Reviewerly/exHarmony huggingface dataset:
DECADE - Deep Learning Based Content-hiding Application Detection System for Android

With the increasing demand for digital privacy, content-hiding (or vault) apps are becoming popular among mobile phone users. Content-hiding apps affiliate to decoy apps. They are used for hiding photos, text, or videos and appear to have an interface very similar to commonly-used utility/productivity/gaming applications (for example, a calculator user interface). While these kinds of applications are convenient for people and let them hide private data, it raises concerns among app security researchers about their presence in legit and illicit app markets. It can also set a barrier for digital investigators, practitioners, victim service agencies, and the intelligence community since these apps are known to encrypt/delete data and make it unrecoverable. Such data could be anything ranging from contraband to classified data. Our research focuses on developing a fully automated Android Vault app Identification and Extraction system, primarily from the Google Play store. Through the feature extractions from description and images of applications followed by various machine learning and deep learning models, the system successfully identifies the content-hiding applications. The system can also automatically extract the user data from vault applications running on Android phones. To facilitate the advancement of research, we also keep an inventory of vault apps found in the Google Play store and offer to trace such apps even if they get removed from the Google Play store for security/other reasons. Our methodology and findings can be further extended to detect and classify content-hiding and anti-forensic apps in any Android app market and not limited to the Google Play store.
A Comprehensive Survey of Sentiment Analysis Based on User Opinion

In this modern era online shopping is getting a lot of attention. Thousands of reviews are available from the customers on different social media platforms which makes it difficult for the user to make a purchasing decision. For a better understanding of user opinion, sentiment analysis (also known as opinion mining) has been conducted which makes a major effect on the purchasing decision of the user. Opinion mining is defined in terms of entities, emotions, and textual relationships. User opinions on e-commerce websites or social media apps have a huge impact on product stakeholders. Over the past decades, researchers, the public sector, and the service industry are carrying out opinion mining, to eradicate and examine community sentiments and opinions. This paper presents a survey of recent studies conducted for sentiment analysis based on user opinion through machine learning techniques (focusing on supervised, semi-supervised, reinforcement, and unsupervised learning), deep learning techniques (focusing on CNN, RNN, and LSTM), and provide the background knowledge.
Abnormal Telephone Recognition Based on Ensemble Learning

Abnormal calls have the characteristics of multiple occurrence and concealment. It has become the focus of the industry to identify abnormal calls by using machine learning. However, single machine learning model has the characteristics of poor generalization and high probability of model selection by mistake. In order to improve the performance of traditional single machine learning model, this paper proposes an abnormal telephone recognition method based on Stacking integration strategy under multi-model fusion. Random Forest, Decision Tree, XGBoost and Bayes are selected as the primary learners of Stacking integration, and logical regression is used as the secondary learners to build up a two-layer classifier learning model. The empirical analysis on the CDR call data set provided by a operator. The experimental results show that the two-layer classification model based on Stacking integration had better predictive effect than the traditional single learning method.
Hybrid clustering and classification methods to find out the pattern of the spread of covid-19 in East Java province

Abstract Covid-19 entered Indonesia in March 2020, which had an extraordinary impact on all aspects of life. The real impact is the economic and social sectors. Various efforts and policies from the Indonesian government were made to suppress the increase in the rate of Covid-19 cases. The spread of Covid-19 cases in Indonesia has been evenly distributed in various provinces, one of which is East Java. East Java has 38 districts/cities, where the case of each district is different, there are green, yellow, red, and black zones. To find out the pattern of spread of each district/city zone, clustering is used. The method used in this study was to use the hybrid method of clustering with K-means and classification with its algorithm Decision Tree. The purpose of this study to prepare mitigation measures to inhibit the rate of spread of Covid-19 in East Java Province. The variables used include the number of positive, the number of dead, the number of recovered, the number of suspects, and the number of probable. With this grouped data, it is expected to help make the right decision in reducing the spread and minimizing the number of positive patients. Based on the results of the study, there will be cluster results, namely 7 very high-risk areas (C0), 1 high risk district (C1), 29 moderate risk districts (C2), and 1 low-risk district (C3). It also has a validity index level of 0.356 as measured by the Davies-Bouldin Index.
K-nearest Multi-agent Deep Reinforcement Learning for Collaborative Tasks with a Variable Number of Agents

