Reviewerly/exHarmony huggingface dataset:
A Comprehensive Insight into Game Theory in relevance to Cyber Security

The progressively ubiquitous connectivity in the present information systems pose newer challenges tosecurity. The conventional security mechanisms have come a long way in securing the well-definedobjectives of confidentiality, integrity, authenticity and availability. Nevertheless, with the growth in thesystem complexities and attack sophistication, providing security via traditional means can beunaffordable. A novel theoretical perspective and an innovative approach are thus required forunderstanding security from decision-making and strategic viewpoint. One of the analytical tools whichmay assist the researchers in designing security protocols for computer networks is game theory. Thegame-theoretic concept finds extensive applications in security at different levels, including thecyberspace and is generally categorized under security games. It can be utilized as a robust mathematicaltool for modelling and analyzing contemporary security issues. Game theory offers a natural frameworkfor capturing the defensive as well as adversarial interactions between the defenders and the attackers.Furthermore, defenders can attain a deep understanding of the potential attack threats and the strategiesof attackers by equilibrium evaluation of the security games. In this paper, the concept of game theoryhas been presented, followed by game-theoretic applications in cybersecurity including cryptography.Different types of games, particularly those focused on securing the cyberspace, have been analysed andvaried game-theoretic methodologies including mechanism design theories have been outlined foroffering a modern foundation of the science of cybersecurity.
Deep Neural Networks with Knowledge Instillation

Deep neural network (DNN) has become an effective computational tool because of its superior performance in practice. However, the generalization of DNN still largely depends on the training data, no matter in quantity or quality. In this paper, we propose a knowledge instillation framework, named NeuKI, for feed-forward DNN, aiming to enhance learning performance with the aid of knowledge. This task is particularly challenging due to the complicated nature of knowledge and numerous variants of DNN architectures. To bridge the gap, we construct a separate knowledge-DNN faithfully encoding the instilled knowledge for joint training. The core idea is to regularize the training of target-DNN with the constructed knowledge-DNN, so that the instilled knowledge can guide the model training. The proposed NeuKI is demonstrated to be applicable to both knowledge rules and constraints, where rules are encoded by structure and constraints are handled by loss. Experiments are conducted on several real-world datasets from different domains, and the results demonstrate the effectiveness of NeuKI in improving learning performance, as well as relevant data efficiency and model interpretability.
Surveying the Development of Authorship Identification of Text Messages

People typically use multiple messaging systems and send text messages concurrently by different messaging systems such as SMS, email, Twitter, and Facebook.This means that these forms of messaging systems are highly integrated into people's everyday activities.Moreover, forensic science has had many challenges in many different kinds of crimes, differing from physical crimes to computer mediated crime activities.Identification of suspects has become crucial for law enforcement due in particularly to the anonymity that the internet and associated services provide and identify the ownership of messages.In this paper, survey the development of existing author identification have been done by establishing a comprehensive review of author identification to determining the core approaches and techniques utilized.Furthermore, this study also examines possible challenges in author identification and points to some of the open problems which need to be tackled.
A Top-Down Binary Hierarchical Topic Model for Biomedical Literature

Over the past two decades, a number of advances in topic modeling have produced sophisticated models that are capable of generating topic hierarchies. In particular, hierarchical Latent Dirichlet Allocation (hLDA) builds a topic tree based on the nested Chinese Restaurant Process (nCRP) or other sampling processes to generate a topic hierarchy that allows arbitrarily large branch structures and adaptive dataset growth. In addition, hierarchical topic models based on the latent tree model, such as Hierarchical Latent Tree Analysis (HLTA), have been developed over the last five years. However, these models do not work well in cases with millions of documents and hundreds of thousands of terms. In addition, the topic trees generated by these models are always poorly interpretable, and the relationships among topics in different levels are relatively simple. The biomedical literature, including Medline abstracts, has large-scale documents in two major categories: biological laboratory research and medical clinical research. We propose a top-down binary hierarchical topic model (biHTM) for biomedical literature by iteratively applying a flat topic model and adaptively processing subtrees of the hierarchy. The biHTM topic hierarchy of complete Medline abstracts with more than 14 topic node levels shows good bimodality and interpretability. Compared to hLDA and HLTA, biHTM shows promising results in experiments assessed in terms of runtime and quality.
Correction to: Ratings, rankings, research evaluation: how do schools of education behave strategically within stratified UK higher education?

