Reviewerly/exHarmony huggingface dataset:
Method for Detecting Android Malware Based on Ensemble Learning

In recent years, we have become increasingly dependent on smart devices. Android is an operating system mainly used on mobile devices, where hundreds of millions of users can download various apps through many application stores. Under these circumstances, a large number of malicious apps can be put into the application stores by developers to achieve the purpose of attacking, controlling user devices, and even stealing user information and property. Therefore, it is necessary to identify malwares in mass apps through analysis and detection to remind users. We propose an idea of detecting and discriminating Android malware based on an ensemble learning method. Firstly, a static analysis of AndroidManifest file in APK is performed to extract features such as permission calls, component calls, and intents in system. Then we use XGBoost method, an implementation of ensemble learning, to detect malicious applications. The conclusion is that this system performs very well in Android malware detection.
PROTECTOR: Towards the protection of sensitive data in Europe and the US

The evolution of mobile multimedia computing and artificial intelligence represents a significant challenge in terms of real-time data processing and data protection management. It can be seen in the EU and US, where governments are adapting their regulation laws to meet current and future societal requirements. However, existing solutions provide limited capabilities and operate in isolation, focusing on protecting particular rights of citizens. The main contribution of the article at hand is a novel platform, called PROTECTOR, that protects sensitive data of EU and US citizens according to the rights provided by current EU/US data protection regulation laws. Three case studies prove how PROTECTOR deals with several citizen’s rights in two different legislation environments, such as the EU GDPR and the US. Finally, a pool of experiments with a different number of citizens, policies, and data have demonstrated the feasibility of the proposed solution.
Evolving hypernetworks for game-playing agents

This work investigates the evolution of indirectly-encoded neural networks through a hypernetwork approach. We find that for some Atari games, a hypernetwork with over 14 times fewer parameters, can compete or even outperform directly-encoded policy networks. While hypernetworks perform worse than directly encoded networks in the game Frostbite, in the game Gravitar, the approach reaches a higher score than any other evolutionary method and outperforms complicated deep reinforcement learning setups such as Rainbow.
A multi-scale fuzzy entropy measure for anomaly detection in activities of daily living

Anomaly detection in the Activities of Daily Living (ADL) of older adults is essential for healthcare management, to act to avoid prospective problems early and improve this group's quality of life. Once ADLs are recognised, the gathered information will be utilised to detect anomalies in comparison with routine activities. Existing methods provide some limited reliabilities in detecting the anomalous events in ADLs, particularly due to ignoring the changes in individuals' routine. Therefore, it is important to develop an appropriate method or algorithm that can efficiently detect anomalies in older adults' daily activities. The focus of this study is to distinguish and detect anomalies in ADLs based on data gathered from ambient sensors. In this paper, a novel method based on a Multi-scale Fuzzy Entropy measure to discriminate between normal and anomalous cases in ADLs with a high degree of accuracy is investigated. Experimental evaluation is conducted to detect anomalies in ADL data obtained from two different datasets (ADL and CASAS). The experimental results show that the proposed method can detect anomalous instances with a high degree of accuracy. Comparisons with other methods have also offered support to the proposed method.
Graph Publishing with Local Differential Privacy for Hierarchical Social Networks

With the spread of social network services, social media applications have obtained a significant amount of personal data and relational information from their users. In the era of big data, data should to be shared to make maximum use of its potential value. Therefore, the problem of personal privacy protection has become increasingly important. At present, differential privacy is a strictly proven privacy protection model, which has garnered people’s attention and research in many fields. However, the existing social network differential privacy publishing technology mainly focuses on the centralized model, that is, the data collector is assumed to be credible. The hierarchical random graph model, which satisfies differential privacy to the local model, was applied in our study. We improved efficiency and accuracy using the Monte Carlo Markov chain. The experiment showed that the local differential privacy had better utility than the centralized differential privacy under the same differential privacy conditions.
Vietnamese Word Segmentation with SVM: Ambiguity Reduction and Suffix Capture

In this paper, we approach Vietnamese word segmentation as a binary classification by using the Support Vector Machine classifier. We inherit features from prior works such as n-gram of syllables, n-gram of syllable types, and checking conjunction of adjacent syllables in the dictionary. We propose two novel ways to feature extraction, one to reduce the overlap ambiguity and the other to increase the ability to predict unknown words containing suffixes. Different from UETsegmenter and RDRsegmenter, two state-of-the-art Vietnamese word segmentation methods, we do not employ the longest matching algorithm as an initial processing step or any post-processing technique. According to experimental results on benchmark Vietnamese datasets, our proposed method obtained a better $$\text {F}_{1}\text {-score}$$ than the prior state-of-the-art methods UETsegmenter, and RDRsegmenter.
Streaming Analytics and Workflow Automation for DFS

