Offense 掌握胜利之剑

足球永远是在进攻与防守之间来进行的,好的防守,虽然可以赢得自身的信心,但并不能赢得一场比赛,而进攻才是获得比赛胜利的唯一手段。许多相识的朋友,经常聊天说,进攻可以赢得比赛,而防守可以赢得冠军,话虽如此,但是在WE的虚拟绿茵场中,如果一味的防守,被动挨打,其结果只能落得失败的下场。每个人都有一套属于自己的进攻思路,而进攻思路的多寡,决定了在比赛中破门得分的成功率。今天和大家讨论的,就是如何来攻破对手球门的进球之道。

Deep-BERT:Transfer Learning for Classifying Multilingual Offensive Texts on Social Media

Offensive messages on social media,have recently been frequently used to harass and criticize people.In recent studies,many promising algorithms have been developed to identify offensive texts.Most algorithms analyze text in a unidirectional manner,where a bidirectional method can maximize performance results and capture semantic and contextual information in sentences.In addition,there are many separate models for identifying offensive texts based on monolin-gual and multilingual,but there are a few models that can detect both monolingual and multilingual-based offensive texts.In this study,a detection system has been developed for both monolingual and multilingual offensive texts by combining deep convolutional neural network and bidirectional encoder representations from transformers(Deep-BERT)to identify offensive posts on social media that are used to harass others.This paper explores a variety of ways to deal with multilin-gualism,including collaborative multilingual and translation-based approaches.Then,the Deep-BERT is tested on the Bengali and English datasets,including the different bidirectional encoder representations from transformers(BERT)pre-trained word-embedding techniques,and found that the proposed Deep-BERT’s efficacy outperformed all existing offensive text classification algorithms reaching an accuracy of 91.83%.The proposed model is a state-of-the-art model that can classify both monolingual-based and multilingual-based offensive texts.

Improved Ant Lion Optimizer with Deep Learning Driven Arabic Hate Speech Detection

Arabic is the world’s first language,categorized by its rich and complicated grammatical formats.Furthermore,the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for verbs and nouns.The Arabic language consists of distinct variations utilized in a community and particular situations.Social media sites are a medium for expressing opinions and social phenomena like racism,hatred,offensive language,and all kinds of verbal violence.Such conduct does not impact particular nations,communities,or groups only,extending beyond such areas into people’s everyday lives.This study introduces an Improved Ant Lion Optimizer with Deep Learning Dirven Offensive and Hate Speech Detection(IALODL-OHSD)on Arabic Cross-Corpora.The presented IALODL-OHSD model mainly aims to detect and classify offensive/hate speech expressed on social media.In the IALODL-OHSD model,a threestage process is performed,namely pre-processing,word embedding,and classification.Primarily,data pre-processing is performed to transform the Arabic social media text into a useful format.In addition,the word2vec word embedding process is utilized to produce word embeddings.The attentionbased cascaded long short-term memory(ACLSTM)model is utilized for the classification process.Finally,the IALO algorithm is exploited as a hyperparameter optimizer to boost classifier results.To illustrate a brief result analysis of the IALODL-OHSD model,a detailed set of simulations were performed.The extensive comparison study portrayed the enhanced performance of the IALODL-OHSD model over other approaches.

Improved Attentive Recurrent Network for Applied Linguistics-Based Offensive Speech Detection

Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech therapy.The ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate speech.Amongst the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the Internet.Based on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and profanity.Amongst these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or violence.Machine Learning(ML)techniques have recently been applied to recognize hate speech-related content.The current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social media.The GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate speech.In the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding processes.Then,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and classification.At last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition process.To depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was conducted.The comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.

PATERNITY IDENTIFICATION ON ABORTED FETAL MATERIAL

After a rape, woman who is pregnamt often elect to abort the fetus.The authors describe 2 cases witers genetic markers ABO, MN,Rh,PGM1,EsD,ACP,GLOI,GPT,HP,Cc and HLA-A,-B were tasted on the aborted fetal material to provide evidence of the genetic constitution of the rapist The results showed that this type of testing is possible for prenatal paternity identification.

