Reviewerly/exHarmony huggingface dataset:
DESIGNING STRATEGIES FOR UNIVERSITY STUDENTS’ WRITING SKILL

In the process of teaching and learning English writing skill, some strategies which are considered appropriate for a certain group of learners, may not suitable for other groups as each group has its own problems and needs in writing. Therefore, teaching learning strategies for English writing requires to be continuously developed. This study aims to design strategies based on the students’ writing needs and problems as the development of the previous common implemented strategies in writing course classes at English Study Program of Cordova University, West Sumbawa Regency. The research subjects were students who are active studying English as their major specialization and lecturers who have been teaching English writing course in academic year 2018/2019.The result shows that the previous strategies give little significant impact toward the students’ writing skill progress. For the reasons, new strategies were designed and recommended to be continuously implemented for teaching and learning writing courses and involved in curriculum at English Study Program, Cordova University. Those strategies are (1) Collaborative Writing Strategy. (2) Combining Diary and Guided Writing Strategy. (3) Additional credit hours for writing course classes (4). Graded writing course classes need to be continuously taught by the same lecturer
Feature-guided Neural Model Training for Supervised Document Representation Learning

With the advent of neural models, there has been a rapid move away from feature engineering, or at best, simplistically combining handcrafted features with learned representations as side information. We propose a method that uses hand-crafted features to guide learning by explicitly attending to feature indicators when learning the relationship between the input and target variables. In experiments over two different tasks — quality assessment of Wikipedia articles and popularity prediction of online petitions— we demonstrate that the proposed method yields neural models that consistently outperform those that simply use hand-crafted features as side information.
A Comparison between Conditional Random Field and Structured Support Vector Machine for Arabic Named Entity Recognition

The Named Entity Recognition (NER) is an integrated task in many NLP applications such as machine translation, Information extraction and question answering.Arabic is one of the authorised spoken languages in the united nation.Currently, there is much Arabic information on the internet, so, nowadays the need for tools which process this information becomes significant.In this study, we have examined the impact of the conditional random field and the structured support vector machine in the task of Arabic NER.The structured support vector machine is the first time to be applied in the Arabic name entity recognition.Our proposed system has three stages: Preprocessing, extracting features and building model.We have used simple features like the bag of words in the [-1,1] window, the bag of part of speech tag in the [-1,1] window to enable our system to detect the multi-words entities.Also, we have tried to enhance the Stanford part of speech tagger to enhance the tagger output tags, which enabled our system to differentiate between the name entities from the non-entities.In addition, we have employed the binary features of: Is a person, is a prename, is a pre-location, is a location and is an organization.Our system has been trained and tested on part of ANER Crop.The results have proved that the conditional random field-based Arabic NER system outperforms the structured support vector machine-based Arabic NER using the same features set.
Reliable Redundant Services Placement in Federated Micro-Clouds

In a battlefield or disaster relief environment, network connectivity ability is unstable and task requirements may change frequently. Federated micro-clouds are promising for solving problem of service delivery in this dynamic scenario. However, due to the harsh environment and the dynamic feature of federated micro-clouds, there is a serious risk of service failure. Redundant configuration is an effective method to enhance service reliability. But when determining the placement strategy for an application request containing multiple services, few previous approaches can effectively handle such a highly complex task of redundant services placement under multiple constraints, including resource constraints, communication delay limits and location constraints. In this paper, we study how to reliably place redundant services requested by user on federated micro-clouds. We first design a two-level reliability evaluation model to quantitatively measure the reliability of each services placement from the two aspects of node reliability and Shared Risk Domain (SRD) reliability. Second, we propose a Reliable Redundant Services Placement (RRSP) approach to iteratively place each service by using Dynamic Graph Growth (DGG) algorithm. RRSP can determine the current optimal location according to the reliability of nodes and the number of service combinations to find a highly reliable placement strategy in a short time. The evaluation results show that our algorithm can provide more reliable services placement than three other representative algorithms.
Fuzzy c-Means Directional Clustering (FCMDC) algorithm using trigonometric approximation

Cluster analysis is widely used in data analysis. Statistical data analysis is generally performed on the linear data. If the data has directional structure, classical statistical methods cannot be applied directly to it. This study aims to improve a new directional clustering algorithm which is based on trigonometric approximation. The trigonometric approximation is used for both descriptive statistics and clustering of directional data. In this paper, the fuzzy clustering algorithms (FCD and FCM4DD) improved for directional data and the proposed method are carried out on some numerical and real data examples, and the simulation results are presented. Consequently, these results indicate that the fuzzy c-means directional clustering algorithm gives the better results from the points of the mean square error and the standard deviation for cluster centers.
A spatially explicit evolutionary algorithm for the spatial partitioning problem

