Reviewerly/exHarmony huggingface dataset:
Pseudo-Honeypot: Toward Efficient and Scalable Spam Sniffer

Honeypot-based spammer gathering solutions usually lack attribute variability, deployment flexibility, and network scalability, deemed as their common drawbacks. This paper explores pseudo-honeypot, a novel honeypot-like system to overcome such drawbacks, for efficient and scalable spammer sniffing. The pseudo-honeypot takes advantage of user diversity and selects normal accounts, with attributes that have the higher potential of attracting spammers, as the parasitic bodies. By harnessing such category of users, pseudo-honeypot can monitor their streaming posts and behavioral patterns transparently. When compared with its traditional honeypot counterpart, the proposed solution offers the substantial advantages of attribute variability, deployment flexibility, network scalability, and system portability. Meanwhile, it offers a novel method to collect the social network dataset that has a higher probability of including spams and spammers, without being noticed by advanced spammers. We take the Twitter social network as an example to exhibit its system design, including pseudo-honeypot nodes selection, monitoring, feature extraction, ground truth labeling, and learning-based classification. Through experiments, we demonstrate the efficiency of pseudo-honeypot in terms of spams and spammers gathering. In particular, we confirm our solution can garner spammers at least 19 times faster than the state-of-the-art honeypot-based counterpart.
A Tableau Calculus for Non-clausal Maximum Satisfiability

We define a non-clausal MaxSAT tableau calculus. Given a multiset of propositional formulas $$\phi $$ , we prove that the calculus is sound in the sense that if the minimum number of contradictions derived among the branches of a completed tableau for $$\phi $$ is m, then the minimum number of unsatisfied formulas in $$\phi $$ is m. We also prove that it is complete in the sense that if the minimum number of unsatisfied formulas in $$\phi $$ is m, then the minimum number of contradictions among the branches of any completed tableau for $$\phi $$ is m. Moreover, we describe how to extend the proposed calculus to deal with hard and weighted soft formulas.
A Clustering based Approach for Contextual Anomaly Detection in Internet of Things

Internet of Things (IoT) is a network which connects different communication devices with the internet to attain quick, robust and realtime information transfer and communication, achieving intelligent management.IoT is still in its infancy so it faces numerous challenges varying from data management to security concerns.Sensors generate enormous quantities of data that need to be handled efficiently to have successful deployment of IoT applications.Concerning data management, a great challenge that faces the IoT environment is the detection of contextual anomalies.Contextual anomaly detection is a sophisticated task because the context has to be taken into consideration in the anomaly detection process rather than checking only the deviation of the data value as in point anomaly detection.As a result, in this paper, a novel clustering based algorithm is proposed to detect contextual anomalies in Internet of Things.Attributes were separated into two different categories, namely contextual attributes and behavioral attributes.K-Means clustering technique was applied on the contextual and behavioral attributes separately, then the intersection between the contextual and behavioral clusters was used to detect the contextual anomalies.Moreover, the algorithm was applied on a real room occupation dataset of size around 20,000 records and the experiments showed promising results.
PENGEMBANGAN MEDIA PEMBELAJARAN MONOPOLI PADA MATERI SISTEM PENCERNAAN DI SMP

This research was aims to developed, feasibility and compare differences in learning out-comes before and after used Monopoly Games Smart (MGS) learning media on digestive system topic in junior high school IBA Palembang. Research applies Research and Development methods with steps, namely define, design, and development. Egibility was based on the evaluation of media expert and small scale trials. Data collection in this study used interviews, questionnaire and documentation. Analysis tehcnique were including analysis of product data.. The results show that the Monopoly Games Smart (MGS) learning media has significant effect in improving the mastery of concept. Based on students mastery of concept showed that the implementation of learning using Monopoly Games Smart (MGS) is better than convensional. The learning process in the digestive system topic is more effective and student centered which makes students more active in learning and makes the learning process more meaningful. If the learning response is good, mastery of the concept will be good.
Analisis Sentimen Masyarakat terhadap Hasil Quick Count Pemilihan Presiden Indonesia 2019 pada Media Sosial Twitter Menggunakan Metode Naive Bayes Classifier