Game Theoretical Secure Wireless Communication for UAV-assisted Vehicular Internet of Things

With the ever-expanding applications of vehicles and the development of wireless communication technology,the burgeoning unmanned aerial vehicle(UAV)assisted vehicular internet of things(UVIoTs)has emerged,where the ground vehicles can experience more efficient wireless services by employing UAVs as a temporary mobile base station.However,due to the diversity of UAVs,there exist UAVs such as jammers to degenerate the performance of wireless communication between the normal UAVs and vehicles.To solve above the problem,in this paper,we propose a game based secure data transmission scheme in UVIoTs.Specifically,we exploit the offensive and defensive game to model the interactions between the normal UAVs and jammers.Here,the strategy of the normal UAV is to determine whether to transmit data,while that of the jammer is whether to interfere.We then formulate two optimization problems,i.e.,maximizing the both utilities of UAVs and jammers.Afterwards,we exploit the backward induction method to analyze the proposed countermeasures and finally solve the optimal solution.Lastly,the simulation results show that the proposed scheme can improve the wireless communication performance under the attacks of jammers compared with conventional schemes.

Consensus-Based Ensemble Model for Arabic Cyberbullying Detection

Due to the proliferation of internet-enabled smartphones,many people,particularly young people in Arabic society,have widely adopted social media platforms as a primary means of communication,interaction and friendship mak-ing.The technological advances in smartphones and communication have enabled young people to keep in touch and form huge social networks from all over the world.However,such networks expose young people to cyberbullying and offen-sive content that puts their safety and emotional well-being at serious risk.Although,many solutions have been proposed to automatically detect cyberbully-ing,most of the existing solutions have been designed for English speaking con-sumers.The morphologically rich languages-such as the Arabic language-lead to data sparsity problems.Thus,render solutions developed for another language are ineffective once applied to the Arabic language content.To this end,this study focuses on improving the efficacy of the existing cyberbullying detection models for Arabic content by designing and developing a Consensus-based Ensemble Cyberbullying Detection Model.A diverse set of heterogeneous classifiers from the traditional machine and deep learning technique have been trained using Arabic cyberbullying labeled dataset collected fromfive different platforms.The outputs of the selected classifiers are combined using consensus-based decision-making in which the F1-Score of each classifier was used to rank the classifiers.Then,the Sigmoid function,which can reproduce human-like decision making,is used to infer thefinal decision.The outcomes show the efficacy of the proposed model comparing to the other studied classifiers.The overall improvement gained by the proposed model reaches 1.3%comparing with the best trained classifier.Besides its effectiveness for Arabic language content,the proposed model can be generalized to improve cyberbullying detection in other languages.

U.S. National Strategy: betweem Offensive and Integration

As the hegemonic country in the world system, U. S. national strategy is global and multi-directional. Since September 11, 2001, America has made series of adjustments in its global strategy, including adjustment in security focus and change of security means. Come what may, since America has become the superpower in the world system, its national strategy has always been between offensive and integration. In a sense, current American strategy is the combination of the two. Although different government would tilt toward one direction, none

Bullying Among School Students in the UAE Society

The purpose of this study is to research the bullying phenomenon among school students in the UAE society. This is done through showing the extent of prevalence of bullying, the rate of recurrence of bullying incidences, the most widespread forms of bullying among school children in the Emirati society, and finally, the variation with regards to the prevalence and forms of bullying as related to the student’s gender. Therefore, this study aims to probe the prevalence of this phenomenon in schools, and the frequency of bullying cases as well as its forms. For this purpose, a questionnaire was designed and conducted on a sample size of 1,309 students of both genders. The data were later analyzed using descriptive statistical and analytical metrics that are appropriate for the variables’ measurement level, and which achieve the objectives of the study. The study found that a third of the students (33.3%) were involved in bullying incidents. Furthermore, it was found that 14.2% were the party causing the bullying incident, while 19.1% were the party upon which bullying was inflicted. The study also revealed that within school premises the places where bullying was most likely to occur are corridors, and the places which students felt were the least safe are the closed spaces. As for the forms of bullying students are subjected to, offensive name calling or insults came in first place, followed by cyber/online bullying. The young age and smaller size of a student were among the most important motivators for students to bully him/her. It was also found that 32.8% of students who are exposed to bullying respond in a similar manner. The study showed that most of the bullied students (78.4%) know the person doing the bullying, the females being more cognizant of the perpetrator bullying them. Moreover, 40.7% of the students believe that the teachers and other employees are aware of the bullying taking place, female students to a greater extent than males in this regard. In the study sample, the students believe that strong and strict school administration would contribute to stopping the bullying phenomenon. The study additionally concluded a number of recommendations to reduce this phenomenon.