Spatial optimization seeks optimal allocation or arrangement of spatial units under constraints such as distance, adjacency, contiguity, and pattern. Evolutionary Algorithms (EAs) are well-known optimization heuristics. However, classic EAs, based on a binary problem encoding and bit-operation-based offspring operators, are spatially unaware and do not capture topological and geometric relationships. Unsurprisingly when spatial characteristics are not explicitly considered in the design of EA operators, that EA becomes ineffective because satisfying spatial constraints is computationally expensive. We design and develop novel spatially explicit EA recombination operators, inspired by the path relinking and ejection chain heuristic strategies, that implement crossover and mutation using intelligently guided strategies in a spatially constrained decision space. Our spatial EA approach is general and slots well into the foundational theory of evolutionary algorithms for spatial optimization. We demonstrate improved solution quality and computational performance with a large-scale spatial partitioning application.
Development of scenario modeling of conflict tools in a security system based on formal grammars

The results of the development of tools for scenario modeling based on formal grammars are presented. The works related to various ways of scenario description in their development systems are analyzed. For the natural language description of scenarios, it is indicated that this approach is sufficiently transparent and understandable for users. However, this approach has several disadvantages for formalizing and unifying scenario description. In particular, the presence of a number of ambiguities in the language makes it impossible for the description to be uniquely interpreted, and as a result, unsuitable for performing formal transformations on the description. The graphical representation of the script is a visual representation of the script. Moreover, the visual representation of the scenario in the form of some automaton model can be estimated as extremely attractive for subsequent multi-agent modeling of its execution. The disadvantage of such scenario description is still the difficulty of performing formal manipulations and the need to switch to a more convenient representation for manipulations. Using formal grammars to describe scenarios is a compromise approach that allows scripts to be described in an unambiguously interpreted form. The formal grammar description is also more familiar to computer language specialists. In addition, there are programs focused on working with formal grammars. The transition from the natural linguistic description of scenarios to its formal representation as a standard description in the Backus-Naura form is shown. The change in the presentation form was made using the example of description of the scenario of behavior of cyber-conflict participants in the security system. The resulting script description was used in the context-free grammar analyzer. The results showed the applicability of the proposed approach and the tools used to describe and verify the description correctness of scenarios related to any subject area
Cross-Cultural Adaptation and Validation of the "Student Communication & Study Habits" Questionnaire to the Mexican Context

Within the framework of the international research project Digital Learners in Higher Education that investigate how university learners in different institutional contexts and cultures think about digital technologies and how they use them in their social and educational lives. This study aims to culturally adapt and validate a questionnaire prepared in another country to gain insights into how first year university students communicate and their general study habits, through digital technologies, at the Universidad Autonoma de Chiapas (UNACH) in Mexico.
Dual Threshold Adaptive Dynamic Migration Strategy of Virtual Resources Based on BBU Pool

The rapid development of mobile communications and the continuous growth of service needs lead to an increase in the number of base stations (BSs). Through virtualization and cloud technology, virtual Baseband Units (BBUs) are deployed on a virtual machine (VM) to build a BBU pool to achieve hardware resource sharing, which not only saves BS construction costs but also facilitates management and control. However, too high or too low server resource utilization in the pool not only affects the performance of the virtual BBU but also increases the maintenance cost of the physical equipment. In this paper, BBUs are virtualized to construct a virtual BBU pool based on the OpenStack cloud architecture and a dual threshold adaptive dynamic migration strategy is proposed in this scenario. Establish upper and lower threshold of resource utilization of the servers in the pool and the strategy determines whether the dynamic migration is triggered according to the resource utilization of each compute node. If the migration is triggered, the strategy selects the virtual resource to be moved out and the target node to realize the dynamic migration to achieve the purpose of balancing the server load and saving energy consumption. The migration strategy proposed in this paper is simulated on Cloudsim and the experimental results show that the strategy can effectively reduce the number of migrations and migration time on the basis of reducing energy consumption and SLA violations. This paper successfully deployed the strategy on the OpenStack platform, which implements dynamic migration autonomously to save the overall energy consumption of the BBU pool, instead of manual operations.
Accuracy Improvement of Straight Take-off, Flying Forward and Landing of a Drone with Reinforcement Learning

