Reviewerly/exHarmony huggingface dataset:
StyleCAPTCHA

CAPTCHAs are widely deployed for bot detection. Many CAPTCHAs are based on visual perception tasks such as text and objection classification. However, they are under serious threat from advanced visual perception technologies based on deep convolutional networks (DCNs). We propose a novel CAPTCHA, called StyleCAPTCHA, that asks a user to classify stylized human versus animal face images. StyleCAPTCHA creates each stylized image by combining the content representations of a human or animal face image and the style representations of a reference image. Both the original face image and the style reference image are hidden from the user. To defend against attacks using DCNs, the StyleCAPTCHA service changes the style regularly. To adapt to the new styles, the attacker has to repeatedly train or retrain her DCNs, but since the attacker has insufficient training examples, she cannot train her DCNs well. We also propose Classifier Cross-task Transferability to measure the transferability of a classifier from its original task to another task. This metric allows us to arrange the schedule of styles and to limit the transferability of attackers' DCNs across classification tasks using different styles. Our evaluation shows that StyleCAPTCHA defends against state-of-the-art face detectors and against general DCN classifiers effectively.
The MuMMER Data Set for Robot Perception in Multi-party HRI Scenarios

This paper presents the MuMMER data set, a data set for human-robot interaction scenarios that is available for research purposes <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . It comprises 1h 29 min of multimodal recordings of people interacting with the social robot Pepper in entertainment scenarios, such as quiz, chat, and route guidance. In the 33 clips (of 1 to 4 min long) recorded from the robot point of view, the participants are interacting with the robot in an unconstrained manner.The data set exhibits interesting features and difficulties, such as people leaving the field of view, robot moving (head rotation with embedded camera in the head), different illumination conditions. The data set contains color and depth videos from a Kinect v2, an Intel D435, and the video from Pepper.All the visual faces and the identities in the data set were manually annotated, making the identities consistent across time and clips. The goal of the data set is to evaluate perception algorithms in multi-party human/robot interaction, in particular the re-identification part when a track is lost, as this ability is crucial for keeping the dialog history. The data set can easily be extended with other types of annotations.We also present a benchmark on this data set that should serve as a baseline for future comparison. The baseline system, IHPER <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (Idiap Human Perception system) is available for research and is evaluated on the MuMMER data set. We show that an identity precision and recall of ~80% and a MOTA score above 80% are obtained.
Exploiting Class Labels to Boost Performance on Embedding-based Text Classification

Text classification is one of the most frequent tasks for processing textual data, facilitating among others research from large-scale datasets. Embeddings of different kinds have recently become the de facto standard as features used for text classification. These embeddings have the capacity to capture meanings of words inferred from occurrences in large external collections. While they are built out of external collections, they are unaware of the distributional characteristics of words in the classification dataset at hand, including most importantly the distribution of words across classes in training data. To make the most of these embeddings as features and to boost the performance of classifiers using them, we introduce a weighting scheme, Term Frequency-Category Ratio (TF-CR), which can weight high-frequency, category-exclusive words higher when computing word embeddings. Our experiments on eight datasets show the effectiveness of TF-CR, leading to improved performance scores over the well-known weighting schemes TF-IDF and KLD as well as over the absence of a weighting scheme in most cases.
An Overview of Blockchain Consensus and Vulnerability

Bitcoin, a secure and transparent peer-to-peer payment system, is the origin of blockchain technology. Blockchain has the capability to stores data records in a secure and immutable way without the centralized control. It achieves a goal through a novel decentralized consensus that provides a platform in trustless environment. Consensus mechanism plays an important role to maintain consistency and security in blockchain platform. In this paper, we have discussed the Byzantine Generals Problem and surveyed several popular consensus mechanisms in current blockchain networks. A comparison table of blockchain consensus is presented on the basis of some parameters. This table can be helpful for understanding consensus advantages and disadvantages and its usability in blockchain platform. Vulnerability like 51% in consensus is common in permissionless blockchain which is discussed, and also, some other failures are mentioned in this paper.
Defense Against Advanced Persistent Threats: Optimal Network Security Hardening Using Multi-stage Maze Network Game