Traditionally, the performance of multi-agent deep reinforcement learning algorithms are demonstrated and validated in gaming environments where we often have a fixed number of agents. In many industrial applications, the number of available agents can change at any given day and even when the number of agents is known ahead of time, it is common for an agent to break during the operation and become unavailable for a period of time. In this paper, we propose a new deep reinforcement learning algorithm for multi-agent collaborative tasks with a variable number of agents. We demonstrate the application of our algorithm using a fleet management simulator developed by Hitachi to generate realistic scenarios in a production site.
PUOKMS: Password-Protected Updatable Oblivious Key Management System for Cloud Storage

Updatable oblivious key management system (UOKMS) has been widely applied in reality to protect outsourced data confidentiality. We demonstrate that existing UOKMS fails to prevent users’ private information from being leaked. We show that an adversary can impersonate any user to access her/his sensitive data in existing UOKMS, and this problem is further exacerbated by the collusion between two entities (i.e., a key server and a cloud server). In this paper, we propose a secure two-layered encryption mechanism to resist impersonation and collusion attacks. Specifically, the first layer public/secret key is generated by a user’s password via an oblivious way, where the user’s password is hardened by a set of dedicated identity servers to thwart password guessing attacks; besides, multiple key servers secretly share a user-specific server-side key for each user to assist the user in generating the second layer symmetric key. We also utilize a key renewal mechanism that periodically updates the secret on each key server to resist perpetual leakage of the secret. With these two mechanisms, we develop a password-protected updatable oblivious key management system for cloud storage, dubbed PUOKMS. We evaluate PUOKMS in terms of security and efficiency, which demonstrates that PUOKMS achieves a strong security guarantee with high efficiency.
Motion Feature Retrieval in Basketball Match Video Based on Multisource Motion Feature Fusion

Both the human body and its motion are three-dimensional information, while the traditional feature description method of two-person interaction based on RGB video has a low degree of discrimination due to the lack of depth information. According to the respective advantages and complementary characteristics of RGB video and depth video, a retrieval algorithm based on multisource motion feature fusion is proposed. Firstly, the algorithm uses the combination of spatiotemporal interest points and word bag model to represent the features of RGB video. Then, the directional gradient histogram is used to represent the feature of the depth video frame. The statistical features of key frames are introduced to represent the histogram features of depth video. Finally, the multifeature image fusion algorithm is used to fuse the two video features. The experimental results show that multisource feature fusion can greatly improve the retrieval accuracy of motion features.
Spectral analyses, geology and petrology of the Gulf of Suez rift-related Oligo-Miocene basalts at Abu Zenima area, west central Sinai, Egypt

The initial phase of the Gulf of Suez rifting in the late Oligocene was accompanied by the eruption of continental basaltic magmas, which continued to the early Miocene. Within the rift zone at Abu Zenima area, west-central Sinai, several km-scale Oligo-Miocene basaltic exposures in the form of dykes, sills and lava flows intruded/extruded the Phanerozoic sedimentary successions. These basaltic outcrops were successfully discriminated from their country rocks by remote sensing techniques, using the Landsat-8 dataset. The basalts are evolved tholeiitic, Ti-rich varieties and exhibit narrow compositional range with absence of any felsic counterparts, suggesting equilibrium crystallization process in their evolutions rather than fractional crystallization mechanism. Simulations of equilibrium crystallization process and evolution trajectories of these basalts have been modeled.
Deep Homography Estimation based on Attention Mechanism

Since the existing supervised learning has a strong dependence on the real ground labeling and ignores the importance of depth differences and moving objects in the image, an unsupervised homography estimation algorithm is proposed. Firstly, a resnet34 backbone network is constructed, and two feature extraction modules with shared weights are used. Then, each initial feature extraction module is embedded with an Shuffle attention mechanism (SA), which is used to extract features that can provide greater help for model training. Secondly, the triple loss function is used as the loss function of the neural network, so that the neural network can better learn the difference of the input image, and learn the alignment between the corrected image generated by the estimated homography matrix and the input image. Finally, the proposed algorithm is compared with the existing methods, and the results show that the proposed method has good adaptability and performance in low texture and low brightness images.
Fast and Secure Consortium Blockchains with Lightweight Block Verifiers

Due to advantages of moderate cost and easy deployment, consortium blockchains have been widely adopted in various commercial services and industrial applications. Nevertheless, because of limited number of miners, consortium blockchains are vulnerable to collusion attacks incurred by compromised block verifiers, i.e., miners verifying new blocks. To address this issue, edge devices acting as lightweight nodes can be recruited as lightweight block verifiers (LBVs) to verify the blocks together with typical miners. This increases the number of block verifiers, thereby enhancing blockchain security, but may result in a larger block verification delay. To tackle this dilemma, in this paper, we propose a computing resource management scheme to maximize the system utility related to users&#x2019; satisfaction with the blockchain services, which aims at jointly optimizing computing resources of miners and LBVs. We prove the existence and uniqueness of the optimal set of strategies of miners and LBVs, and then exploit Karush-Kuhn-Tucker (KKT) conditions to find these strategies. The numerical results demonstrate that, compared with existing schemes, the proposed scheme achieves secure block verification by involving LBVs with optimized block verification delay.
Assessing Simultaneous Action Selection and Complete Information in TAG with Sushi Go!