&lt;p&gt;Indonesia is one of the countries that adheres to a democratic system. In the course of a democratic system it is marked by periodic general elections. In 2019 Indonesia held a general election simultaneously to elect the President, DPR, DPRD and DPD. After the election, a lot of opinion arise within the community, including on social media twitter. One of the topics discussed was the results of the quick count of the presidential election. Therefore, a method that can be used to analyze sentiment from the quick count opinion is needed, that is naive Bayes method. The aims of this study are to find the best naive Bayes model and to classify sentiments. The result shows the best accuracy of 82.90% with α = 0.05. The classification obtained is 34.5% (471) positive tweets and 65.5% (895) negative tweets on the results of the quick count.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords :&lt;/strong&gt; sentiment analysis, naive Bayes classifier, elections, quick count&lt;/p&gt;
ST MADAR 2019 Shared Task: Arabic Fine-Grained Dialect Identification

This paper describes the solution that we propose on MADAR 2019 Arabic Fine-Grained Dialect Identification task.The proposed solution utilized a set of classifiers that we trained on character and word features.These classifiers are: Support Vector Machines (SVM), Bernoulli Naive Bayes (BNB), Multinomial Naive Bayes (MNB), Logistic Regression (LR), Stochastic Gradient Descent (SGD), Passive Aggressive(PA) and Perceptron (PC).The system achieved competitive results, with a performance of 62.87% and 62.12% for both development and test sets.
Multi-Granularity Representations of Dialog

Neural models of dialog rely on generalized latent representations of language.This paper introduces a novel training procedure which explicitly learns multiple representations of language at several levels of granularity.The multi-granularity training algorithm modifies the mechanism by which negative candidate responses are sampled in order to control the granularity of learned latent representations.Strong performance gains are observed on the next utterance retrieval task using both the MultiWOZ dataset and the Ubuntu dialog corpus.Analysis significantly demonstrates that multiple granularities of representation are being learned, and that multi-granularity training facilitates better transfer to downstream tasks.
Argumentative Evidences Classification and Argument Scheme Detection Using Tree Kernels

The purpose of this study is to deploy a novel methodology for classifying different argumentative support (supporting evidences) in arguments, without considering the context.The proposed methodology is based on the idea that the use of Tree Kernel algorithms can be a good way to discriminate between different types of argumentative stances without the need of highly engineered features.This can be useful in different Argumentation Mining sub-tasks.This work provides an example of classifier built using a Tree Kernel method, which can discriminate between different kinds of argumentative support with a high accuracy.The ability to distinguish different kinds of support is, in fact, a key step toward Argument Scheme classification.
A Sequence Anomaly Detection Approach Based on Isolation Forest Algorithm for Time-Series

Anomalous behavior detection in many applications is becoming more and more important, especially for computer security and sensor networks domains, in which data are typical time-series. However, the sequence anomaly detection for time-series data exists lots of problems, for example, there is no anomalous point in time series sequence but the whole sequence may be anomalous. In this paper, we use the sliding window framework to split time-series into sequences and taking into account the time-series statistical features of the sequence, proposing a novel sequence anomaly detection algorithm based on iForest, namely iForestFS. The experimental results are performed on three real-world data sets derived from UCI repository demonstrate that the proposed algorithm can effectively detect anomalous sequence of time-series data.
TANTANGAN PENDIDIK DI ERA MILLENNIAL

Millennial or Generation Y is a group of people born after Generation X, namely people born in the range of 1980-2000. Now, this generation is between the ages of 17-37. There are some millennial students who have dropped out of school or graduated from universities and entered to work, but most of them are still in the school system. The image and concept of the teacher in contemporary society is very much different from the concept of the past. The past teacher means knowledgeable, wise, and prudent. Nowadays, teachers are seen as educational functionaries who are tasked with teaching on the basis of certain scientific and academic qualifications. With this task, the teacher receives material rewards from the state or the education manager. Thus, the most important factor in the teaching profession is scientific and academic qualifications. Meanwhile, wisdom and prudence which are moral attitudes and behaviors are no longer significant. In the classical concept, moral factors are above teacher qualifications. In general, teachers should understand the social changes that exist in the present. Teachers must not stop learning about new things. Global challenges in the current era are also different from the global challenges from the past. Whatever steps and methods carried out at school are certainly aimed at forming character and preparing quality human resources in Indonesia. In this paper, the author will outline the challenges for teachers in the millennial era and the efforts made in dealing with these challenges. Previously the author also explained about the millennial generation, and the professionalism of teachers. Â
AMBTC based high payload data hiding with modulo-2 operation and Hamming code