The article Ratings, rankings, research evaluation: how do Schools of Education behave strategically within stratified UK higher education?, written by Marcelo Marques and Justin J. W. Powell, was originally published electronically in SpringerLink on 16 August 2019 without Open Access.
Process approach in teaching of esl writing: teacher’s assistance and its practicality in real classroom

Writing in a first and second language are two different contexts and involve significant different composing process. In general, most ESL students have difficulties in the rhetorical and linguistics aspects of writing because of their prior education and education backgrounds (Mengjie Wei, 2017:16). Given the complexity of writing tasks, it is not surprising that L2 writers may experience various writing difficulties ranging from mechanical up to the content or idea development level. Although writing is teachable, the conversion of thought into written form is a difficult activity that requires organized pedagogical approaches and complementary skills. This is a case study which discusses the practices of process approach in teaching ESL writing and its practicality. The findings were based on a long-term observation and interview of three form 4 students and a teacher in Malaysia rural area school. Students’ writings were analysed to show the students’ development when activities based on Process approach were implemented. Findings managed to identify five different techniques used by the teacher in trying to implement process approach in writing. This process process managed to show the students the fallacy of assuming too much in writing and the need to provide more detail, or where they were being repetitious of ideas and get off the track. Changes could be seen in terms of words and elaboration in the second draft throughout the learning process until the compositions were produced.
Organizational Silence and its Relation to Organizational Justice among School Workers in Sultanate of Oman

The study aimed to identify the levels of organizational silence and organizational justice among school employees in Oman, and to investigate possible differences based on gender, school type and years of experience.The dimensions of organizational justice that are more capable of predicting organizational silence were also identified.The researchers used the descriptive correlation approach.Two measures were used, one was for the organizational justice )distributive, procedural and interactional justice(, and the other was to measure the organizational silence )acquiescent, defensive and protective silence(.The sample included 565 teachers.The results showed that there was low level of organizational silence, while high level of organizational justice was identified.No statistical differences were found in the organizational silence levels among teachers according to the three variables.However, statistical gender differences were found in the levels of organizational justice in favour of females.No statistical differences were found due to school type and years of experience.The results also showed that the acquiescent and defensive silence can be predicted by the procedural and interactional justice, while the protective silence can be predicted through the interactional justice.
Improving Performance of the Multiplexed Colored QR Codes

The vast popularity and useful applications of the QR code were the incentives that encourage the research towards improving its storage capacity and performance.A colored quick response (QR) algorithm is proposed, devised and tested to expand the capacity of the black and white modules to hold colored modules that can fold as many as those available in the 8 bits red-Green-Blue (RGB) color code.Fast Multiplexing Technique (FMT) is established to improve the performance and the storage capacity of the QR color code by multiplexing the black/white QR codes into RGB color shades then folding them into the QR code Modules.Comparative experiments with the classical multiplexing technique -MUX system-proved that FMT has a much better performance, (exponentially faster), while maintaining the capacity multitude to 24 folds that of the classical QR code.
Counterfactual protocol within device independent framework and its insecurity

Abstract We consider the counterfactual protocol proposed in Phys. Rev. Lett ., 103 , 230501 (2009) within a device independent framework and show how its security can easily be compromised. Capitalising on the fact that the protocol is based on the use of a single photon entanglement phenomenon, we propose an equivalent protocol. It can be made secure within such a pessimistic framework against a supra-quantum Eve limited only by the no-signalling principle. The equivalence the protocol demonstrates the possibility of device independent framework for counterfactual quantum cryptography.
SMART-In English: Learn English Using Speech Recognition

English is an international language and important to learn. For someone learning English sometimes is a difficulty, especially in pronunciation. Therefore, SMART-In is a prototype of Android App that uses Speech Recognition technology by utilizing services from the Cloud Speech API (Application Programming Interface). SMART-In English can be used as an alternative to English learning, especially in the pronunciation of a word. Using speech recognition can display the score of the pronunciation spoken by the user, recorded, show a level the pronunciation of the word and display the correct pronunciation.
Hidden Markov Random Field for Multi-Agent Optimal Decision in Top-Coal Caving