Researchers reuse data from past studies to avoid costly re-collection of experimental data. However, large-scale data reuse is challenging due to lack of consensus on metadata representations among research groups and disciplines. Dataset File System (DFS) is a semi-structured data description format that promotes such consensus by standardizing the semantics of data description, storage, and retrieval. In this paper, we present analytic-streams - a specification for streaming data analytics with DFS, and streaming-hub - a visual programming toolkit built on DFS to simplify data analysis workflows. Analytic-streams facilitate higher-order data analysis with less computational overhead, while streaming-hub enables storage, retrieval, manipulation, and visualization of data and analytics. We discuss how they simplify data pre-processing, aggregation, and visualization, and their implications on data analysis workflows.
An Introduction to Zero-Shot Learning: An Essential Review

With deep learning achieving more successful results than traditional machine learning methods, researches in the field of computer vision have evolved towards this area. However, in order to obtain successful models in deep learning methods, it needs a large number of training samples similar to traditional machine learning methods. In order to meet this requirement, auxiliary information of visual data has been used in recent years. Zero-shot learning methods focused on the compatibility functions of image embeddings and class embeddings, and researches aimed at better representation of class embeddings on visual data. In this paper, recent studies on zero-shot learning have been examined and evaluated.
Improving the phase sensitivity of an SU (1, 1) interferometer via a nonlinear phase encoding

We propose a scheme that a dynamically evolving atom-molecule condensate maps onto an SU(1, 1) interferometer, which includes a nonlinear medium (nonlinear phase encoding). We give an analytical result of the phase uncertainty from the error propagation by measuring the particle number at the output and demonstrate the optimal quantum metrology, which is obtained with a time-reversal protocol. We show the phase sensitivity of the nonlinear medium with time-reversal protocol scales as 1/(2.1N2), which overcome the conventional sensitivity limit of 1/N. Finally, the effect of the poor particle resolution detection on the phase sensitivity is discussed.
Obstacle detection in a field environment based on a convolutional neural network security

Information security has become an important subject in the artificial intelligence filed to handle big data. Most of the systems aim at obstacle detection on ordinary roads. In this paper, we proposed a method for detecting obstacles in a field environment based on convolutional neural network (CNN). Firstly, we propose a region of interest (ROI) extraction algorithm to deal with the suspected obstacle area. Secondly, we design a CNN model to classify the extracted feature maps of candidate areas. The experimental results indicate that the proposed method has high recognition accuracy and can detect obstacles effectively.Abbreviations: CNN: Convolutional Neural Network; ROI: Region of Interest; DBM: Deep Boltzmann Machines; AE: Auto-encoders; RPN: Region Proposal Network; ReLU: Rectified Linear Unit; RCNN: Regions with CNN Features; VGG-Net: Visual Geometry Group Net
Lattice-Based Cryptanalysis of RSA with Implicitly Related Keys

We address the security issue of RSA with implicitly related keys in this paper. Informally, we investigate under what condition is it possible to efficiently factorize RSA moduli in polynomial time given implicit relation of the related private keys that certain portions of bit pattern are the same. We formulate concrete attack scenarios and propose lattice-based cryptanalysis by using lattice reduction algorithms. A subtle lattice technique is adapted to represent an unknown private key with the help of known implicit relation. We analyze a simple case when given two RSA instances with the known amount of shared most significant bits (MSBs) and least significant bits (LSBs) of the private keys. We further extend to a generic lattice-based attack for given more RSA instances with implicitly related keys. Our theoretical results indicate that RSA with implicitly related keys is more insecure and better asymptotic results can be achieved as the number of RSA instances increases. Furthermore, we conduct numerical experiments to verify the validity of the proposed attacks.
The ACL FWS-RC: A Dataset for Recognition and Classification of Sentence about Future Works

Sentences about future work (FWS) mentioned in the academic papers are very important, which contain valuable information and can provide researchers with new research topics or directions. At present, researchers' analysis of academic papers mainly focuses on the content of citations, bibliographic information, etc., and little attention is paid to the FWS contained in the full text. This paper constructs a corpus of the FWS based on the full text content of academic papers, and analyzes characteristics and the rules of FWS. Taking 4,024 conference papers in Natural Language Processing (NLP) as the research object, three basic annotation specifications are formulated, and 3,067 sentences about future work are extracted from 4,509 chapters by manual annotation. Then, all the FWS are manually coded, and the sentences are classified into 6 main categories, which are method, resources, evaluation, application, problem and other, and 17 sub-categories. Finally, we analyze the future work in different types. The results show that sentences mentioning methods account for the highest proportion. There is little difference in the number of the FWS in other categories. To the best of our knowledge, this is the first attempt at constructing the corpus of FWS, which will provide the basis for the automatic extraction and classification of the FWS and facilitate researchers to conduct large-scale research on future work in academic papers. Our future works include extending scale of the FWS corpus and automatic extraction of the FWS.
Explore the Improvement of the Management of China's International Film Festivals Based on Artificial Intelligence