Advanced Persistent Threat (APT) is a stealthy, continuous and sophisticated method of network attacks, which can cause serious privacy leakage and millions of dollars losses. In this paper, we introduce a new game-theoretic framework of the interaction between a defender who uses limited Security Resources(SRs) to harden network and an attacker who adopts a multi-stage plan to attack the network. The game model is derived from Stackelberg games called a Multi-stage Maze Network Game (M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NG) in which the characteristics of APT are fully considered. The possible plans of the attacker are compactly represented using attack graphs(AGs), but the compact representation of the attacker’s strategies presents a computational challenge and reaching the Nash Equilibrium(NE) is NP-hard. We present a method that first translates AGs into Markov Decision Process(MDP) and then achieves the optimal SRs allocation using the policy hill-climbing(PHC) algorithm. Finally, we present an empirical evaluation of the model and analyze the scalability and sensitivity of the algorithm. Simulation results exhibit that our proposed reinforcement learning-based SRs allocation is feasible and efficient.
Spatial Fuzzy Risk Mapping for Tuberculosis in Surabaya, Indonesia

Surabaya, one of the major cities in Indonesia, is an endemic area for spreading tuberculosis. Surabaya City Health Office in 2018 has found 7,007 cases of tuberculosis which is the highest case in East Java province. This data shows that TB is still a major health problem. TB risk mapping is needed to guide the Public Health Service in TB control planning, for example, the promotion of clean and healthy living behaviors, immunizations, and home visit programs and optimization of TB screening activities. This paper proposes the spatial risk mapping of tuberculosis based on several criteria that become tuberculosis risk factors using a fuzzy method called spatial fuzzy risk mapping. These criteria consist of the number of people with tuberculosis (BTA Positive), population density, unhealthy houses, and health facilities. Fuzzy multi-criteria decision making determines the weight value of each criterion, followed by the ranking process to select the best alternative from the sub-district areas. After fuzzy membership calculation, the sub-district areas area directly classified into 3 index level that is low, medium, and high according to the rule association. The determination of the TB disease risk index covers 31 sub-districts in Surabaya as densely populated urban areas. The risk map is visualized into spatial GIS mapping. In the last 3 years (2013-2015), there were 4 sub-districts are decreasing (12.9%), 6 sub-districts are increasing (19.4%) and the remaining 68.7% did not change. There are 13.33% sub-districts in 2015 that are defined as low risk by the fuzzy risk, but it must be high risk by the Public Health Service. The fuzzy risk index results appropriate with the real condition and it is suitable with the Public Health Service report.
Hardware Implementation and Comparison of CRA and TRA when Trying to Recover the AES-128 Key

In this scientific paper, a comparison of two side-channel radio attacks is made: CRA - Correlation Radio Attack and TRA - Template Radio Attack. Both attacks are tested on mixed-signal chips that include both the radio transceiver and the digital logic on the same integrated circuit. In systems like this, the analog transmitter unintentionally leaks sensitive data, related to the cryptographic hardware components and the software being run on the CPU, by broadcasting it on a shifted frequency. The basic conception is that information from the electromagnetic leakage is gathered and then exploited by analyzing it via the side-channel attacks in order to crack the AES-128 algorithm. The system needed for the experiment consists of: a PCA10040 chip by Nordic Semiconductor, an USRP n210 by CP-Ettus Research and a Linux-based computer.
Improving Skew Detection and Correction in Different Document Images Using a Deep Learning Approach

During the optical scanning of a document using any of the Optical Character Recognition (OCR) devices, it is difficult to ensure that the scanned document will not get skewed. The high-speed scanning of the skewed document affects the text recognition process. Therefore, it is essential to de-skew the document images to improve the accuracy of the text recognition task. This paper presents a deep learning approach to detect the angle of the skewed scanned document written in different languages. Given the skewed images, we first predict the skewed angle of the document, and later we correct the document images by the angle predicted in reverse order. The angle prediction task is considered to be a classification problem with 360 classes. Each predicted entry in the vector represents the probability of the respective classes with each of value lying between 0 and 1.
Multispectral image denoising methods: A literature review

The Multispectral images appear to give a more true representation of real-world scenes to improve the efficiency of many tasks involving computer vision and object extraction, identification and quantification, tagging operations and imagery segmentation, compared to standard RGB or grayscale images. The use of visible multi-spectral spectrum images as opposed to standard RGB systems enables high colour reproduction characteristics due to the achievement of minimal data from RGB images. Due to the restriction of facilities, restricted bandwidth and radiant energy losses, MSIs are impaired during capture by many noises. The formulation of a new mathematical definition of the deep study model for denoising is a complex investigation problem and several researchers have defined various MSI denoising algorithms or methods. Many researchers suggested its use as a sparse coding of noisy patches in the application of the neural network. These additionally allow different methods to change themselves with an algorithm for a mission. In reality, however, multiple noises often come across a multi-spectral image. We presented in this study the previous techniques for the MSI influenced by noise. The literature survey explains the description and advantages of previous approaches relative to each other.
Response of quantum Fisher information, variance entropy squeezing and entanglement to the intrinsic decoherence of two non-degenerate fields interacting with two qubits

