. The findings of the study reveal that economic reforms are more important as compared with political reforms to grow the economies. The countries focusing on political reforms are not able to overcome the economic crisis. Moreover, both types of reforms do not cause each other in these countries.
Accounting Information Security and IT Governance Under COBIT 5 Framework: A Case Study

This paper aims to explore the role of applying information technology governance using the COBIT 5 framework in improving the security of accounting information systems. The paper also aims to assess the level of governance of information technology in the Trade Bank of Iraq TBI through COBIT 5 processes, including 4 dimensions of planning and organization, acquisition and implementation, support and delivery, and monitoring. The study uses a qualitative approach through a case study conducted at the Iraqi Bank of Commerce. The results show that applying the COBIT 5 governance mechanisms of information technology reduces data processing risks and improves automated accounting information systems' security. The results also show that the Trade Bank of Iraq can implement the COBIT 5 Framework. Also, the accounting system used in the Bank has all the features of information technology governance that, on the one hand, ensure confidentiality of customer information and, on the other hand, prevent the system from penetrating. The results of the current study provide a better understanding of stakeholders on the nature of IT governance in the light of the COBIT 5 framework and its role in enhancing accounting information security.
Design and Implementation of Online Examination System based on MSVS and SQL for University Students in Iraq

The transfer of knowledge and skills is a process that is closely linked to the measurement of its usefulness. It's hard to imagine teaching without considering the results and evaluation of students. At the same time, the current education system is based on a longstanding traditional paradigm. The aim of this article is to propose a new digital review system based on Microsoft visual studio (MSVS); it is the only one of its kind in terms of application versatility and also features professional interfaces for the interactive display of questions and answers. In our school, we have systematically assessed this program to determine the system performance of teaching staff and students, the findings of this study demonstrate the assumption that students were prepared for this type of knowledge; the evaluation result of the propose exam system outperforms conventional system about approximately 80%.
Measurement uncertainty and entanglement in the hybrid-spin Heisenberg model

In this work, we study the dynamics of entropy-based uncertainty under a hybrid-spin (1, 1/2) Heisenberg XXZ model with an inhomogeneous magnetic field. Specifically, we examine the effect of the magnetic field strength B and coupling strength J of the two spins on the measurement uncertainty and the entanglement of the considered system, and nontrivially reveal that the magnetic field and coupling strength are responsible for the uncertainty dynamics associated with incompatible measurements. Interestingly, the entropic uncertainty eventually reaches a stable value with growing B, and the entanglement quantified by negativity eventually reduces to zero. Contrarily, a larger coupling constant J effectively reduces uncertainty. In addition, the effects of temperature T also are evaluated in the current framework. Furthermore, the relationship between uncertainty and entanglement is discussed; we claim that negativity and entropic uncertainty are approximately inversely correlated. Therefore, our observations are expected to be helpful in illustrating the dynamical entropy-based uncertainty in a hybrid Heisenberg spin-chain model and thus would be useful for realistic quantum information processing.