Applying model-based learning for the optimal decision of the multi-agent system is not trivial due to the expensive price or even the impossibility of obtaining the ground truth for training the model of the complex environment. Such as learning the optimal action of hydraulic supports in the top-coal caving, the optimal action could not accessible as the ground truth of the corresponding state in the intricate processes. Regarding the latent ground truth as the hidden variable is an effective method in the hidden Markov model. This paper extends the hidden variable of ground truth to the multi-agent system and proposes the hidden Markov random field (HMRF) model with reinforcement learning for optimizing the action decision of the multi-agent. In the HMRF model, the input states and the output actions of the multi-agent are considered as an observable random field and a latent Markov random field, respectively. Based on the HMRF model, the optimal decision is inferred by the maximum posterior probability with the prior probability obtained by Q-learning. Meanwhile, the parameters of the HMRF model are estimated by the expectation maximum algorithm. In the experiment, the top-coal caving demonstrates the effectiveness of the proposed method that the recall of top-coal is improved prominently with a very small price of increasing the rock-rate. Furthermore, the proposed method is employed to deal with the predator-preys problem in the gym. The experiment result shows that the communication between agents by the HMRF increases the reward of the preys.
The Influences of Audio Visual Media And Vocabulary Mastery towards English Learning Achievement of EFL Students

This study was an attempt to determine the impact of interaction between teaching media and vocabulary mastery towards students’ learning achievement in colleges of education at Jambi University. This is a quasi-experimental study with nonequivalent control and experimental group design. Based on the test for normality, homogeneity, and analysis of variance to the data students’ achievement score of experiment class can be concluded that, there are significant differences of learners’ achievement between before and after treatment in experimental class using audio-visual media. Then, there are significant differences between the learners’ vocabulary mastery on their learning achievement. The result of this study showed; 1. There is a significant effect between teaching media toward learners’ achievement. Test result obtained that p-value 0,015 (&lt;0,05) 2. Vocabulary mastery gave the significant effect to the learners’ achievement. Test result obtained that p-value for vocabulary mastery is 0,000 (&lt;0,05). 3. There is no interaction influence between the teaching media and vocabulary mastery towards learners’ achievement. Test result retrieved that p-value is 0,948 (&gt;0,05).
Analysis of the Regional Secondary Vocational Education System by Assessing its Sustainability and Balance

The article assesses the systemic risks of regional secondary vocational education in terms of its sustainability and balance. In addition, attention is focused on the situation in St. Petersburg and the Leningrad Region.
Deep Learning based, a New Model for Video Captioning

Visually impaired individuals face many difficulties in their daily lives.In this study, a video captioning system has been developed for visually impaired individuals to analyze the events through real-time images and express them in meaningful sentences.It is aimed to better understand the problems experienced by visually impaired individuals in their daily lives.For this reason, the opinions and suggestions of the disabled individuals within the Altınokta Blind Association (Turkish organization of blind people) have been collected to produce more realistic solutions to their problems.In this study, MSVD which consists of 1970 YouTube clips has been used as training dataset.First, all clips have been muted so that the sounds of the clips have not been used in the sentence extraction process.The CNN and LSTM architectures have been used to create sentence and experimental results have been compared using BLEU 4, ROUGE-L and CIDEr and METEOR.
Adapted Lesk Algorithm based Word Sense Disambiguation using the Context Information

The process of identifying the meaning of a polysemous word correctly from a given context is known as the Word Sense Disambiguation (WSD) in natural language processing (NLP).Adapted Lesk algorithm based system is proposed which makes use of knowledge based approach.This work utilizes WordNet as the knowledge source (lexical database).The proposed system has three units -Input query, Pre-Processing and WSD classifier.Task of input query is to take the inputs sentence (which is an unstructured query) from the user and render it to the pre-processing unit.Pre-processing unit will convert the received unstructured query into a structured query by adding some features such as Part of Speech (POS) tagging, grammatical identification (Subject, Verb, and Object) and this structured query is transferred to the WSD classifier.WSD classifier uniquely identifies the sense of the polysemous word using the context information of the query and the lexical database.
A Parallel and Reverse Learn++.NSE Classification Algorithm

There are lots of typical applications of classification learning for accumulated big data in the nonstationary environments.It is very necessary and urgent to study the algorithms that can carry out classification learning efficiently in these environments.The recently proposed algorithm, named Learn++.NSE, has made an important breakthrough, which is one of the important research achievements in this research field.However, the Learn++.NSE algorithm adopts a serial ensemble mechanism, and its execution efficiency needs to be further improved when facing the long-term accumulated big data.A Parallel and Reverse Learn++.NSE algorithm, abbreviated as PRLearn++.NSE, is proposed in this paper by changing the ensemble mechanism of the base-classifiers, which uses the old base-classifiers as a supplement to the new base-classifier.It constructs a fast and parallel ensemble mechanism.The experimental results on the artificially generated dataset and real dataset show that the proposed PRLearn++.NSE algorithm can greatly improve the efficiency of ensemble classification learning under the premise of obtaining the approaching classification accuracy of Learn++.NSE algorithm and it is very suitable for fast ensemble classification learning for the long-term accumulated big data.
Enhanced Differential Cache Attacks on SM4 with Algebraic Analysis and Error-Tolerance