We investigate the link between quantum Fisher information (QFI) and quantum entanglement for a scheme of two atoms that interact with two non-degenerate modes in the absence and presence of intrinsic decoherence effect. The explicit form of the density matrix approach to the dissipative systems is given when the cavity-field is in generalized coherent states. The dynamical behavior of the QFI is compared with the atom–atom concurrence entanglement. We examine the effect of the dipole–dipole interaction and intrinsic decoherence on the dynamical properties of the QFI, concurrence and variance entropy squeezing. It is found that the atom–atom concurrence and the QFI can be controlled by the intrinsic decoherence and the dipole–dipole interaction.
Demographic Face Profiling Based on Age, Gender and Race

User profiling has lately got much interest and has been increasingly used in various fields of applications such as security, medicine, and commerce. The aim of this work is to predict a user demographic profile based on soft biometric modalities, namely the age, the gender and the race, for the authentication of suspicious people. We propose different types of characteristics based on global and local face features relative to the color, the texture and the shape. The retained characteristics are selected by the PSO algorithm. The classification phase is based on the SVM classifier optimized by a grid search to determine its best parameters. Validated on the public Morph II database and on our own database, the proposed approaches of users' demographic profile estimation yield interesting results.
Robust, Imperceptible and End-to-End Audio Steganography Based on CNN

Recently, deep learning-based steganography emerges, where the end-to-end steganography is a promising direction. However, most of the existing approaches are developed for the image which are not suitable for the audio. In this paper, we design a CNN-based end-to-end framework that consists of an encoder and a decoder. The encoder achieves encoding the secret message into the audio cover and the corresponding decoder is used to extract the message. Specifically, a derivative-based distortion function is adopted as the loss function of the encoder. Besides, instead of directly generating the stego audios, the encoder in our framework generates the modification vector of the audio sampling value. In this way, the distortion incurred by message embedding can be further reduced. The experiment results show that, compared with the existing approach based on generative adversarial network (GAN), even without an adversarial steganalytic network, stego audios perform relatively more imperceptible. In addition, considering some possible pollution of stego audios in the transmission, we further improve the robustness of our approach by introducing noise simulation layers into the framework.
Mining User Interests from Social Media

Social media users readily share their preferences, life events, sentiment and opinions, and implicitly signal their thoughts, feelings, and psychological behavior. This makes social media a viable source of information to accurately and effectively mine users' interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services and higher user satisfaction. In this tutorial, we cover five important aspects related to the effective mining of user interests: (1) the foundations of social user interest modeling, such as information sources, various types of representation models and temporal features, (2) techniques that have been adopted or proposed for mining user interests, (3) different evaluation methodologies and benchmark datasets, (4) different applications that have been taking advantage of user interest mining from social media platforms, and (5) existing challenges, open research questions and exciting opportunities for further work.
Knowledge Graphs: Research Directions

In these lecture notes, we provide an overview of some of the high-level research directions and open questions relating to knowledge graphs. We discuss six high-level concepts relating to knowledge graphs: data models, queries, ontologies, rules, embeddings and graph neural networks. While traditionally these concepts have been explored by different communities in the context of graphs, more recent works have begun to look at how they relate to one another, and how they can be unified. In fact, at a more foundational level, we can find some surprising relations between the different concepts. The research questions we explore mostly involve combinations of these concepts.
Securing IoT Devices Generated Data Using Homomorphic Encryption

Cloud computing is emerging trends. Cloud service provider provides services like network, application, storage, etc. People use cloud storage for storing and retrieving their data for future use. Internet IoT devices are memory and power constrained. All data generated by these devices are outsourced on cloud, but sharing private data to cloud is risky. Though it is in encrypted format, processing data on cloud is import task. Our research focuses on processing on encrypted data. GSW scheme is used to perform computation of an encrypted data, collect data from IoT devices, and encrypt it using GSW algorithm. Computation is performed on encrypted data like addition and multiplication on cloud server.
Integrating Machine Learning with Blockchain to Ensure Data Privacy