Digitalizing tabletop games for general game playing (GGP) AI research is a continuously growing field. Tabletop Games Framework (TAG) is a framework developed to simplify the process of implementing tabletop board games to digital form. Sushi Go! is a game that combines simultaneous action selection and complete information. This creates a unique combination of mechanics, which presents a new challenge for GGP agents. By implementing Sushi Go! into TAG, we can test different agent's performance using these mechanics and compare them to their existing performances in the other games of TAG. Results of this testing are presented, which display that the framework is capable of implementing Sushi Go! and that the agents perform with mixed results. Further developing heuristics for the agents should prove to increase their performance when faced with these types of games.
Certainty Factor for Online Game Addiction Scale Diagnosis — A Case Study of Informatics Student, University of Khairun

Online game addiction is the behavior of someone who wants to always play online games that lasts a long time and allows the individual concerned to not be able to control it so that it can have a negative impact that can have a negative impact on health and mental if too frequently play it. The aim of this study is to diagnose the online game addiction scale to students using the certainty factor algorithm. The level of online game addiction will be divided into three levels, namely: mild, moderate and severe addiction diagnoses. The calculation process starts from determining the weight value of each symptom and then determining the weight value of the user then calculating the weight value of each symptom with the user’s weight value using the certainty factor method so as to produce how large the percentage level of online game addiction. The results showed that from a number of processed data the diagnostic results were obtained 13% of students with mild addiction, 37% of students with moderate addiction, 50% of students with severe addiction.
Agile Software Quality Metrics

Agile development practices, based on the Agile Manifesto values and principles [1], originated in commercial Information Technology companies starting in 2001. The benefits of Agile development practices have been realized and have led to subsequent widespread adoption of Agile. Agile continues to be adopted in industries and projects that previously dominantly used document-driven Waterfall development practices (for example, Defense Department of (DoD) programs). Sound metrics are key underpinnings of successful software development, and they have matured steadily for traditional software development over the past 30 years. However, Agile quality metrics and practitioner guidance is less mature. This paper explores the current state of Agile development metrics and proposes both a framework for Agile Metrics selection and a foundational Agile Quality Metric “Core Set”.
A Multi-population BRKGA for the Automatic Clustering Problem

The clustering problem, or grouping, has two variants. If the number of clusters is predefined, this problem is known as the Clustering Problem (CP) or k-Clustering Problem, but when the number of clusters is not defined, the problem is known as the Automatic Clustering Problem (ACP). This paper proposes a new multi-population Biased Random-Key Genetic Algorithm (BRKGA) for the ACP, considering the silhouette index as similarity measure. In algorithm, several BRKGA populations evolve independently, such that each population is responsible for searching the best clustering for a given cluster number, i.e., each population solves one k-Clustering Problem for a particular k. Extensive experiments in 53 benchmark instances commonly used in the literature show that the algorithm obtained very competitive results compared to the state-of-the-art algorithms.
Unsupervised Topic Aware Document-Level Semantic Representation for Document Clustering

Text representation is critical for a lot of applications in natural/computational language processing and understanding. Textual documents are the basic unit of communication in written/typed form, in a lot of practical settings in a human work environment. Humans are very good at understanding the purpose of the document and comprehension of the semantics from the document. Document clustering is a specialized clustering in which documents written in human language are automatically partitioned into groups(clusters) of distinguishable sub collection. Document clustering process is very sensitive to document representation. This paper investigates the document representation models for succinctly representing document level semantics and employs a topic aware document representation approach. It suggests several desirable features for effective document representation (i) it should capture word-to-word relationship, (ii) it should derive from the basic words and their relation to form topic-units (a larger composition of words) and (iii) feature –weighting scheme based on frequent topic-units and document correlation. The task of document clustering is used to evaluate representation schemes. Standard text mining datasets are used and clustering is evaluated on internal and external clustering evaluation measures.
Chinese Named Entity Recognition for a Power Customer Service Intelligent Q&amp;A System