Nowadays, drones are expected to be used in several fields, especially flying indoors to explore the surroundings. In this paper, we present a novel method that uses ArUco markers as a reference to improve the accuracy of a drone on straight takeoff, flying forward, and landing based on Reinforcement Learning (RL). Particularly, the drone first detects a specific marker with its onboard cameras. Then, it calculates the position and orientation relative to the marker so as to adjust its actions for achieving better accuracy with an RL method. We perform several simulation experiments by using the Robot Operating System (ROS) and its Gazebo simulator. Simulation results show that our proposed method can efficiently improve the accuracy of the considered actions.
Unsupervised Ensemble Hashing: Boosting Minimum Hamming Distance

Hashing aims at learning discriminative binary codes of high-dimensional data for the approximate nearest neighbor searching. However, the distance ranking obtained by traditional methods is not optimum in the Hamming space, and it degrades the performance for retrieval tasks. To tackle the above problem, an unsupervised ensemble hashing is proposed to improve the ranking accuracy by assembling the diverse hash tables independently in this paper. We observe that the higher the accuracy is the larger diversity the base learner has, and the more effective the ensemble method is. Based on this principle, two special ensembles hashing approaches are proposed to increase diversity by bootstrap sampling with data-dependent methods. Especially, the results are better when the minimum Hamming distance is large and the variance of the Hamming distance is small. This proposed method is conducted in the experiments and the results show that it can achieve about 10%–25% performance compared with the baseline algorithm, which achieves competitive results with the state-of-the-art methods on the CIFAR-10 and LabelMe benchmarks.
Primitivity of the group of a cipher involving the action of the key-schedule

The algebraic structure of the group generated by the encryption functions of a block cipher depends on the key schedule algorithm used for generating the round keys. For such a reason, in general, studying this group does not appear to be an easy task. Previous works, focusing on the algebraic properties of groups associated to a cipher, have studied the group generated by the round functions of the cipher considering independent round keys. In this paper, we want to study the more realistic group generated by the encryption functions, where the key schedule satisfies certain requirements. In this context, we are able to identify sufficient conditions that permit to guarantee the primitivity of this group and the security of the cipher with respect to the partition-based trapdoor. This type of trapdoor has been recently introduced by Bannier et al. in 2016 and it is a generalization of that introduced by Paterson in 1999.
Social Media Users Send Promotional Links to Strangers: Legitimate Promotion or Security Vulnerability?

Nowadays, many users make money by publishing content on social media platforms. In order to attract users' attention, they often take measures to promote themselves. The security vulnerabilities in social media platforms may provide convenience for their user promotion work. We call this type of vulnerability the user promotion security vulnerability (UPSV). UPSV may cause unfair competition and endanger the interests of legitimate users and the social media platforms. Therefore it has great research significance to find and fix this vulnerability. In this paper, we propose a UPSV which widely exists in the function of sending messages to strangers of in-app chatting of many social media platforms. We first analyzed this vulnerability in some apps, and then YY app (China's largest live streaming platform) was chosen as the research object to verify the actual effect of the vulnerability on illegitimate user promotion. We took the method of promoting a target YY streamer through sending promotional links to viewers, and to improve promotion effect, we used the method of user preference learning to select viewers for promotion. The experimental results show that among the promoted viewers, more than 44% entered the target streamers' channels to watch live streaming, more than 21% followed the target steamers, and more than 13% gave gifts to the target steamers. It fully proves that this UPSV is real, exploitable and harmful, and we also proposed some fix suggestions to help the platforms to fix it.
DEIXIS IN DONALD TRUMP’S SPEECH TO UN GENERAL ASSEMBLY

The objective of the study is “to describe how deixis are used in Donald Trump’s Speech to UN General Assembly”. The design of this study is a descriptive qualitative design. Source of data collection in this research is the text of Donald Trump’s Speech to UN General Assembly done in 2018. After collecting data, the researcher found and selected data that the most of sentences or words have deixis types. After the data were reduced, then the next step is to display data, then, presenting data. Data summarizing was a means to know the result of the study. Here, the researcher made conclusion after completely identifying and analyzing the types of deixis and the most dominant one found in the speech. Based on data analysis, the researcher found five kinds of deixis in Donald Trump’s Speech to UN General Assembly, they are person deixis, spatial/place deixis, temporal/time deixis, discourse deixis and social deixis. The total number of deixis in this speech are 344 utterances. The deixis that mostly used by the Donald Trump is the person deixis with the frequency of 279 (81.10%).Keywords:
Multi-Party Secure Collaborative Filtering for Recommendation Generation