Nowadays data is collected without any specific purpose, every activity of a machine or a human being is recorded, If needed in the future then the data will be analyzed. But here the question of trust arises as the data will go through many phases for the analysis by different parties. The data may contain some sensitive or private information that can be miss utilized by the organizations involved in the analysis stages. So it is needed for the hour to consider the data privacy issues very seriously. Data privacy refers to how to control the use of a piece of data by its relative importance. For example, someone will not hesitate to say his/her name to strangers but will not share mobile no. address until the stranger becomes familiar. In this digital age, the personal use of data privacy generally applied to critical personal information. From a business point of view, data privacy operates in a broader sense beyond the critical information of employees & customers. There is a general conclusion that AI & Machine Learning driven technologies are obstructed by data privacy issues as the ML uses large data sets to train & test. But can the obstacle block convert to a stepping stone? Yes, It can be. Imagine a scenario where no one is trust-able, so in that case, Blockchain enters the scene. Blockchain uses the data but in an anonymity way. So in this paper, we are proposing a way, how to combine Machine Learning & Blockchain to ensure data privacy.
Educational Plans and Programs for Digital Competence: A Geographical Analysis From an Educational Equity Perspective

This article discusses the plans that contribute to the development of digital competence for students in Galicia (Spain). From 1100 public schools, 18 programs were selected. A concurrent triangulation mixed-methods design was applied. The content analysis of the proposals has been combined with impact statistics in schools and a Geographic Information System (GIS). The results show prioritized digital competence dimensions and a partial projection across the set of schools: 26% do not incorporate any programs, whilst 5% implement five or more of these initiatives. The findings question the principle of equity in education that prevails in the public education system.
An Automatic Facial Expression Recognition System Employing Convolutional Neural Network with Multi-strategy Gravitational Search Algorithm

Facial expression recognition (FER) plays a vital role in image processing according to the widespread development of human interactive applications. In the past few years, various researchers have focused on FER for implementing it in different applications. The existing system suffers from various complexities such as low accuracy, computational cost, and poor recognition performances. In this article, we proposed a novel concept to recognize facial expressions. The proposed work comprises three sections that are pre-processing, feature extraction, and classification. The pre-processing techniques remove the unwanted data from the original image and enhance the crucial details for further processing. The Convolutional Neural Network (CNN) is used for feature extraction. But, it yields lower performance in terms of feature extraction due to the shortage of hyperparameter tuning. Hence, the Multi-strategy Gravitational Search Algorithm (M-GSA) is utilized to extract the facial expression features from the eyebrow movement, nose, chin, and lip corner of the facial images. The facial expressions are classified via the Support Vector Machine (SVM) classifier. In this work, the top five facial expressions such as surprise, sad, happy, fear, and angry with three facial expression datasets such as FER-2013 dataset, CK+ dataset, and JAFFE dataset. Ultimately, the proposed method demonstrates better classification accuracy and recognition rates than different kinds of state-of-art methods.
Towards Blockchain-based Stainless Steel Tracking

Supply chain is an entire network of producing and delivering a specific product to a final consumer. Stainless steel is a specific product being delivered on a supply chain. It is not easy to manage and monitor the entire supply chain because a supply chain has high complexity, including various organizations and activities. Due to this difficulty, several issues occur in the process of supplying stainless steel, such as forgery and alteration. Blockchain is a decentralized and distributed ledger technology that specializes in transparency and immutability. Many companies try to introduce this blockchain technology into supply chain management to conveniently monitor their supply chain. Accordingly, the blockchain technology can make steel companies be able to investigate and protect high-quality products from counterfeited low quality products. This paper proposes a design of a blockchain-based stainless steel tracking system to thoroughly track the entire process involved in supplies from stainless steel mills to the final customers. This proposed design is based on the hyperledger fabric which is one of the most popular private blockchain platforms.
Discovering of Game AIs’ Characters Using a Neural Network based AI Imitator for AI Clustering

In game AI research, most work aims at building a more powerful AI but few are reported on explaining AI's behavior or revealing the characters of AI. Previous efforts in training a human-like AI via style learning implies that AI may behave with human characteristics. However, the early work treated human playstyle as a whole instead of identifying the difference among various AIs. In this paper, we focus on finding out manlike characters of individual AIs, and clustering AIs according to their characters. We propose a Neural Network based game AI imitator to imitate AIs' behavior and find that some AIs are easier to imitate than others. Based on this observation we define the term imitability to describe the difficulty of imitation and cluster the AIs into two categories according to their imitability. Through statically analyzing, we find that AIs with lower imitability are generally farseeing with a global perspective while the other group are nearsighted and narrow-minded. The AIs hard to imitate also perform better when fighting with others. Upon the above semantic analysis of the clustering results, we conclude that the imitability can be used to identify AIs' character.