Power customer service intelligent Q&A system can greatly improve the efficiency of power customer service and reduce labor costs. In order to deal with the questions that need to be solved by reasoning, it is necessary to build the power customer service knowledge graph and accurately understand the questions. One of the key tasks is to implement a named entity recognizer using the historical log data of power customer service Q&A. Recently, lattice based neural networks have gained great advantages in Chinese named entity recognition. However, lattice based models rely heavily on an external predetermined dictionary, and the quality of the dictionary may interfere with entity boundary learning. As the power customer service Q&A is a form of oral conversation., it is difficult to build the specialized dictionary, which seriously restricts the application of the original menthod of lattice structure based neural network for Chinese named entity recognition in the field of power customer service. Therefore, this paper proposes a method of using entity boundary locally in lattice based neural networks for Chinese named entity recognition. Through joint learning of entity boundary and entity recognition, without any external dictionary, experiments on data sets in the field of power customer service show that this method has very good potential.
Conflict Resolution for Data Updates by Multiple Bidirectional Transformations

Bidirectional transformation (BX) is a robust mechanism to propagate changes of data across the transformation while maintaining consistency between two or more data sources. Recently, systems that coordinate multiple BX have been proposed. However, conflicts when multiple BX update the same source have not been well studied yet. In this paper, we propose a new conflict resolution method for BX based on an algorithm of Operational Transformation (OT). We apply the algorithm of OT to the backward transformation of the tree duplication primitive $$\mathrm {Dup}$$ of an existing BX language X proposed by Hu et al. X had been shown, by embedding into Inv, which is capable of maintaining structured documents such as XML through BX, to allow more flexible operations by Inv’s bidirectionality-satisfying operations. Using this mechanism, we propose a new backward transformation function $$ mput $$ that can resolve conflicts between updates on two views. Our implementation of $$ mput $$ can simultaneously satisfy the properties GETPUTGET and PUTGETPUT, which are the round-tripping properties inherited from X, and TP1, which is a confluence property inherited from OT for server-client environment.
Replacing Data Augmentation with Rotation-Equivariant CNNs in Image-Based Classification of Oral Cancer

We present how replacing convolutional neural networks with a rotation-equivariant counterpart can be used to reduce the amount of training images needed for classification of whether a cell is cancerous or not. Our hypothesis is that data augmentation schemes by rotation can be replaced, thereby increasing weight sharing and reducing overfitting. The dataset at hand consists of single cell images. We have balanced a subset of almost 9.000 images from healthy patients and patients diagnosed with cancer. Results show that classification accuracy is improved and overfitting reduced if compared to an ordinary convolutional neural network. The results are encouraging and thereby an advancing step towards making screening of patients widely used for the application of oral cancer.
Adversarial Attacks Medical Diagnosis Model with Generative Adversarial Networks

The emergence of adversarial samples poses a huge threat to the medical diagnosis system. Once the medical model is attacked, the accompanying medical accidents and economic losses are immeasurable. A GAN-based medical adversarial attack method called AmdGAN is proposed. AmdGAN can perform black-box distilling model attacks on the constructed high-precision medical diagnosis model. Using distilling knowledge in adversarial attacks, the transmission of adversarial information can be enhanced. Effective adversarial attacks can be carried out on small sample and large sample medical datasets, so that convolution kernel receptive fields of different scales can extract more refined feature map information. AmdGAN can improve the adversarial attack success rate of medical diagnosis models. The generated adversarial samples are also closer to real samples. As a result, the network effect of GAN is better. AmdGAN solves the problem that it is difficult to obtain the medical diagnosis model’s network structure and parameter information during adversarial attacks. The adversarial attack success rate is also better than that of the traditional attack method.
Putting Table Cartograms into Practice

Given an \(m\times n\) table T of positive weights, and a rectangle R with an area equal to the sum of the weights, a table cartogram computes a partition of R into \(m\times n\) convex quadrilateral faces such that each face has the same adjacencies as its corresponding cell in T, and has an area equal to the cell’s weight. In this paper, we examine constraint optimization-based and physics-inspired cartographic transformation approaches to produce cartograms for large tables with thousands of cells. We show that large table cartograms may provide diagrammatic representations in various real-life scenarios, e.g., for analyzing correlations between geospatial variables and creating visual effects in images. Our experiments with real-life datasets provide insights into how one approach may outperform the other in various application contexts.