Block ciphers with Feistel structures are vulnerable to a specific type of cache attacks named differential cache attacks. The attacks leverage side-channel leakages from cache and differential property of cipher component to reveal the master key of cipher. In this paper, we combine the algebraic analysis to enhance the attacks, and propose a novel method named Algebraic Differential Cache Attack (ADCA). By converting both cipher and cache leakages to algebraic equations, ADCA can reveal the cipher key automatically with the help of the SAT solver, which allows the analysis on much deeper rounds and makes a considerable reduction in attack complexity. When it is applied to the block cipher SM4, 10 plaintexts are enough to reveal the master key in 8-rounds analysis, while the traditional differential cache attack needs 20 ones. Finally, to eliminate the impact from noise, an error-tolerant method is proposed to deduce cache events from the leakage traces. It vastly enhances the robustness of attack, and makes the attack more practical. The experimental results show that the error-tolerant ADCA can correctly reveal the master key even when the uncertainty rate of cache events reaches to 60%.
Dense Word Representation Utilization in Indonesian Dependency Parsing

Available Indonesian dependency parsers can be considered worse than other languages’ parsers that have been researched thoroughly. Currently, Indonesia dependency parsers can’t reliably parse sentences with gerund(s) and/or ellipsis correctly. This is because of the sparse feature representation that causes difficulty in parsing these types of sentences. In this research, dense representation is proposed for Indonesian dependency parser. The use of dense word representation may allow better generalization and gives more information regarding the words to be parsed, which allows a more accurate parsing. The scope of the dependency parsing in this research is limited to well-formed Indonesian sentences, using the local transition-based parsing. Based on our experiments, we found that using word embedding instead of sparse word representation increases parsing accuracy significantly.
A Semi-Supervised Paraphrase Identification Model Based on Multi-Granularity Interaction Reasoning

Conventional paraphrase identification (PI) models based on deep learning usually focus on text representation and ignore the mining and matching of multi-granular interaction features. In addition, supervised learning relies on a large labeled data. However, labeled training set for PI is small in comparison with the high complexity of the task. To solve the problems, we propose a semi-supervised deep learning framework for PI. We use a neural encoder with word-by-word attention mechanism to reason equivalence or contradiction over pairs of words, phrases and sentences. We employ a two-stage training procedure. First, we use a language modeling objective to learn the initial parameters on the unlabeled corpora of more than one million pairs of sentences. This is followed by a supervised training, where we adapt these parameters to a specific classification task with labeled data. Experimental results on MRPC (Microsoft Research Paraphrase Corpus) and SICK (Sentences Involving Compositional Knowledge) datasets demonstrate the effectiveness of our approach. Compared with the previous neural network models, we achieve absolute improvements in accuracy of 7.6% and F1 of 5.4% on MRPC, Pearson's r of 4.5% and Spearman's ρ of 5.1% on SICK.
An Improved RDWT-based Image Steganography Scheme with QR Decomposition and Double Entropy

This paper introduces an improved RDWT-based image steganography with QR decomposition and double entropy system.It demonstrates image steganography method that hides grayscale secret image into grayscale cover image using RDWT, QR decomposition and entropy calculation.The proposed scheme made use of the human visual system (HVS) in the embedding process.Both cover and secret image are being segmented into non-overlapping blocks with identical block size.Then, entropy values generated from every image block will be sorted from the lowest value to the highest value.The embedding process starts by embedding the secret image block with lowest entropy value into the cover image block with lowest entropy value.The process goes on until all image blocks have been embedded.Embedding secret image into cover image according to the entropy values causes differences that HVS can less likely to detect because of the small changes on image texture.By applying the double entropy system, proposed scheme managed to achieve a higher PSNR value of 60.3773 while previous work gave a value of 55.5771.In terms of SSIM value, proposed scheme generated a value of 0.9998 comparing to previous work's value of 0.9967.The proposed scheme eliminated the false-positive issue and required low computational time of only 0.72 seconds for embedding and 1.14 seconds for extraction process.Also, it has shown better result compared to previous work in terms of imperceptibility.