In recent years, with the continuous development of mobile Internet and big data technology, artificial intelligence has become the core driving force of a new round of industrial transformation. With the advent of the era of 5G, its characteristics of high speed, low delay and high reliability make the integration of terminal and cloud closer. AI technology will realize more complex applications and integrate into all walks of life.Based on the application status of artificial intelligence technology in the management field of China's International Film Festivals, this paper analyzes and assumes the three management stages which are the early, middle and last stages of the China's International Film Festivals.To explore the inevitability and possibility of the application of artificial intelligence technology of the win-win development in this field.
Materialising contexts: virtual soundscapes for real-world exploration

This article presents the results of a study based on a group of participants' interactions with an experimental sound installation at the National Science and Media Museum in Bradford, UK. The installation used audio augmented reality to attach virtual sound sources to a vintage radio receiver from the museum's collection, with a view to understanding the potentials of this technology for promoting exploration and engagement within museums and galleries. We employ a practice-based design ethnography, including a thematic analysis of our participants' interactions with spatialised interactive audio, and present an identified sequence of interactional phases. We discuss how audio augmented artefacts can communicate and engage visitors beyond their traditional confines of line-of-sight, and how visitors can be drawn to engage further, beyond the realm of their original encounter. Finally, we provide evidence of how contextualised and embodied interactions, along with authentic audio reproduction, evoked personal memories associated with our museum artefact, and how this can promote interest in the acquisition of declarative knowledge. Additionally, through the adoption of a functional and theoretical aura-based model, we present ways in which this could be achieved, and, overall, we demonstrate a material object's potential role as an interface for engaging users with, and contextualising, immaterial digital audio archival content.
Word-Spotting approach using transfer deep learning of a CNN network

Convolutional Neural Networks (CNNs) are deep learning models that are trained to automatically extract the most discriminating features directly from an input image to be used for visual classification tasks. Recently, CNNs attracted a lot of interest thanks to their effectiveness in many computer vision applications (medical imaging, video surveillance, biometrics, pattern recognition, OCR, etc.). Transfer learning is an optimization method that uses a pretrained network to speed up the training of another related task or application. This helps speed up and improve the training process on a new dataset. In this paper, we propose a new approach of handwritten word retrieval based on deep learning and transfer learning. We compared the performance between two types of extracted features based on transfer learning: from a pre-trained model and a fine-tuned network. Experiments are performed using six different CNN architectures and three similarity measures on the presegmented Bentham dataset of the ICDAR competition. The obtained results demonstrate the effectiveness of our proposed approach compared to existing methods, evaluated in this competition.
Beyond the ‘Silk Road’: Assessing Illicit Drug Marketplaces on the Public Web

Criminals take advantage of internet communications to amplify the impact of their actions and to form international criminal networks. At the same time, vast amounts of information generated by their online activities have become available for analysis. Open source web intelligence is a valuable methodology for understanding and responding to these new global criminal phenomena. Collecting data from websites, social media platforms and online discussion forums enables researchers, investigators and policy-makers to study and to develop appropriate responses to emerging threats. Automated web intelligence tools such as web crawlers can be used to extract relevant information from target websites and to map the threat landscape of criminogenic environments online. For the study presented in this chapter, we used our web-crawling software to download contents of 28 Russian online marketplaces for illicit drugs. Drug names, types, prices, quantities and geographical locations of sales were extracted and mapped to identify drug trafficking hotspots. Findings indicate such marketplaces can operate due to the ability of their clients to pay anonymously with virtual currencies (specifically Bitcoin and Qiwi) and to deliver the drugs through non-contact methods. This type of service is available in all large cities within Russia and provides to the seller with a safer and more anonymous alternative to “street-level” purchases. The method described in this study can be used to investigate and to prioritize online threats according to their location and severity.
Characteristics of Malay translated hadith corpus

Annotated corpus can greatly assist in the natural language processing field. For example, computers can understand more of the document context, and indexing and clustering in information retrieval can be done precisely with less or no ambiguity of words. However, there are only a few annotated corpora in Malay language, which are not publicly shared. In this paper, we delve into analysing and annotating Malay translated hadith documents in terms of tagging and entities. There are three phases, which are manual filtering and cleaning, analysing the corpus and creating the benchmark. As the result, an analysis and benchmark of Malay translated hadith corpus were produced in term of part-of-speech and named entities tags that follows the Zipf's law distribution.
When Is a Test Not a Proof?