National Slurs on Dutch

National slur is a linguistic form expressing unequal treatment of one ethnic group by another.It is the direct result of national xenophobia,which is usually formed through semantic degradation by giving nicknames or derogatory terms and making proverbs.The associative meaning of a nation-specific word is often rendered to give insult to this ethnic group in confrontation or opposition with the nation in dominance of their shared language.This paper revolves around the formation,application,cultural connotation,and the proper understanding of national slurs on Dutch.After a thorough analysis of national slurs on Dutch as well as a careful induction and deduction about words of offensive nationality concerning "Dutch",the perjoration of word meaning in Dutch expressions can be clearly pictured in this paper.Thus,the national slurs on Dutch can reveal itself more profoundly.Born the cultural awareness of national slur in mind under such a pretext,special attention can be adequately given in understanding and conveying the cultural connotation of Dutch expressions.Therefore,the study of national slurs on Dutch will effectively facilitate cultural communication.

Flexible Defense Succeeds Creative Attacks!—A Simulation Approach Based on Position Data in Professional Football

Introduction: The key to success is finding the perfect mixture of tactical patterns and sudden breaks of them, which depends on the behavior of the opponent team and is not easy to estimate by just watching matches. According to the specific tactical team behavior of “attack vs. defense” professional football matches are investigated based on a simulation approach, professional football matches are investigated according to the specific tactical team behavior of “attack vs. defense.” Methods: The formation patterns of all the sample games are categorized by SOCCER© for defense and attack. Monte Carlo-Simulation can evaluate the mathematical, optimal strategy. The interaction simulation between attack and defense shows optimal flexibility rates for both tactical groups. Approach: A simulation approach based on 40 position data sets of the 2014/15 German Bundesliga has been conducted to analyze and optimize such strategic team behavior in professional soccer. Results: The results revealed that both attack and defense have optimal planning rates to be more successful. The more complex the success indicator, the more successful attacking player groups get. The results also show that defensive player groups always succeed in attacking groups below a specific planning rate value. Conclusion: Groups are always succeeding. The simulation-based position data analysis shows successful strategic behavior patterns for attack and defense. Attacking player groups need very high flexibility to be successful (stay in ball possession). In contrast, defensive player groups only need to be below a defined flexibility rate to be guaranteed more success.

Male reproductive suppression： not a social affair

In the animal kingdom there are countless strategies via which males optimize their reproductivesuccess when faced with male-male competition. These male strategies typically fall into two maincategories： pre- and post-copulatory competition. Within these 2 categories, a set of behaviors,referred to as reproductive suppression, is known to cause inhibition of reproductive physiologyand/or reproductive behavior in an otherwise fertile individual. What becomes evident when con-sidering examples of reproductive suppression is that these strategies conventionally encompassreproductive interference strategies that occur between members of a hierarchical social group.However, mechanisms aimed at impairing a competitor＇s reproductive output are also present innon-social animals. Yet, current thinking emphasizes the importance of sociality as the primarydriving force of reproductive suppression. Therefore, the question arises as to whether there is anactual difference between reproductive suppression strategies in social animals and equivalentpre-copulatory competition strategies in non-social animals. In this perspective paper we explore abroad taxonomic range of species whose individuals do not repeatedly interact with the same indi-viduals in networks and yet, depress the fitness of rivals. Examples like alteration of male repro-ductive physiology, female mimicry, rival spermatophore destruction, and cementing the rival＇sgenital region in non-social animals, highlight that male pre-copulatory reproductive suppressionand male pre-copulatory competition overlap. Finally, we highlight that a distinction between malereproductive interference in animals with and without a social hierarchy might obscure importantsimilarities and does not help to elucidate why different proximate mechanisms evolved. We there-fore emphasize that male reproductive suppression need not be restricted to social animals.

Alliance Free and Alliance Cover Sets

A defensive （offensive） k-alliance in F = （V, E） is a set S C V such that every v in S （in the boundary of S） has at least k more neighbors in S than it has in V ／ S. A set X C_ V is defensive （offensive） k-alliance free, if for all defensive （offensive） k-alliance S, S／ X ≠ 0, i.e., X does not contain any defensive （offensive） k-alliance as a subset. A set Y C V is a defensive （offensive） k-alliance cover, if for all defensive （offensive） k-alliance S, S ∩ Y ≠ 0, i.e., Y contains at least one vertex from each defensive （offensive） k-alliance of F. In this paper we show several mathematical properties of defensive （offensive） k-alliance free sets and defensive （offensive） k-alliance cover sets, including tight bounds on their cardinality.