An efficient data hiding method with modulo-2 operation and Hamming code (3, 2) based on absolute moment block truncation coding (AMBTC) is proposed. In order to obtain good data hiding performance, different textures are assigned to different embedding strategies. The AMBTC compressed codes are divided into smooth and complex blocks according to texture. In the smooth block, the secret data and the four most significant bits plane of the two quantization levels are calculated using modulo-2 operation to replace the bitmap in order to improve the security of data transmission. Moreover, Hamming code (3, 2) is used to embed the two additional secret bits in the three significant bits planes of the two quantization levels. In the complex block, one secret bit is embedded by swapping the order of two quantization levels and flipping the bitmap. Experimental results show that the proposed method achieves higher capacity than the existing data hiding methods and maintains good visual quality.
A Study of Economic Value Estimation on Cryptocurrency Value back by Gold, Methods, Techniques, and Tools

After Bitcoin Introduced around the world, many Cryptocurrencies was created that followed the standard of bitcoin. The use of Bitcoin or other Cryptocurrency as a currency is also an interesting study from an Islamic economic perspective. They tried to use gold with value back by gold , which gold itself is famous for its exchange rate stability. From abu bakar There is a need for monitoring organization of the cryptocurrency, to controlling from Riba (Interest), Maysir (gambling) and ghahar (Uncertainty). To solve this problem there is a need a tool that can predict with certainty based on valid historical data, to produce accurate prediction results and produce Economic value estimations that are close to Gold real value. With the results we can monitoring day by day, see next day value and continuously based on Cryptocurrency with value back by gold, and see what other impact influences the value by looking the factor negative or positive with sentiment analysis. In the last section we discuss and provide method that we analyse from previous work to produce method to estimate value cryptocurrency value back by gold.
Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling

Jaccard similarity is widely used as a distance measure in many machine learning and search applications. Typically, hashing methods are essential for the use of Jaccard similarity to be practical in large-scale settings. For hashing binary (0/1) data, the idea of one permutation hashing (OPH) with densification significantly accelerates traditional minwise hashing algorithms while providing unbiased and accurate estimates. In this paper, we propose a strategy named “re-randomization” in the process of densification that could achieve the smallest variance among all densification schemes. The success of this idea naturally inspires us to generalize one permutation hashing to weighted (non-binary) data, which results in the socalled “bin-wise consistent weighted sampling (BCWS)” algorithm. We analyze the behavior of BCWS and compare it with a recent alternative. Extensive experiments on various datasets illustrates the effectiveness of our proposed methods.
Cuckoo Search Algorithm With Neighborhood Attraction for Numerical Optimization

Cuckoo search (CS) algorithm has been proved to be an effective method in solving numerical optimization problems. Nevertheless, with regard to Levy flight, each individual is attracted by the best solution found so far in the entire population, which may lead to premature convergence. Motivated by this observation, a new cuckoo search with neighborhood attraction (NACS) is proposed. In NACS, the neighborhood attraction scheme based on ring topology is firstly designed, where the best solution in a predefined neighborhood is employed to guide individual evolution. Then, to further enhance the exploration ability, the neighborhood attraction scheme and Levy flight are combined to generate potential candidate solutions. Moreover, the step size is adaptively regulated according to the degree of individual evolution. To validate the effectiveness of the presented algorithm, 25 extensively used benchmark test problems with different dimensions are employed. Experimental results reveal that the presented method is a competitive optimizer compared with other algorithms.
Summary Cloze: A New Task for Content Selection in Topic-Focused Summarization

A key challenge in topic-focused summarization is determining what information should be included in the summary, a problem known as content selection.In this work, we propose a new method for studying content selection in topic-focused summarization called the summary cloze task.The goal of the summary cloze task is to generate the next sentence of a summary conditioned on the beginning of the summary, a topic, and a reference document(s).The main challenge is deciding what information in the references is relevant to the topic and partial summary and should be included in the summary.Although the cloze task does not address all aspects of the traditional summarization problem, the more narrow scope of the task allows us to collect a large-scale datset of nearly 500k summary cloze instances from Wikipedia.We report experimental results on this new dataset using various extractive models and a two-step abstractive model that first extractively selects a small number of sentences and then abstractively summarizes them.Our results show that the topic and partial summary help the models identify relevant content, but the task remains a significant challenge.
A Systematic Comparison of English Noun Compound Representations

Building meaningful representations of noun compounds is not trivial since many of them scarcely appear in the corpus.To that end, composition functions approximate the distributional representation of a noun compound by combining its constituent distributional vectors.In the more general case, phrase embeddings have been trained by minimizing the distance between the vectors representing paraphrases.We compare various types of noun compound representations, including distributional, compositional, and paraphrasebased representations, through a series of tasks and analyses, and with an extensive number of underlying word embeddings.We find that indeed, in most cases, composition functions produce higher quality representations than distributional ones, and they improve with computational power.No single function performs best in all scenarios, suggesting that a joint training objective may produce improved representations.
On Overcoming the Identified Limitations of a Usable PIN Entry Method