Recommender systems based on collaborative filter- ing technique generate the accurate and reliable predictions for customers by using their preferences about various products. Usage of user ratings has been a prevalent and successful practice for various e-commerce based websites, but launching a new e-commerce site will not benefit from this, as new site has no database of customers. A combined effort by existing companies and newly launched companies for recommendation generation can be beneficial for both companies and customers, if confidential data is protected. In literature, most of the existing techniques for secure prediction generation are based on data distortion and homomorphic encryption techniques, which may cause accuracy loss and high computation cost, respectively. To overcome these issues, this paper proposes a prediction generation scheme for the horizontally distributed data among different companies, which can help new entrants and existing sites, while preserving the privacy of the customer data. Analysis of the proposed scheme is done for parameters: privacy, accuracy, coverage and performance. The proposed scheme is secure, and the accuracy and coverage are improved due to collaboration of multiple parties. Moreover, the computation complexity of the proposed scheme is also low.
Exploring the Sentiment Analysis of Electric Vehicles Social Media Data by Using Feature Selection Methods

This study presents a recently obtained social media data set based upon the case study of Electric Vehicles (EV) and looks to implement a sentiment analysis (SA) in order to gain insights. This study uses two methods in order to fully analyze the public’s sentiment on EVs. First, we implement a SA tool in which we used to extract the sentiment of comments. Next we labeled the data with these sentiments obtained and classified them. While performing classification we found the problem of dimensionality and also explored the use of feature selection (FS) models in order to reduce the data set’s dimensionality. We found that the use of three FS models (Chi Squared, Information Gain and ReliefF) showed the most promising results when used alongside a logistic and support vector machines classification algorithm. the contributions of this paper are in providing an real-world example of social media text analytics which can be adopted in many other areas of research and business. Moving forward researchers can use the methodological approach in this paper to further refine and improve their own case uses in text analytics.
Spatial Distribution Extraction of Visible Light IDs for Supporting Robotic Rescue Efforts

An extraction method of spatial distribution of visible light IDs captured by using an image sensor is proposed. A unique spectral shape of a visible light ID with a non-return-to-zero pattern is exploited for distinguishing the ID signals from noises. A proof-of-concept experiment confirms that only the ID signals having a non-return-to-zero pattern can be extracted from an image sequence while DC and blinking components of visible lights are filtered out. Our method is suitable to simultaneously obtain precise situations and positions from rescuees without using special devices, and it can be used to support robotic rescue efforts after disasters.
An Analysis of Reversible Data Hiding Algorithms for Encrypted Domain

Reversible data hiding (RDH) also known as lossless data hiding is used to hide some secret information in a cover media to form the marked media, such that it is possible to exactly recover the original database, once hidden data is extracted. RDH in encrypted domain (RDH-ED) is being used popularly because encryption is an effective tool to provide privacy protection to the original database. In this paper, basic concept of RDH-ED and review of existing techniques of RDH in encrypted domain are presented. From the survey, it has been concluded that the aim of RDH-ED is to hide secret data in cipher domain without affecting the plain-text content and the error-free recovery of original content. It is new and emerging technology and its use is increasing in privacy preserving applications like in medical and military. Thus for future work, more work can be done on its methodology to obtain the feasible rate distortion performance and good visual quality of the recovered plain-text data.
Risk-sensitive motion planning for MAVs based on mission-related fault-tolerant analysis

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A Novel Set of Contextual Features for Web Spam Detection

Web spam is one of the significant problems facing search engines. It wastes sources and time, decreases the quality of results and leads to user discontent. The two main approaches to the detection spam web pages are link and content-based analysis. In this study, we mainly focus on content-based analysis in both user-visible text and the source code of a web page to propose a set of features for web spam detection. we explore the relationship between types and frequency of HTML (HyperText Markup Language) tags used in a web page source code. We also examine the structure of the URL as the other source of information. Finally, the content of a web page visible to the user is considered semantically in order to identify relevance among the number of the existing topics in the text as well as the coherence of a text using Latent Dirichlet Allocation. Experimental results show that the proposed features increases the index of balanced accuracy from 0.33 to 0.69 and improves the web spam detection rate.