A common primitive in election and auction protocols is a plaintext equivalence test (PET) in which two ciphertexts are tested for equality of their plaintexts, and a verifiable proof of the test's outcome is provided. The most commonly-cited PETs require at least one honest party, but many applications claim universal verifiability, at odds with this requirement. If a test that relies on at least one honest participant is mistakenly used in a place where a universally verifiable proof is needed, then a collusion by all participants can insert a forged proof of equality into the tallying transcript. We show this breaks universal verifiability for the JCJ/Civitas scheme among others, because the only PETs they reference are not universally verifiable. We then demonstrate how to fix the problem.
Comparing indirect encodings by evolutionary attractor analysis in the trait space of modular robots

In evolutionary robotics, the representation of the robot is of primary importance. Often indirect encodings are used, whereby a complex developmental process grows a body and a brain from a genotype. In this work, we aim at improving the interpretability of robot morphologies and behaviours resulting from indirect encoding. We develop and use a methodology that focuses on the analysis of evolutionary attractors, represented in what we call the trait space: Using trait descriptors defined in the literature, we define morphological and behavioural Cartesian planes where we project the phenotype of the final population. In our experiments we show that, using this analysis method, we are able to better discern the effect of encodings that differ only in minor details.
Fast Monocular Depth Estimation on an FPGA

Depth sensing is crucial for understanding 3D scenes on embedded systems such as home robots, self-driving cars, and drones. Monocular depth estimation which gives pixel-wise depth from a general camera, has attracted attention in recent years, due to the reliability, low-cost and small area requirement. Past research by using Convolutional Neural Network (CNN) has gained high accuracy and been increasing interest. However, the CNN requires a massive amount of MACs (Multiply ACcumulations) and weights, so its latency is extremely long. To address this problem, we present hardware-oriented pruning for separable convolutions and effectively parallelized MAC Unit. We introduce a filter-wise pruned DepthFCN and novel FPGA architecture that exploit its sparsity. Moreover, dense convolution and pruned separable convolution are implemented on a shared convolutional circuit due to high hardware efficiency and a high parallel degree. We compare the proposed FPGA-based system with the Jetson TX2. The FPGA accelerator achieves 123.6 FPS with 0.3 W power consumption for a 256×256 image, and its accuracy is 76.2%. Compared with the mobile GPU, it is 1.5 times faster and its power consumption is 20 times lower. We demonstrate the fastest monocular depth estimation by using a low-cost FPGA board that is suitable for embedded systems.
Developing Support Vector Machine with New Fuzzy Selection for the Infringement of a Patent Rights Problem

Classification problems are very important issues in real enterprises. In the patent infringement issue, accurate classification could help enterprises to understand court decisions to avoid patent infringement. However, the general classification method does not perform well in the patent infringement problem because there are too many complex variables. Therefore, this study attempts to develop a classification method, the support vector machine with new fuzzy selection (SVMFS), to judge the infringement of patent rights. The raw data are divided into training and testing sets. However, the data quality of the training set is not easy to evaluate. Effective data quality management requires a structural core that can support data operations. This study adopts new fuzzy selection based on membership values, which are generated from fuzzy c-means clustering, to select appropriate data to enhance the classification performance of the support vector machine (SVM). An empirical example based on the SVMFS shows that the proposed SVMFS can obtain a superior accuracy rate. Moreover, the new fuzzy selection also verifies that it can effectively select the training dataset.
An Interface between Grassmann manifolds and vector spaces

In this paper, we propose a method to map data from a Grassmann manifold to a vector space while maximizing discrimination capability for classification. Subspaces are a practical and robust representation for image set recognition. However, as they exist on a Grassmann manifold, machine learning tools constructed on Euclidean geometry cannot be promptly utilized. Recently, methods to construct end-to-end learnable models for subspaces are starting to be explored, but they require multiple matrix decompositions and can be hard to compute and extend. Therefore we introduce a layer to map Grassmann manifold-valued data to vector space, in such a way that it can be seamlessly used as a layer along with other powerful tools defined on Euclidean space. The key idea of our method is to formulate the manifold logarithmic map (log) as a learnable model, where we seek to learn a tangency point that minimizes a loss function with respect to the data. The log effectively transforms a manifold point into a tangent vector. This log model can be learned with Riemannian stochastic gradient descent on the target manifold. We demonstrate the effectiveness of our proposed method on the applications of hand shape recognition, face identification and facial emotion recognition.