In the domain of password security, research has made significant progress in handling different kinds of threats which require human intelligence factor to fix the vulnerabilities. In spite of having strong theoretical establishments, most of these defense mechanisms cannot be used in practice as humans have limitations in processing complex information. The little bit of good news is that very few research proposals in this field have shown the promises to be deployable in practice. This paper focuses on such one method - proposed by Roth <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> back in 2004, which provides adequate user-friendliness to enter Personal Identification Number (PIN) securely in the presence of human shoulder surfers. Surprisingly, the background algorithm of this method for validating users’ responses runs in linear time on a search space of cardinality 5 and hence, the validation process does not put much load on the authenticating device. Therefore, such human identification protocol can also be integrated into the IoT infrastructure for conducting a more secured login from the client-side. Having such advantages, though remained secure for almost ten years after its release in 2004, recently, few proposals revealed some serious vulnerable aspects of the Roth <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s proposal. In this paper, we have taken an attempt to save this user-friendly form of authentication. Firstly, we have made a critical discussion on the importance of the targeted PIN entry method in the domain of usable security and then given a brief overview of the identified limitations of this protocol. Followed by this, a few initiatives have been taken to fix the identified vulnerabilities of Roth <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s proposal by revising its working principle, while the login procedure and the usability standard of this method stay unaffected.
A privacy‐preserving multifactor authentication system

In Acar et al1, the surname of the third author was misspelled. The correct author name should be. Raheem Beyah. The online version has been corrected.
End-to-End Visual Domain Adaptation Network for Cross-Domain 3D CPS Data Retrieval

3D CPS (Cyber Physical System) data has been widely generated and utilized for multiple applications, e.g. autonomous driving, unmanned aerial vehicle and so on. For large-scale 3D CPS data analysis, 3D object retrieval plays a significant role for urban perception. In this paper, we propose an end-to-end domain adaptation framework for cross-domain 3D objects retrieval (C3DOR-Net), which learns a joint embedding space for 3D objects from different domains in an end-to-end manner. Specifically, we focus on the unsupervised case when 3D objects in the target domain are unlabeled. To better encode a 3D object, the proposed method learns multi-view visual features in a data-driven manner for 3D object representation. Then, the domain adaptation strategy is implemented to benefit both domain alignment and final classification. Specifically, an center-based discriminative feature learning method enables the domain invariant features with better intra-class compactness and inter-class separability. C3DOR-Net can achieve remarkable retrieval performances by maximizing the inter-class divergence and minimizing the intra-class divergence. We evaluate our method on two cross-domain protocols: 1) CAD-to-CAD object retrieval on two popular 3D datasets (NTU and PSB) in three designed cross-domain scenarios; 2) SHREC’19 monocular image based 3D object retrieval. Experimental results demonstrate that our method can significantly boost the cross-domain retrieval performances.
An Algorithm for Natural Images Text Recognition Using Four Direction Features

Irregular text has widespread applications in multiple areas. Different from regular text, irregular text is difficult to recognize because of its various shapes and distorted patterns. In this paper, we develop a multidirectional convolutional neural network (MCN) to extract four direction features to fully describe the textual information. Meanwhile, the character placement possibility is extracted as the weight of the four direction features. Based on these works, we propose the encoder to fuse the four direction features for the generation of feature code to predict the character sequence. The whole network is end-to-end trainable due to using images and word-level labels. The experiments on standard benchmarks, including the IIIT-5K, SVT, CUTE80, and ICDAR datasets, demonstrate the superiority of the proposed method on both regular and irregular datasets. The developed method shows an increase of 1.2% in the CUTE80 dataset and 1.5% in the SVT dataset, and has fewer parameters than most existing methods.
Incorporating Label Dependencies in Multilabel Stance Detection

Stance detection in social media is a wellstudied task in a variety of domains.Nevertheless, previous work has mostly focused on multiclass versions of the problem, where the labels are mutually exclusive, and typically positive, negative or neutral.In this paper, we address versions of the task in which an utterance can have multiple labels, thus corresponding to multilabel classification.We propose a method that explicitly incorporates label dependencies in the training objective and compare it against a variety of baselines, as well as a reduction of multilabel to multiclass learning.In experiments with three datasets, we find that our proposed method improves upon all baselines on two out of three datasets.We also show that the reduction of multilabel to multiclass classification can be very competitive, especially in cases where the output consists of a small number of labels and one can enumerate over all label combinations.